Startups are increasingly using AI to handle legal and accounting tasks themselves, avoiding high professional fees. This signals a significant market need for tools that formalize and support this DIY approach, especially as startups scale and require more robust solutions for investors.

The company provides public benchmarks for free to build trust. It monetizes by selling private benchmarking services and subscription-based enterprise reports, ensuring AI labs cannot pay for better public scores and thus maintaining objectivity.

Most successful SaaS companies weren't built on new core tech, but by packaging existing tech (like databases or CRMs) into solutions for specific industries. AI is no different. The opportunity lies in unbundling a general tool like ChatGPT and rebundling its capabilities into vertical-specific products.

Resource-constrained startups are forgoing traditional hires like lawyers, instead using LLMs to analyze legal documents, identify unfavorable terms, and generate negotiation counter-arguments, saving significant legal fees in their first years.

Harvey's initial product was a tool for individual lawyers. The company found greater value by shifting focus to the productivity of entire legal teams and firms, tackling enterprise-level challenges like workflow orchestration, governance, and secure collaboration, which go far beyond simple model intelligence.

Founders can get objective performance feedback without waiting for a fundraising cycle. AI benchmarking tools can analyze routine documents like monthly investor updates or board packs, providing continuous, low-effort insight into how the company truly stacks up against the market.

AI tools enable solo builders to bypass the slow, traditional "hire-design-refine" loop. This massive speed increase in iteration allows them to compete effectively against larger, well-funded incumbents who are bogged down by process and legacy concerns.

The company wasn't built to solve a minor inconvenience. It was born from founder Jack Kokko's intense fear as an analyst of missing critical information in high-stakes M&A meetings. This deep-seated professional anxiety, not just a need for efficiency, fueled the creation of a market intelligence platform.

The founders built the tool because they needed independent, comparative data on LLM performance vs. cost for their own legal AI startup. It only became a full-time company after its utility grew with the explosion of new models, demonstrating how solving a personal niche problem can address a wider market need.