We’re excited to announce the launch of the Hudson Labs Co-Analyst — high-precision AI built for institutional investors. The Co-Analyst delivers what other AI tools can’t: terminal-grade precision with the adaptability of AI

Introducing the Hudson Labs Co-Analyst

The launch follows extensive beta testing with hedge funds, asset managers, and family offices representing over $1 trillion in assets under management (AUM). Early results show the platform reduces research time by more than 50% while maintaining the accuracy, reliability, and auditability institutional investors demand.

The Co-Analyst gets it right by applying proprietary retrieval systems, custom AI models, source selection, noise suppression technology, and in-house pipelines to ensure institutional-grade precision, transparency, and trust.

“Generalist AI tools simply don’t meet the standards of institutional finance,” said Kris Bennatti, CEO and Co-Founder of Hudson Labs. “The Co-Analyst combines the precision of a terminal with the adaptability of AI, allowing investors to spend less time chasing data and more time making decisions.”

Get the precision of a terminal with the adaptability of AI

• Any metric. Any format.
• Only the facts. Direct-from-source. Every time.
• No prompting gymnastics. Just ask and get results.

Get up to speed in 2 hours instead of 2 weeks

• Management commentary linked directly to the numbers. Track trends side-by-side with management quotes on drivers, exceptions and hedges.
• Soft guidance captured — every word, every hedge. No missed forward-looking statements, no guesswork.
• All documents, one clear answer. Filings, releases, decks, transcripts — synthesized so you don’t miss a line.
• Verbatim summaries — no spin, no paraphrase. The most important management statements, word-for-word.

Client feedback shows the impact

The launch follows extensive beta testing with hedge funds, asset managers, and family offices representing over $1 trillion in combined assets under management (AUM). Early results show the platform reduces research time by more than 50% while maintaining the accuracy, reliability, and auditability institutional investors demand.

"Honestly, I think this kind of data collation…is not only differentiated, but also helps bring it closer to being like an actual "co-analyst" rather than a glorified ctrl-F. Great job on that, it's the best I've seen.”

Tripp Blumm
Portfolio Manager, Princeton Value Partners

“I’m blown away by what you’ve built…just looked at three companies with the Co-Analyst and added one to my watchlist because I could easily see that the company has a huge pipeline that I would have missed or taken at least two hours to find.”

Ken Taylor
Analyst at Telos Research

“I also use Hudson Labs to do some of these processes too to sort of take you know what was a two or three hour process down to a 20-minute process.”

Brett Caughran
Fundamental Edge

‍

Integrate high-precision AI into your workflow today with a 14-day trial. Start your trial here.

‍

Generalist artificial Intelligence fails most dramatically in finance-specific use cases:

• Multi-document analysis
• Guidance identification
• Numeric accuracy and precision

But where generalist models fail, the Co-Analyst gets it right — fast, precise, and source-verified.

Learn more about solutions to generalist AI failure modes here, how we compare to Perplexity Pro here, and to OpenAI here.

‍

‍

About Hudson Labs - Technology Leadership

‍
Hudson Labs has been at the forefront of finance-specific AI since its founding. The company launched the first-ever LLM-powered financial application in 2021. Co-Founder Suhas Pai, author of Designing Large Language Model Applications (O’Reilly), brings deep expertise in AI research, while CEO Kris Bennatti, a former CPA and data scientist, bridges technical innovation with capital markets experience.
‍

Hudson Labs’ approach has earned recognition in Forbes, The Wall Street Journal, and CNBC, and the firm’s technology has demonstrated success in surfacing financial irregularities before they became public.

‍

‍

‍

‍