Today, 70% of the workforce is not actively looking for a new job. Due to this, posting an ad and hoping for the best doesn’t work anymore.Does it mean that hiring is fully broken? This episode of the Innovantage podcast offers a solution. Its host and Sigli’s CBDO, Max Golikov, sat down with Juris Zalāns, co-founder of Talenme, to speak about the ongoing changes in the recruitment and HR space.In this conversation, Juris explained how digital tools are reshaping this sphere and why human trust is still the most powerful algorithm of all.Juris’s entrepreneurial journey began in the corporate world. At the beginning of his path, he held senior roles in procurement, logistics, and resource planning. He had a stable career, but he wanted to create solutions beyond the limits of traditional corporate responsibilities. He started contributing to initiatives outside his formal role and was looking for ways to deliver additional value within the organization.This initiative eventually led to his first startup. He introduced a contract-based discount system for employees. It happened more than a decade ago. But his solution resembled models that are only now gaining traction in the Baltic region.How Talenme helps businesses find employeesNow, Juris and his team are building Talenme. It is a referral-driven hiring engine designed to transform how companies find talent. Instead of relying only on traditional recruitment agencies or headhunters, the platform enables businesses to tap into broader human networks to identify suitable candidates.The concept is quite simple. Companies publish open roles along with a referral bonus. As a result, anyone can recommend qualified candidates. In this model, individuals become active contributors to the hiring process. They help organizations discover talent through trusted connections instead of cold outreach. If someone knows a strong candidate for a role, they can recommend them and receive a reward once the hire is successful.This approach aims to democratize headhunting and turn recruitment into a marketplace.Additionally, the model allows recruitment agencies and professionals to reuse their existing candidate pipelines more efficiently. Candidates who may not fit one role can still be matched with other opportunities within the ecosystem. This makes the hiring process more dynamic and cost-effective.What makes Talenme stand out?A key differentiator of Talenme is the crowd effect. The offered hiring approach is built around the reality that most talent is not actively searching for jobs. Today, only a small portion of the market actively applies for open roles. As a result, companies often compete for the same limited pool of candidates on traditional platforms.Talenme addresses this gap by focusing on access to passive talent through human networks. The model leverages personal connections as the primary channel to reach qualified specialists who may otherwise remain inaccessible. With its incentives for referrals, Talenme improves reach and significantly reduces sourcing limitations.Behind the scenes: Lean team, constant iterationTalenme is a compact, highly focused team. It consists of the co-founders and a CTO. Product development, sales, and marketing teams are freelancers who work part-time. This lean setup allows flexibility. But it demands significant multitasking and continuous prioritization.Operating with limited resources means balancing multiple responsibilities (client communication, bug fixing, financial management, and strategic planning) simultaneously. Due to this, startup execution often feels like constant grinding, with late nights and ongoing problem-solving becoming part of the daily routine.Another key challenge is aligning the product with real market needs. Initial ideas about features and client expectations often evolve when the product meets real users. It is crucial to listen to customers instead of relying on assumptions.Rethinking work-life balance in startupsJuris believes that the idea of traditional work-life balance in the startup world is largely a myth. Instead of balance, founders operate in a constant state of shifting priorities, where the most urgent tasks demand immediate attention. To build a startup, you need to keep a sustained focus. Apart from this, you should be ready to accept trade-offs rather than expect a perfectly structured routine.Startup life inevitably influences personal relationships, social time, and overall lifestyle. High responsibility and ongoing decision-making often blur the boundaries between professional and personal spheres.Key achievements in the first yearDuring its first year, Talenme reached several milestones that demonstrate early market validation and steady progress. One notable achievement was generating over $20,000 in revenue within the same year the product was pre-launched. It is a very significant step for an early-stage startup, especially given how challenging it is to secure initial traction and paying customers.The team also succeeded in attracting strong market interest. Major retail chains and well-known IT companies began testing the platform through pilot programs.Early feedback has been particularly positive from organizations that previously relied on internal referral programs and are now exploring how to scale them externally.In addition to this, maintaining team cohesion has been an important internal achievement. Despite the high workload and limited time, the team has preserved a collaborative and supportive culture.Smart communication in small teamsIn a small startup team, effective communication is less about sharing everything and more about sharing what truly matters. It makes no sense to overwhelm team members with constant updates. The focus should be on providing the information they need to perform their roles efficiently. Overcommunication can create noise and distract from execution.For example, high-level updates about fundraising or long-term strategy may be shared as context. At the same time, detailed discussions should center on areas where the team can actively contribute (for example, product development or feature decisions).However, transparency remains essential from the very beginning. Setting clear expectations helps align the team and attract people who genuinely believe in the vision. In early-stage startups, motivation is often driven by the opportunity to build something meaningful. Meanwhile, financial incentives are less important.Talent acquisition trends in 2026One of the defining trends in talent acquisition is the growing dominance of passive candidates. Unemployment rates are historically low across Europe. The majority of qualified professionals are already employed. Organizations need to rethink their recruitment strategies. They can’t rely only on inbound applications. Businesses must focus on engagement and employer branding to attract and retain talent. Employee-centric approaches (increasing workplace satisfaction, maintaining strong internal culture, etc.) are becoming key competitive advantages.Another trend is the growing reliance on external hiring channels. Many companies are outsourcing HR processes to recruitment agencies or headhunters. At the same time, gig work and flexible talent models continue to expand. This enables businesses to scale faster without fully increasing headcount.Financial pressure also impacts hiring decisions. Rising salaries and slower wage growth stabilization mean startups and smaller companies must be more resourceful when they are competing with large corporations for skilled professionals.Role of AI in hiring: Support tool, not a decision makerThe popularity of AI in talent acquisition is growing. But its role remains complex and often misunderstood. Automated screening tools and AI-powered applicant tracking systems promise faster candidate evaluation. However, real-world cases show that overreliance on automation can lead to flawed outcomes. Quite often, qualified candidates are automatically rejected due to rigid filtering criteria.At the same time, both candidates and recruiters are now using AI in parallel. Applicants generate AI-enhanced resumes, while HR teams rely on AI-assisted scoring and shortlisting. This creates a loop where automation influences both sides of the hiring process and raises concerns about transparency and decision accuracy.Juris explained that AI works best as a support feature rather than a replacement for human judgment. It can help structure data, speed up candidate scoring, and improve efficiency. However, blindly following AI recommendations can reduce hiring quality, especially in nuanced roles where context and interpersonal fit matter.Regulatory uncertainty, including emerging AI legislation, is also making corporations more cautious about full-scale automation in HR processes. As a result, many organizations are adopting a hybrid approach. They try to combine AI-driven insights with human validation.Referral-driven hiring models highlight the continued importance of human interaction. Personal context and reputation signals often provide stronger candidate validation than algorithmic filtering alone.Future of AI in talent matchmakingWhile organizations increasingly rely on AI for screening and data analysis, trust in automated decisions still has clear limits. Recruiters and hiring managers tend to treat AI outputs as guidance (not absolute truth), especially in high-stakes hiring decisions.According to Juris, in the future, AI is likely to evolve into intelligent matchmaking. Instead of only filtering resumes, it may help identify strong candidate-role connections by analyzing skills and network relevance. For example, platforms could allow users to connect their professional networks and receive notifications when suitable roles match the profiles of people they know. This would transform recruitment into a more proactive process.However, even with advanced AI capabilities, the human element will remain essential. A recommendation from a trusted contact carries social proof and additional insight into candidate fit that automated systems often miss.Referrals vs. cold applications: Who gets reviewed first?When it comes to reviewing CVs, referrals often get priority over cold applications. This approach is not about favoritism, but efficiency. Referrals signal trust and reduce uncertainty. This allows hiring managers to focus their energy on the most promising candidates.However, this system can pose challenges for those who are just starting their careers. Early-career candidates should concentrate on learning and demonstrating potential. It’s also vital to recognize that building a reputation and network takes time.When it comes to recruitment in startups, referrals are highly effective for hard-to-fill mid- and senior-level roles, particularly in IT, fintech, and sales. But giants also rely on recommendations as well.Data from 2024 shows that companies like Accenture hire roughly one in three employees through internal referrals.However, for junior or high-volume positions like internships, referrals play a different role. They can accelerate hiring when speed matters. But they are less critical since candidates are more accessible through traditional channels.Juris emphasized that resumes are rarely the deciding factor.In his experience, hiring decisions are guided more by attitude and self-motivation than by formal degrees or prior titles. The ideal candidate is eager to learn and capable of taking initiative without constant supervision. For startups, assembling a team of motivated learners can have a huge impact on future growth. Rise of project-based careersAs studies show, younger generations move through more jobs over their careers. According to a recent survey, Gen Z workers are expected to change jobs 20-30 times, compared to 10-12 times for millennials. It is still not fully a gig economy since many professional roles require onboarding and company-specific knowledge. However, this trend points toward a project economy. It means that employees work on discrete assignments within organizations before moving on to the next project. Platforms like Fiverr demonstrate the growing popularity of short-term, skill-based work. In this environment, hiring solutions that accelerate the path from job posting to onboarding are crucial. The most effective recruitment approaches will be those that quickly connect companies with the right talent.Evaluation of career experienceHiring decisions often come down to the trade-off between breadth and depth of experience. For example, imagine that you consider two engineers. One of them has a long-term journey at a single company. This specialist has gained deep expertise in a specific product or enterprise. The other has multiple short-term project experiences across several companies. Thanks to this, this engineer has managed to accumulate diverse skills and exposure to different industries and workflows.The optimal choice depends on the organization’s current needs. If the goal is rapidly scaling knowledge across new domains, a candidate with multiple project experiences may be a good choice. This specialist can bring fresh insights and accelerate learning within the team. On the other hand, if the priority is leveraging existing expertise for execution and specialization, a candidate with long-term experience may deliver more consistent results.Why salary isn’t the top priority for Gen ZFor Gen Z, career choices are increasingly driven by lifestyle alignment and work-life balance rather than purely financial incentives. Unlike previous generations, many young professionals in Europe face less financial pressure. Very often, they are able to rely on family support while pursuing early career opportunities. As a result, their priorities shift toward roles that offer meaningful work and allow them to maintain a healthy work-life balance.This trend challenges companies to reconsider traditional compensation-focused recruitment strategies. High salaries alone are no longer sufficient to attract or retain talent. Employers must also foster flexible work environments that prioritize employee well-being and personal growth.Future of HR tech: AI and human interactionSpeaking about the future of HR technology, Juris highlighted the growing role of the combination of AI-driven analytics and human-centered interactions. AI is expected to become an essential tool in HR processes, from talent sourcing to workflow optimization. Its power in data analysis and pattern recognition can help HR teams make faster, more informed decisions.However, AI can’t replace the human elements critical to recruitment and workforce management. Skills like reading interpersonal cues, detecting honesty or motivation, and building relationships remain inherently human. Missteps with fully automated interactions (such as AI-generated calls or outreach) can seriously frustrate candidates.Regional regulations also shape the adoption of HR tech. Centralized systems like blockchain-based CVs could simplify hiring across borders. However, varying legal frameworks in different countries require human judgment to ensure compliance.Why technology in HR is long overdueHR has historically lagged behind other business functions in adopting technology. Marketing, logistics, and production have been fully digitalized. But HR still relies heavily on manual processes for recruitment and talent development.In such conditions, despite being the most human function, HR is in the most need of tech support. Digital solutions can cover data analytics, candidate matchmaking, operational automation, and many other tasks. This can allow HR professionals to focus on human-centered work.The right balance between digitalization and human participation remains key to increased efficiency across every function. And HR is not an exception here.If you want to learn more about the role of modern technologies in business and our everyday life, stay tuned! The next episodes of the Innovantage podcast will offer new insights and perspectives on these aspects from industry experts and tech leaders.

In the current landscape, the pressure on leadership to "do something with AI" is immense. Boardrooms and shareholders are increasingly viewing AI as a universal solvent for operational friction. However, at Sigli, we have observed a recurring pattern: when AI is treated as a shortcut to bypass organizational inefficiencies, it fails. Worse, it scales those inefficiencies at a digital pace.For a software development company focused on data and AI, our most critical advice to partners is often to pause. AI is a powerful multiplier, but it is mathematically indifferent to what it multiplies. If you apply it to a fractured foundation, you simply achieve automated chaos. To ensure a return on investment, CEOs and COOs must prioritize four foundational pillars before flipping the switch on automation.The Integrity of the Mirror: Data as a Strategic AssetThe primary risk for any executive-led AI initiative is "Model Hallucination," but the root cause is rarely the algorithm, it is the data. AI does not possess human intuition; it is a mirror that reflects the environment described by your data. If your departments operate in silos, where Marketing’s "Customer Acquisition Cost" differs from Finance’s "Marketing Expense," the AI cannot reconcile the truth. It will simply provide a confident, sophisticated answer based on a flawed premise.Strategic data integrity requires moving beyond simple storage and into the realm of Active Data Governance. This is not a clerical task; it is a leadership mandate to establish a "Universal Source of Truth." Before investing in predictive models, the organization must ensure data is cleaned, centralized, and standardized. At Sigli, we often find that the deployment of a high-performance data warehouse, making the actual state of the business visible for the first time, yields a more immediate and measurable ROI than the most advanced neural network could provide on a shaky foundation.Mapping the Logic: Why You Cannot Automate Tribal KnowledgeAI thrives on repeatable, deterministic logic. Yet, many of the world’s most successful companies still run on "tribal knowledge", critical operational logic that exists only in the heads of veteran employees. If a process requires a human to "just know" when to bypass a rule or how to fix an error, that process is not ready for an AI agent.Automation requires a level of Process Maturity where every workflow can be mapped as a logical flowchart. If your COOs cannot document a process to the point where a junior employee could execute it with 100% accuracy, an AI will fail to replicate it. Leadership must first audit these "Human Glue" moments where manual intervention keeps the gears turning. By streamlining and standardizing these workflows today, you aren't just improving manual efficiency; you are creating the behavioral blueprint that will eventually allow AI to scale your operations.Strategic Solvability: Guarding Against Technological FOMOThe "Fear of Missing Out" is perhaps the most expensive driver of modern technical debt. We frequently see organizations rush into Generative AI pilots because of industry noise, rather than a diagnosed bottleneck. This leads to "Pilot Purgatory," where projects consume resources but never reach production because they were never tied to a core business challenge.A value-driven roadmap requires the discipline to ask if a problem is AI-shaped. AI is uniquely gifted at three things: massive scale, extreme speed, and high-dimensional pattern recognition. If a business challenge, such as a high support ticket volume or a complex supply chain—does not fall into those categories, it may be better solved with a simple script, a better UI, or a management change. Sigli’s approach is to identify the "Hard Problems" first. By ensuring every technical dollar is tied to a Top-3 KPI, you ensure that AI is a strategic asset rather than an expensive science experiment.Infrastructure Modernization: The Engine Room of InnovationThe final hurdle for the CEO is the "Legacy Tax." Modern AI requires high-speed data portability and cloud-native environments. Trying to integrate a cutting-edge LLM into a twenty-year-old on-premise server is an exercise in futility. The integration costs alone often exceed the value the AI provides.Legacy systems typically lack the API-first architecture necessary for modern software to communicate. This forces your engineering teams to build "brittle bridges", custom code that breaks every time the model or the system updates. True digital transformation is about building an Extensible Architecture. By modernizing the core tech stack and moving toward a modular, cloud-based environment, you grant your organization the agility to swap in new AI models as the technology evolves. You aren't just buying software; you are buying the ability to pivot.The most successful AI implementations we have led didn't start with a model; they started with a cleanup. By fixing the "boring" fundamentals, data quality, process clarity, and system architecture, you aren't delaying your AI future. You are ensuring that when you finally deploy it, the results are predictable, scalable, and profitable.Don't build your digital future on sand. Build a foundation that makes AI's success a mathematical certainty.

In this episode of the Innovantage podcast, its host and Sigli’s CBDO, Max Golikov, speaks with Thijs Verheul, co-founder of United Wardrobe, the fashion marketplace acquired by Vinted. In their conversation, Thijs, who is now an angel investor and author of Thrift to Triumph, shares his insights and lessons learned on execution. How to build the right team? How to manage burnout? How to make tough decisions? Find answers in our article!How everything startedThijs’s entrepreneurial journey did not start with a clear plan. At 18, he briefly studied law before realizing it was not for him. After quitting, he spent six months working as a ski instructor in Austria. At 19, he decided to return to university and enrolled at Wageningen University in the Netherlands, known for its focus on life sciences and environmental innovation. It was there, in 2013, that he met his future co-founder, Sjuul Berden.Sjuul proposed the idea of a dedicated marketplace for second-hand clothing, inspired by his sisters’ overflowing wardrobes. At the time, Thijs was skeptical. There were already a lot of marketplaces and Facebook groups where people could buy and sell different items. The idea of a clothing-only marketplace seemed unnecessary. Meanwhile, Vinted was still a small, little-known startup.After months of discussion, Sjuul convinced him. In January 2014, they launched United Wardrobe. Over time, the company raised €3.5 million in venture capital, grew to 60 full-time employees, reached 4.5 million users, and facilitated around €40 million in annual GMV.In 2020, United Wardrobe was acquired by its largest competitor, Vinted. Thijs became a Vinted shareholder and chose to step away. Today, Thijs is a new father and an active angel investor. Turning ideas into real actionAs Thijs highlighted, execution starts with the right team. In his experience, the most important factor is assembling people with complementary skills.He described the ideal founding team as a combination of a hippie, a hacker, and a hustler. The hippie brings the vision and long-term purpose. The hacker builds the product. And the hustler drives the business forward through sales and partnerships. At United Wardrobe, Thijs played the role of the hustler, while his co-founder, Sjuul, was the visionary. Together with an engineer, they formed the core team that could get the company off the ground.Apart from this, the willingness to launch early also plays an important role. The first version of United Wardrobe was far from perfect. But it worked where it mattered. Users could register, and payments functioned. That was enough. By putting the product live quickly, the team could gather real user feedback, analyze data, and improve the platform step by step.Importance of people: Why solo founders rarely winAs an angel investor, Thijs is cautious when it comes to solo founders. Even when a single entrepreneur shows strong early traction (such as €50,000 in monthly recurring revenue), there is still a significant risk. Building a company alone often leads to burnout. Without another founder, there is no equal partner to share responsibility or challenge decisions.Solo founders can hire teams later. But this is not the same as having multiple founders with true ownership and entrepreneurial accountability. Shared ownership creates resilience and faster problem-solving during difficult phases.Nevertheless, exceptions exist. Solo builders will continue to create successful companies, especially in the age of AI. However, from an investor’s perspective, the odds are simply better with a founding team.This belief also shapes Thijs’s own work today. On his current projects, progress depends heavily on the people around him. Teams create motivation and a shared drive to perform. Moreover, working together is more enjoyable and sustainable.First real win: How Facebook groups unlocked growthBefore launching United Wardrobe, the founders focused on building an audience. At that time, Facebook was the dominant social platform. They created a Facebook page and invited their entire network to follow along.They shared early designs, product ideas, and feature concepts. This helped them receive hundreds of comments from potential users. This feedback directly shaped the platform. When United Wardrobe launched, it attracted 500 registered users on day one, without any marketing spend. However, growth stalled shortly after. Two to three weeks later, activity on the platform dropped sharply. The breakthrough came when the team identified two large Facebook groups dedicated to buying and selling second-hand clothing. Through negotiation, they acquired one group and gained permission to promote United Wardrobe in the other.They automatically shared listings from United Wardrobe into these groups. The impact was immediate. The group members were already actively trading clothes, but struggled with scams and unreliable transactions. United Wardrobe offered a safer alternative. It held payments until items were delivered.After that, the platform reached its first real liquidity with 10,000 products and 100,000 users. As a result, Facebook communities became the foundation for scaling the business.Biggest early mistake: Scaling too fastOne of the lowest points in building United Wardrobe came after the company’s third funding round. The team had raised €250,000, followed by €1 million, and then €1.5 million. With fresh capital in hand, they began hiring aggressively.At its peak, the company employed around 60 full-time staff. Nearly 30 people were dedicated to entering the French market. At the time, France seemed promising. However, Vinted already dominated the market.Within six months, the reality became clear. Competing in France would require an entirely different scale. Investors advised the team to reconsider and focus elsewhere.To protect the company’s runway and avoid bankruptcy, United Wardrobe made the painful decision to let go of 30 employees in a single day. For several months, the founders had tried to cut costs and regain momentum. But eventually they withdrew from France and refocused on their core markets in the Netherlands and Belgium.Looking back, Thijs describes the mistake as a combination of youth and inexperience. The team believed hiring more people would automatically drive growth. They underestimated the operational overhead of managing a large team and the emotional toll of scaling too quickly. Major milestones and brutal pressure from VintedThe eventual acquisition by Vinted was the defining milestone of United Wardrobe’s journey. From the first bid to the final deal in October 2020, the process took nearly two years.By that point, Vinted had already become a dominant force across Europe. Both United Wardrobe and Vinted started from a remarkably similar idea. But Vinted scaled faster and raised significantly more capital. Vinted’s aggressive expansion strategy reshaped entire markets. Dozens of local second-hand platforms across Europe disappeared once Vinted entered their regions.The pressure became especially intense in 2018, when Vinted entered the Dutch market. With a TV advertising budget of around €1.5 million, Vinted dwarfed United Wardrobe’s €50,000 marketing budget. United Wardrobe relied on social media, influencers, and strong SEO positioning. The company attracted a younger audience and maintained its position as the top Google result for clothing resale in the Netherlands. But it became clear that only one player would ultimately dominate the market.The deal with Vinted was agreed upon in March 2020, only to be delayed for six months when COVID-19 hit Europe. Ultimately, the acquisition went through.Initially, Thijs and his co-founders preferred a clean cash exit rather than shares in Vinted. However, Vinted’s CEO convinced them to retain equity. Now, Thijs believes that it is one of the best financial choices he ever made. Since then, Vinted’s valuation has grown significantly.What happened after the acquisitionAfter the acquisition, Thijs stayed on for approximately four months to support the transition. During this period, United Wardrobe’s 4.5 million users were migrated to the Vinted platform. Now, he remains a minor shareholder in Vinted but holds no board role or operational influence. Since then, he has returned to the startup world as an angel investor, working with early-stage companies at a more sustainable pace. After the exit of United Wardrobe, Thijs felt a strong need to document the journey. The years of building the company had been intense and emotionally exhausting, and much of it felt like a blur. He began writing as a personal exercise to create a record he could reflect on.The idea of turning those notes into a book came after a conversation with a friend. She encouraged him to share the story more widely.Publicly, United Wardrobe was often portrayed as a straightforward success story. Behind the scenes, however, the company came close to bankruptcy multiple times, and Thijs nearly quit on several occasions. He felt it was important to challenge the simplified narrative of overnight success and highlight the reality.The book called Thrift to Triumph also aims to demystify the acquisition process. Media headlines often make exits seem quick and effortless, when in reality they can take years of negotiations and uncertainty. By sharing the full story, Thijs wanted to give young entrepreneurs a realistic view of what the journey entails. Today, this digital book is available for free.One piece of advice for readersIf there is one core lesson Thijs hopes readers take from his book, it is the importance of people. The story of United Wardrobe makes clear that no matter how powerful AI or technology becomes, building a company still requires real humans working together on the ground.He encourages founders to find strong team members early and to work together in person whenever possible. Shared physical spaces create trust and emotional understanding that tools like Slack simply cannot replace. Building a startup is demanding. But it can also be an enjoyable journey when experienced as a team.The book also provides a closer look at the personal cost of entrepreneurship. Thijs described constant stress, repeated near-burnouts, and the pressure of feeling that failure was not an option. Thijs hopes that readers can learn from the decisions he would not repeat. Life as an angel investorAfter selling United Wardrobe, Thijs noticed a huge interest from entrepreneurs. Pitch decks poured in via email, LinkedIn, and Instagram. Thanks to this, he could cherry-pick startups he believed had potential. For Thijs, access to a wide deal flow is essential. Out of dozens of opportunities, only a few typically turn into worthwhile investments.Angel investing allows him to stay close to the startup world. He enjoys mentoring founders, offering advice on sales, marketing, and strategy, and witnessing teams pursue products that aim to make a real impact. At the same time, it is a challenging and sometimes slow process. Of his eight investments so far, three or four are performing well. The others face hurdles. The work involves careful analysis of valuations and equity structures. All this requires patience and attention to detail.Many startups take years to reach an exit, and returns often require long-term waiting. But the ability to support and guide young entrepreneurs keeps Thijs engaged.How Thijs chooses teams to invest inAccording to Thijs, the most important factor in early-stage investing is the entrepreneurs themselves. He looks for teams with complementary skills. But beyond that, he favors founders who have previous startup experience (especially those who have built and sold a company before). Such entrepreneurs combine lessons learned from past mistakes with the drive and financial freedom to commit fully to the long term.Personal connections and reputation also matter. Thijs often invests in teams that come through his network. He assesses growth metrics and looks for early traction as a signal that the startup is on the right path. His focus is on seed-stage investments (usually small tickets under €100K), where he can provide strategic guidance and help founders navigate early challenges.However, experience alone is not a strict requirement. Thijs also invests in first-time founders if the team is strong, the product is compelling, and the potential for impact is clear. One example is Eddy Grid, a company that builds software to optimize battery usage for factories. Although the founders came from traditional industries, they brought complementary skills and deep industry knowledge.Nevertheless, Thijs emphasized that there is no single formula for success in startup investing.Decisions are made by balancing data and intuition. At the same time, mistakes are inevitable. What matters is that one strong outcome can more than compensate for several failed investments.Breeze and marketplace mechanics in datingThijs also invested in Breeze, a dating app founded by seven students with highly complementary skills (developers, designers, and marketers). Initially, the team generated only a few thousand euros per month. But it managed to catch the attention of prominent Dutch angel investors. Thijs joined to help with marketing and quickly became impressed by their energy and vision.He noticed parallels between dating apps and marketplaces like United Wardrobe. Just as a clothing marketplace needs a large inventory to match buyers with the right products, a dating app requires a critical mass of users to facilitate meaningful connections.Breeze introduced a unique approach. Matches could not chat directly but instead booked dates via an integrated date-picker system. The approach proved highly successful. Thijs noted that multiple marriages and even children have resulted from connections made on the app.Breeze’s business model contrasts with platforms like Tinder or Bumble, which benefit from keeping users single and engaged for as long as possible. Breeze, instead, succeeds when users find partners quickly and leave the app. This makes its incentives aligned with user success rather than retention.The app’s growth has been rapid, particularly in the US and the UK. The app partners with local restaurants and cafes, integrating their reservation systems to facilitate dates. New York, London, Birmingham, and Newcastle are its key focus areas.Thijs also highlighted an important marketing insight: women are far more expensive to acquire than men. For Instagram ads, female installs cost roughly €25 each, while male installs cost only about €0.10. Understanding these dynamics is crucial for balancing user acquisition and maintaining a healthy dating marketplace.Possible failures in angel investmentsOf course, startup investing is high-risk, but high-reward. In his experience as an angel investor, Thijs has seen a mix of outcomes. A few early investments have yielded small profits. But the true success of most seed-stage startups may only become clear over 10 years or more.Some investments have underperformed. In one case, Thijs and his co-investors put significant capital into a startup, only for a later venture capital round to bring in a more aggressive investor. While the outcome was still positive, the communication and process could have been handled better. This illustrates one of the challenges of early-stage investing. In another instance, a startup continues to operate after two years of hard work without reaching its intended goals. These experiences show that even with dedication, products may fail to gain traction, and founders must decide when to pivot.As Thijs explained, angel investing should be done only with money one can afford to lose. Compared with more traditional investments, like stocks, ETFs, or real estate, startups are riskier. They have the potential to go either times 50 or to zero. Success comes from portfolio thinking. Investing in many startups increases the chance that one will achieve exponential returns, effectively compensating for the others.Advice for young startup foundersThijs strongly believes that founders should surround themselves with people whose skills balance their own. Once the team is in place, the key is to start quickly and iterate constantly. Launch early, test the product with friends and family, analyze data every day, and improve continuously. Marketing should be bold. Try to engage users directly and use minimal resources to gain traction. That’s what Thijs advised.Apart from this, Thijs highlighted the importance of self-care and balance for startup founders. He recommended working in focused bursts instead of long, exhausting hours. You should take breaks to walk or relax.Moreover, working with people who energize and inspire you is critical to sustaining performance and maintaining motivation over the long term.Such tips may seem quite simple. But they help build a strong foundation for further resilience and growth.Looking to learn more from startup stories and business insights in the digital age? Stay tuned for the next episodes of the Innovantage podcast.

We at Sigli are proud to announce that we have been ranked among the Top 100 Python Developers on the Techreviewer list for 2026. This recognition reflects our long-term focus on building robust, data-driven software solutions and our commitment to using Python as a core technology for creating scalable, reliable, and insight-driven digital products. Being included in this global ranking highlights both the technical depth of our teams and the real-world impact our solutions deliver for clients operating in complex data environments.A Strong Foundation in Python and Data-Driven EngineeringFrom the beginning, Sigli has been built around the idea that high-quality software starts with a deep understanding of data and the systems that generate it. Python plays a central role in this approach. Its flexibility, clarity, and extensive ecosystem allow us to design solutions that are both technically sound and adaptable to evolving business needs.We use Python to power backend systems, data processing pipelines, analytics platforms, and custom applications where performance and accuracy are critical. Our teams apply Python in environments that demand reliability at scale, ensuring that systems are maintainable, secure, and capable of growing alongside the organizations that rely on them.Delivering Data-Driven Solutions with Measurable ValueOur work in data-driven solutions is a defining part of who we are. We help organizations transform raw data into actionable insights by designing and developing systems that support data collection, processing, analysis, and visualization. Python enables us to connect these layers seamlessly, creating platforms where data flows efficiently and insights are accessible to decision-makers.We collaborate closely with clients to understand how data is generated, where bottlenecks exist, and how insights can be embedded directly into workflows. By aligning Python development with business objectives, we ensure that technology investments result in tangible outcomes such as improved efficiency, better forecasting, and more informed strategic decisions.How We Work: Structured, Collaborative, and Insight-FocusedSigli’s approach to software development is rooted in collaboration and clarity. Every engagement begins with a deep discovery phase, where we work alongside stakeholders to define goals, assess data readiness, and identify technical and organizational constraints. This foundation allows us to design Python-based solutions that are aligned with both short-term needs and long-term strategy.Throughout development, we emphasize transparency, iterative delivery, and continuous validation. Our teams follow best practices in code quality, testing, and documentation to ensure that solutions are not only effective at launch, but sustainable over time. This structured yet flexible way of working enables us to adapt to change without compromising stability or performance.Python Expertise Across Complex Use CasesOur Python capabilities support a wide range of use cases, particularly in environments where data volume, complexity, and accuracy are critical. These include backend services, data analytics platforms, integration layers, and automation systems that streamline operations and enhance visibility.Key areas of our Python development expertise include:Backend systems and APIs designed for scalability and performance.Data processing and analytics pipelines supporting business intelligence.Integration of multiple data sources into unified platforms.Automation tools that reduce manual effort and improve reliability.Maintainable architectures that support long-term evolution.This breadth of experience allows us to tailor Python solutions to each client’s specific context while maintaining high engineering standards.Why This Recognition Matters to SigliBeing ranked among the Top 100 Python Developers in 2026 is a meaningful achievement for Sigli. It reflects the consistency of our work, the expertise of our teams, and the trust our clients place in us to deliver complex, data-centric systems. This recognition also highlights the importance of our focus on combining Python development with a strong understanding of data strategy and system design.For our clients, this ranking offers additional confidence that they are partnering with a team recognized for technical excellence and thoughtful execution. For our internal teams, it serves as motivation to continue refining our skills, embracing innovation, and maintaining the high standards that define our work.About Techreviewer.coTechreviewer.co is an independent research and analytics platform that evaluates technology service providers worldwide. Its rankings are based on criteria such as technical expertise, project experience, service quality, client feedback, and market presence. The Top 100 Python Developers list helps organizations identify trusted partners for Python-based software development and data-centric digital initiatives.

Most digital transformation projects falter at the final hurdle: the human element. Traditional end-user training often fails because it treats software as a destination rather than a tool. To drive genuine adoption, training must shift from "system tours" to workday enablement plans.In the competitive landscape of User Training Services Benelux, success isn't measured by the number of sessions delivered, but by the speed of operational autonomy on Day 1.Role-Based Architecture: Engineering for Operational RealityGeneric software demonstrations are high-effort and low-retention. Effective User Training Services in the Benelux region require a move away from the "one-size-fits-all" demo toward a role-based design that mirrors the functional reality of an organization.Mapping Functional PersonasEffective training begins by mapping operational roles rather than hierarchical titles. In the Benelux market, characterized by multilingual teams and cross-border collaboration, a "Sales Representative" in Amsterdam may have different data compliance requirements or localized workflows than one in Brussels.Key personas to map include:Frontline Operators: (e.g., Sales Reps, Customer Support) focusing on high-frequency data entry.Decision Makers: (e.g., Finance Approvers, Team Leads) focusing on validation and reporting.System Guardians: (e.g., Admins, Power Users) focusing on configuration and troubleshooting.Task-to-Objective ConversionFor every persona, identify the 5–15 mission-critical tasks performed weekly. Instead of "navigating the interface," the focus shifts to executable workflows:The Task: "Submit an expense report."The Objective: "Complete a multi-currency submission in under 3 minutes with zero validation errors."The Result: A drastic reduction in support desk tickets and "rework" cycles.The Localization Factor: Navigating the Benelux LandscapeProviding User Training Services Benelux wide involves more than just translating slides. It requires an understanding of regional nuances in work culture and language.Multilingual Synchronization: Training must be accessible in Dutch, French, and English, ensuring that technical terminology remains consistent across the entire workforce.Regional Compliance: Incorporating local tax laws, GDPR nuances, or specific regional business logic directly into the training flow prevents users from falling back on "shadow" manual processes.The Digital Asset Ecosystem: Reinforcement Beyond the ClassroomA live session is a catalyst, but long-term adoption requires a reusable ecosystem of assets. Professional User Training Services Benelux providers deliver a "Learning Library" designed for continuous onboarding.High-Impact Job Aids (The "Monday Morning" Docs)Users rarely re-watch a 60-minute recording to solve a 30-second problem. The most effective training packages include:Step-by-Step Job Aids: 1–2 page visual guides focusing on "the steps, the fields, and the gotchas."Quick Reference Guides (QRGs): "Top 10 Actions" or "Troubleshooting 101" sheets that act as a safety net during the high-stress go-live week.The Micro-Learning Video StrategyIn a modern digital workplace, videos should be task-based and "snackable" (2–5 minutes).Focus on Friction: Create videos specifically for the tasks that historically generate the most support tickets.Localization Efficiency: By using task-based videos, Benelux organizations can easily swap audio tracks or subtitles to serve different regions without re-shooting the entire technical demonstration.The transition from a "system tour" to an "adoption-led" strategy is what defines premium User Training Services Benelux. By focusing on role-based mapping and a robust asset ecosystem, organizations ensure that their teams aren't just "using the system", they are mastering it.

Low-code and no-code platforms promise a revolution: business app creation without coding. In the Benelux region, where companies are rapidly digitizing via the Microsoft Power Platform and Mendix, the "quick win" is more popular than ever. It feels easy until the app needs to touch real business systems.In the Benelux market, organizations operate on dense Microsoft ecosystems, legacy ERPs, and strict compliance frameworks. This is where the "speed promise" often breaks. The real complexity isn't the UI; it’s the integration, data quality, and governance.The Illusion: "From Idea to App in Two Weeks"Most projects for business app creation without coding in Benelux start with a simple prototype:HR onboarding forms or procurement request flows.Operations dashboards with manual input fields.But business apps cannot stay in a vacuum. Soon, stakeholders ask:"Can it sync with our SAP or Dynamics 365 ERP?""Can we restrict access by region (NL vs. BE) or entity?""Does it meet local GDPR audit requirements?"What Actually Slows Down No-Code Apps?1. Integration Reality GapsStandard connectors often get you 80% of the way. The final 20% handling rate limits, inconsistent API behaviors, or unsupported legacy actions is where projects stall.2. The Data Ownership CrisisNo-code tools expose data issues that have been hidden for years. If your master data is full of duplicates or missing identifiers, your "no-code" app becomes a mirror for those failures.3. The Compliance Bar in BeneluxIn the Benelux, "nice-to-haves" like SSO (Entra ID), role-based access, and detailed audit trails are mandatory. Skipping these during the build phase ensures a rejection from IT during the security review.The Playbook: Making "No-Code" Production-GradeTo succeed with business app creation without coding, you need an "integration-first" mindset.Step 1: The Data Reality CheckBefore designing a single screen, map your systems. Who owns the CRM data? Is the API stable? Do you need middleware? Identifying these risks early prevents 70% of common delays.Step 2: Build a Connected "Slice"Instead of 50 screens, build one core workflow that uses real data and real permissions. Validating the integration early proves the concept is actually viable in a production environment.Step 3: Standardize the Integration LayerDon't wire your apps directly to your ERP. Use an API gateway or shared services. This ensures that when your upstream systems change, you fix one layer not 25 different app workflows.Step 4: Lightweight GovernanceImplement "Minimum Viable Governance" from Day 1:Environment Strategy: Separate Dev, Test, and Prod.App Registry: Track who owns the app and what data it touches.Naming Conventions: Prevent "App_Final_v2" chaos.Choosing a Benelux Partner for No-Code DeliveryIf you are hiring a consultant for business app creation without coding in Benelux, look for these signals:Green FlagsRed FlagsThey ask about your ERP/API limits first.They focus only on how "pretty" the UI is.They have a plan for Entra ID/SSO.They suggest "workarounds" for security. They emphasize data ownership and "Source of Truth."They claim everything is "out of the box."They provide a Day 2 support and maintenance model.They vanish as soon as the app is "Live."No-code platforms are powerful tools for speed. However, the real value lies in making those apps secure, governed, and integrated. If you treat your app like a product rather than a prototype, you can achieve the dream of business app creation without coding in the Benelux without the typical enterprise headaches.

People often say luck is the biggest factor in startup success. But is that really the whole story? What are the actual benchmarks of a successful venture? These questions and many more are at the heart of this Innovantage podcast episode, where its host and Sigli’s CBDO, Max Golikov, speaks with Povilas Žinys, Director of Plug and Play Tech Center in the Baltics. Povilas works closely with startups from pre-seed to Series B and helps founders prepare for the demands of venture-scale growth.Povilas’s professional journeyPovilas’s own path into startups was shaped by a gradual shift away from traditional business. He studied accounting during his bachelor’s degree. But he quickly realized it wasn’t the right fit. He pivoted toward innovation and technology management during his master’s studies and deliberately sought out the most cutting-edge industries he could find.That search led him first to the space sector. He worked at a satellite operator, analyzed market trends, and advised leadership on investment directions. The industry itself seemed exciting. Nevertheless, the corporate environment felt limiting. Later, Povilas moved into cybersecurity and joined a company as a product manager. There, he deepened his expertise through a master’s degree in cybersecurity, helped introduce agile practices, and launched new products.After the company entered an acquisition phase, the work became increasingly focused on internal politics, not on execution. At this moment, Povilas and his colleague decided to start their own company. The idea was born from a simple but widespread problem.They wanted to help businesses manage corporate spending across distributed teams. Their startup aimed to streamline purchases, approvals, payments, and documentation for finance teams. Revenue was expected to be generated through card interchange fees.During this period, Povilas relocated from Luxembourg to Lithuania and joined the Wise Guys accelerator. The company went on to complete additional accelerator programs, raise close to €1 million in funding, launch a product, acquire clients, and reach recurring revenue. However, the startup ultimately struggled due to unreliable banking infrastructure providers. Scaling issues and repeated provider changes led to client losses. The product was eventually sold.One of the startup’s investors was Plug and Play. Later, it offered Povilas the opportunity to establish and lead its Baltic operations. This role provided him with a new perspective. This time, he could take a look at the business world from the investor’s side of the table.What is a startup accelerator?A startup accelerator is designed to help founders move faster and avoid costly mistakes on the path to growth. At its core, an accelerator brings a founding team to the next stage by combining structured learning and access to capital.Accelerators vary by focus. But most of them share common elements, like hands-on mentorship, practical workshops, shared knowledge, and a strong community of peers. Many also offer co-working space. However, the two most critical components are access to a powerful network and early-stage investment. Together, these elements help startups raise their next funding round, enter new markets, or prepare for rapid scaling.Plug and Play works with a wide range of companies at very different stages. Some startups are still in deep R&D or clinical trials without a product on the market. Others already serve thousands of customers.In the Baltics, Plug and Play represents a significant milestone. It is the first Silicon Valley-based venture capital firm to establish a permanent presence in the region. The initiative was launched in collaboration with the Lithuanian government. This reflects a broader trend in Central and Eastern Europe. There, public institutions increasingly support innovation. Meanwhile, similar programs in Western Europe are more often driven by corporate partners.Lithuania and the wider Baltic region offer strong foundations for startups and also serve as an effective testbed for validating products at a smaller scale. However, true growth requires thinking beyond local markets. Plug and Play helps founders go international as early as possible. Challenges for startups in 2026Today, at Plug and Play in Lithuania, startups range from first-time founders with no capital raised to companies that have secured several million euros. The ecosystem also attracts international teams. There are startups from the US, UK, and Asia that rely on Lithuania as a base for their European operations.Lithuania has emerged as a practical hub for launching in the EU. It ensures lower operating costs, strong technical talent, an active startup community, and an approachable government. All this makes it easier for companies to get started and test new ideas. For many international startups, it offers speed and flexibility that are harder to find elsewhere in Europe.Despite these advantages, the core challenges remain largely the same. Fundraising is consistently the biggest hurdle. Among others are entering foreign markets, building the right partnerships, developing a strong product, and scaling teams with the right people. Finding talent is not just about skills. It is also about commitment. The process of building a startup requires deep trust and long-term belief from every person involved.What every successful founder should haveFrom an investor’s perspective, resilience is one of the most important founder traits. Startups rarely succeed quickly. And failure is often part of the journey. What matters is how long founders can keep going. Many successful companies struggled for years before finding product-market fit. In reality, fundraising alone can take nine months or more, despite the common myth of overnight success.Startup life has nothing in common with the stability of corporate roles. There are no guaranteed weekends, no clear work-life balance, and no short-term rewards. Founders often sacrifice income and personal time. They do all this to bet on a future that is far from guaranteed.Those who succeed are usually the ones who endure the longest. They stay focused through slow progress and repeated rejection.Povilas believes that the startup experience offers founders the ultimate combination of freedom and responsibility. It demands tough decisions but allows them to pursue something that is truly their own. The experience itself provides invaluable lessons that shape future careers and personal growth. The relationships established along the way are equally important. Connections made in a startup environment are often deeper and more meaningful than in traditional jobs.How VCs make choicesFor venture capitalists, the team is the single most important factor for startup evaluation. Povilas explained that investors look for founders with proven expertise in their industry, a strong work ethic, organizational skills, and the ability to collaborate effectively. Other factors that matter are:Product quality;Technology;Intellectual property;Scalability;Market potential;Competition. Nevertheless, they are secondary. A strong team can navigate challenges and adapt. Without it, even the best product may fail.Being an expert in every domain isn’t necessary for investors, especially in a global VC like Plug and Play. With a network of 200 venture specialists across industries and regions, Povilas can rely on colleagues for insights into specific markets or technologies. Moreover, it’s crucial to bear in mind that startup evaluation is always context-dependent. A company may be an excellent fit regionally but lack global potential.Startup failure: Is it possible to avoid it?Startup failure is common. Roughly 90% of startups fail within their first year. Many more collapse in the following two years. The likelihood of survival often depends on the stage of the company. In Lithuania, most startups are at the pre-seed stage, and access to later-stage funding (Series A and beyond) is limited locally. This encourages founders to expand abroad. The global approach is essential for building companies capable of reaching unicorn status.Despite high failure rates, there are some indicators of above-average startups:Adaptability;Networking;Persistence. Success is not solely measured in revenue. Learning from failure, gaining experience, building networks, or making a broader impact also count as achievements. Many founders who experience multiple startup failures can eventually find significant successes. They demonstrate resilience and knowledge that often outweigh early financial outcomes.The best age for founding a startupMany successful founders are not in their 20s. They tend to be older, often over 40. They bring years of industry experience to their startups. Povilas explained that this unique experience gives them a crucial advantage. After having worked within an industry, they can identify real problems firsthand and envision practical solutions.While there are exceptional younger founders, statistically, there are more experienced professionals who succeed. They recognize meaningful gaps and execute effectively. Their deep understanding of the industry allows them to move faster and make better decisions. Moreover, they can address problems with insight that comes only from real-life experience.Rethinking startup successSuccess in startups is often misunderstood. Raising a large amount of capital (€20 million, for example) is frequently seen as an automatic success. But as Povilas highlighted, it also brings enormous responsibility.Investors expect a return, and the pressure to deliver can be immense, especially for young founders. Smaller funding rounds often carry lower obligations and allow startups to grow more sustainably.The ultimate measure of success is the ability to finance growth organically. Using their own resources, founders take responsibility for their outcomes and build a business that is under their control.From an investor's perspective, startup funding is a numbers game. Predictable returns require a diversified portfolio. When you invest in just a few companies, it may feel just like gambling. Venture capital remains a high-risk, high-reward asset class, which is typically a small portion of wealthy investors’ portfolios.Launching startups todayAccording to Povilas, establishing a startup in Lithuania and the Baltics has never been easier. Abundant EU funding and support programs make it simpler to raise an initial €500K than ever before. Early-stage launch has become more accessible compared with just a few years ago, when founders often had to rely on personal budgets or limited accelerator support.However, building and scaling a successful business remains challenging. While AI and modern tools have lowered development costs and reduced the need for large engineering teams, competition is fierce.Investors now expect startups to have sustainable advantages. It could be unique technology, strategic partnerships, strong networks, or first-mover positioning.Nevertheless, as Povilas explained, competitive advantages are relative. What works in Lithuania may not hold in Europe or globally. Success depends on speed and partnerships. For instance, securing a partnership in a neighboring country and launching quickly can constitute a real advantage.The scale and intensity of competition also vary by region. In Silicon Valley, rounds and valuations are on a different level. Smaller markets, like Lithuania, are ideal for early-stage testing and prototyping. In such regions, failures are less costly. Gap between startups and corporates in LithuaniaLithuania’s startup ecosystem is growing. But there is a critical gap: corporate engagement. Unlike some countries where companies actively pilot and invest in startups, most Lithuanian corporates treat the ecosystem primarily as a marketing tool. They rely on it to showcase innovation or attract talent. However, they don’t leverage startups to drive real business impact.This limits startups’ ability to scale solutions internationally and deprives corporations of potential revenue growth or cost savings. Povilas mentioned the example of Mercedes. The company runs numerous pilots annually and integrates new technologies into its products. Such collaborations are still rare in Lithuania.How Plug and Play helps startups expand internationallyPlug and Play supports Baltic startups in expanding internationally through two main approaches. First, it sends startups from Lithuania and the Baltics to programs in other global hubs, like Munich, Silicon Valley, New Jersey, or Texas. This allows them to benchmark against founders in other markets and build more diverse networks. Mixed teams with members from different countries (for instance, Canada and Lithuania) create stronger connections that accelerate growth.Second, Plug and Play attracts international co-founders and startups to Lithuania. Companies like Red Arrow Technologies from Germany or renewable startups from the broader portfolio can establish operational hubs or secure fintech licenses in Lithuania.Even if part of a startup’s operation leaves Lithuania, the long-term impact and potential returns benefit the country. Emerging opportunities in Lithuania’s startup landscapeBoth Max and Povilas agreed that, though the role of AI was largely set aside in their discussion, it is expected to impact nearly every sector globally. Beyond AI, Lithuania presents significant opportunities in defense, life sciences, fintech, and cybersecurity.In defense, proximity to Ukraine and ongoing investments in infrastructure position the country as an attractive hub for innovation (particularly in drones and anti-drone technologies). Lithuania is perceived as close enough to conflict zones to provide relevant market insights. But it is still safe enough to serve as a testing and innovation ground.Life sciences is another key growth area. The country has strong research capabilities and ambitious government targets, such as generating 5% of GDP from life sciences by 2030. Today, this sector offers potential to transform Lithuania’s economy toward high-value industries. Early successes, such as Vugene and Biomatter, highlight the momentum and investment interest in this field.Fintech remains a stable sector with continued input and portfolio growth, while cybersecurity represents an untapped opportunity. Today, Lithuania has become an excellent launchpad not just for local and international startups. If you want to dive deeper into the nuances of building a startup and doing business in the age of AI and rapid technological progress, stay tuned for more episodes. In the coming weeks, we will bring you fresh insights from leading experts across various industries.

Most operations don’t break because people aren’t working hard. They break because work is scattered across too many tools, too many handovers, and too many “small decisions” that can’t be captured in if-then rules.You can automate the straightforward steps. But the moment a workflow becomes multi-stage, exception-heavy, or dependent on context, automation quietly hands it back to humans:Someone checks the data “just in case”Someone follows up because the system can’tSomeone reconciles two sources that don’t matchSomeone escalates, routes, rewrites, re-explainsSomeone keeps the process moving manuallyThat’s not a tooling problem. It’s a workflow problem. And it’s exactly where AI agents are starting to make a real difference.Why assistants help… but don’t reduce operational loadAI assistants (copilots, chatbots) are great at self-contained tasks: summarising, drafting, answering questions, generating content. Useful but they still require someone to run the workflow end to end. AI agents go further. They’re designed to operate inside a digital environment, reason across inputs, use tools (APIs, databases, ticketing systems), and execute multi-step work until an outcome is reached within clear boundaries.In plain terms: assistants support work, agents move work forward.The problem this guide helps solveThis guide is for the messy middle of operations, the part most automation can’t reach:Processes with lots of edge casesWorkflows that span multiple departments and systemsTasks that require context, judgement, or prioritisationSituations where speed matters, but mistakes are costlyWhen these workflows stay manual, the symptoms are familiar: long cycle times, recurring backlogs, inconsistent execution, and a growing reliance on “the one person who knows how it works.”What’s inside Sigli’s free AI agent guideFrom Assistants to Agents: Smarter AI for Real-World Ops is built to help teams move from vague interest to concrete action.Inside, you’ll find:A clear explanation of what an AI agent is (and what it isn’t)A practical breakdown of assistants vs agents where each fits6 real-world applications with case studies across healthcare, manufacturing & logistics, finance, retail, sales, and educationA step-by-step roadmap to implement agentic workflows, from ideation to testing and scalingA checklist of common challenges (security, compliance, data integration, adoption, reliability) so you can plan around them upfrontGet a practical view of what’s possible now, what tends to fail, and how to build toward outcomes safely.Download the free guideIf you’re exploring how to reduce operational effort without creating new risk, this guide will give you a strong starting point.👉 Download Sigli’s free AI agent guide here: From Assistants to Agents: Smarter AI for Real-World Ops

AI is often the fastest way to find the real constraint. Not the final answer.Here’s a pattern Sigli notices again and again. A team starts with a clear request: “We need AI.” They want new predictive features , automated insights, smarter workflows and a step-change in competitiveness.Then discovery begins, and the “AI project” quietly turns into something else: data pipelines and data requirements, workflow and model delivery improvements, infrastructure constraints (often security / residency), documentation and operational reliability.At first, this can feel like scope drift. But in fact, It isn’t. It’s the project doing what it’s supposed to do: finding the bottleneck early, before anyone wastes months building a model that can’t reliably run, can’t integrate, or can’t be trusted.A simple truth: if your “AI project” becomes a data/process/integration project, it often means you found the real constraint early.AI isn’t always the end solution. Often, it’s the diagnostic.AI forces uncomfortable specificity.The moment you try to ship something real, you have to answer questions like:What exactly goes in, and what must come out?Where does the data come from, and can we rely on it?How often does this need to run?What happens when it’s wrong?Where in the workflow will people actually use it?And that’s when you discover the reality: the model isn’t the hard part. The system around it is.A real example from Sigli's clientIn one of Sigli’s case studies, a UK property data platform wanted to “implement advanced, up-to-date” machine learning to enrich their data and power new customer features. A typical “AI project,” on paper.But the work that mattered most, what made the AI shippable, looked like this:partnering with the client’s in-house data science team to build new data pipelines and enhance development workflows, alongside ML implementation developing dozens of pipelines to streamline data processing and enable expansion of the feature set auditing and improving existing ML models that were slow and inefficient, because performance issues were blocking day-to-day workflows working under a real constraint: some datasets were confidential, so implementation happened on the client’s on-site servers rather than in the cloud In other words: the “AI project” immediately surfaced that the real work was data readiness + delivery mechanics + infrastructure reality.The most common “something else”: AI-ready dataWhen people say “data quality,” they often mean something broader and more practical:data exists, but it’s not usabledefinitions vary (“what counts as on-market?” “what is a prospect?”)input/output requirements aren’t explicitpipelines aren’t repeatable or fast enoughkey datasets can’t move freely because of confidentialityThat’s why the case study’s three-step approach begins with a review of infrastructure and an audit of existing models plus input/output data requirements and pipelines, before building anything new. And it’s why the delivery centers on pipelines as “engines behind new product features,” not just models. If you want a blunt version: if you can’t move and trust data end-to-end, the smartest model in the world won’t help.What “AI as a diagnostic” looks like in practiceIn the property platform project, the diagnostic showed up as four clear “pivot causes”:1) Data readinessThe project needed multiple / dozens of data pipelines to make the platform’s insights and features possible at scale. That’s a classic signal that value depends on reliable data movement, not on “more AI.”2) Workflow reality (performance and iteration)Existing ML models were slow, and that slowness was interfering with workflows. So the project became: audit, improve, and establish workflows that allow faster feature rollout.3) Infrastructure constraintsConfidential datasets forced an on-prem approach rather than a cloud-first architecture. That single constraint changes everything: tooling, deployment, monitoring, and iteration speed.4) Operational debt (often hidden until AI work begins)The team had to work through a lack of documentation, large/complex datasets, and the usual pain of a tech stack transition. That’s another “AI diagnostic” pattern: AI work exposes the systems you can’t safely evolve yet.So… what shipped?What shipped wasn’t “just AI.” Sigli and the client’s internal team delivered infrastructure upgrades including new pipelines, advanced ML functionality, and improved workflows to release new features faster. And those foundations unlocked concrete product outcomes, like expanded feature capabilities including property tracking and market trend analysis for end users. This is exactly why “turning into something else” is a good sign:you don’t get trapped in prototype landyou build the machinery that makes insights repeatableyou leave the client stronger, not dependent on a one-off modelA quick checklist: are you building AI, or are you diagnosing a constraint?If you want to tell early whether an AI project will “turn into something else,” ask:Do we have clear input/output data requirements? Can we run the data flow end-to-end reliably (even once)?Are we constrained by confidentiality / residency (on-prem vs cloud)? Are existing models/pipelines blocking workflows because they’re slow or brittle? Do we have the documentation and ownership needed to maintain this? “AI projects turning into something else” is often the moment a team stops chasing the buzzword and starts shipping.The bad outcome isn’t a pivot. The bad outcome is an AI demo that can’t survive contact with real systems, real constraints, and real users.The good outcome is what this case study shows: AI work that acts like a diagnostic, and creates durable foundations (data pipelines, delivery workflows, infrastructure choices) that make future AI faster, safer, and actually valuable.