Practical wisdom for founders and investors navigating the AI landscape

Funding a startup is tough. As someone who founded a hardware B2C company a decade ago, I know firsthand how challenging it can be to secure investment for high-risk, capital-intensive ventures. While equity funding was my initial goal, I quickly discovered the value of public grants as an alternative funding source.

The process, however, was far from efficient. I spent weeks and months searching for suitable grants and writing applications—valuable time that should have been dedicated to product development and customer acquisition.

This personal frustration led to the founding of StartMatch.ai, an AI-powered platform that generates tailored applications in less than half the traditional time. By giving founders back their most precious resource—time—we help them focus on what truly matters: building their products and serving customers.

Along our journey developing this B2B AI tool, we’ve gathered several important insights that may benefit both startups building AI solutions and the investors supporting them.

Learning #1: AI Doesn’t Equal AI – Model Selection Matters

One of our earliest and most significant realizations was that not all AI models are created equal. The performance of LLMs varies dramatically, and selecting the right one for your specific use case can be a game-changer.

When we began developing StartMatch.ai, we initially experimented with several popular models. The differences in output quality and consistency were striking. Some models excelled at creative writing but struggled with structured documents like grant applications. Others demonstrated impressive factual recall but lacked the writing capabilities essential for compelling applications.

For startups building AI tools, it’s critical to rigorously test multiple models against your specific requirements rather than simply selecting the most hyped option. For investors, understanding that a startup has conducted thorough model evaluation and selection can be a strong indicator of technical diligence.

Learning #2: B2B Customers Prioritize Privacy and Data Protection

If you’re building a B2B AI product, prepare for intensive discussions about data protection, GDPR compliance, and privacy. We received more questions about these areas than about features or capabilities.

We discovered that prospective customers are as interested in our AI’s impressive writing abilities as in how we handled their sensitive business information. Questions about data storage, processing locations, and security measures dominated our sales conversations.

For startups, building good safeguards from the ground up is far more efficient than retrofitting them later. For investors, recognizing that privacy features are not just compliance requirements but essential selling points in the B2B space can help identify promising AI ventures.

Learning #3: LLMs Struggle with Consistency and Counting

While LLMs demonstrate remarkable capabilities in many areas, they have specific limitations that required substantial engineering to overcome.

Grant applications typically have strict character or word count limits. We discovered that LLMs are surprisingly poor at counting and consistently staying within these boundaries. What seemed like a straightforward requirement—“write a compelling project description in 2,000 characters“—often resulted in submissions that were significantly over or under the limit.

Similarly, consistently extracting data from resource documents proved challenging. And it makes a big difference if the maximum supported project volume is €20.000 or €200.000.

Addressing these limitations required developing specialized prompting techniques, implementing post-processing validation systems, and creating robust review mechanisms. For AI startups, recognizing and engineering around these model limitations early can prevent significant issues later. For investors, understanding that successful AI products require substantial engineering beyond simply connecting to an API is crucial for evaluating technical teams.

Learning #4: AI Literacy Remains Surprisingly Low

Despite ChatGPT being publicly available for over two years, we’ve found that AI literacy among potential B2B customers varies dramatically.

Many of our customers have limited experience with AI tools, often citing privacy concerns and fears about hallucinations as reasons for their hesitation. This lack of familiarity creates some challenges.

For startups, gauging your target market’s AI literacy and tailoring your messaging accordingly is essential. For investors, companies that demonstrate awareness of and strategies for addressing varying AI comfort levels may be better positioned for broader market adoption.

Learning #5: LLMs Are Relatively Affordable

One of the most pleasant surprises in our journey has been the accessibility of powerful AI models. Every major LLM provider offers generous free credits for startups, and even their regular pricing remains reasonable considering the capabilities these models deliver.

The intense competition between OpenAI, Anthropic, Google, and the advent of open source models has created a favorable environment for startups building on these foundations. This competitive landscape has not only kept prices in check but has also accelerated model improvements and specialization.

For startups, this means that building sophisticated AI products is more financially viable than ever. For investors, it highlights that the barrier to entry for AI startups has shifted from access to technology to the quality of implementation and domain expertise.

The Path Forward

Building a B2B AI tool has been a journey of continuous learning and adaptation. At StartMatch.ai, we’ve transformed my personal frustration with grant applications into a solution that helps founders reclaim time for what matters most—building great products and serving customers.

For those considering similar ventures, remember that success in B2B AI isn’t just about having the most advanced models, but about thoughtfully applying them to solve real business problems while addressing the legitimate concerns of enterprise customers.

And for investors evaluating AI startups, looking beyond the hype to examine how companies handle model selection, data privacy, technical limitations, customer education, and cost optimization may provide valuable signals about their potential for long-term success.

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About the Author: Robert Kopka is the founder and CEO of StartMatch.ai, an AI-powered platform that helps startups find and apply for suitable grants in half the time. He is also the founder of Luke-Roberts.com where he developed smart lighting solutions before he sold the company to a competitor in 2021.