The Co-Analyst is built for institutional equity research - providing the precision of a terminal with the adaptability of AI. Get the data you need — fast, precise, and verifiable.
I’m Kris, co-founder of Hudson Labs with @piesauce. After years in finance and AI research, we’re excited to introduce the Co-Analyst — our high-precision AI platform built for institutional investors, hedge funds, and asset managers
Why we built this
We built the Co-Analyst after seeing the limits of traditional terminals like Bloomberg and FactSet. They’re great for standardized data, but analyzing guidance trends, niche operating metrics, supply chain exposure, product-level results etc. still require painfully manual workflows.
Generalist AI tools haven’t solved this. They break down in the workflows that matter most to investors — multi-document and multi-period analysis, guidance extraction, and numeric accuracy — failing to meet the reliability standards of institutional-grade research.
So we built the Co-Analyst:
- 🎯 Terminal-grade precision + AI adaptability - 🗂 Any metric, any source, any format — filings, transcripts, presentations, press releases - 📈 Soft guidance captured — every hedge, every forward-looking statement - 📝 Verbatim call summaries — no spin, no paraphrase - ⚡ 2 hours instead of 2 weeks to get up to speed on a new name
All direct-from-source, with no hallucinations, no prompting gymnastics.
Feedback:
- Any big wins you’ve had using AI in investment research? - Any frustrations? - What do you want from an AI tool for investing?
Finally a product built for finance people, by finance + AI people. Most tools force generic models into finance use cases and miss the mark. This one actually feels like a big step forward for analysts and portfolio managers.
As someone who’s wrestled with multi metrics and formats and different period analysis, this resonates. Bloomberg is great, but it’s not built for that kind of nuance. This looks like a big step forward. Hugeee Congrats!
Congrats on the launch! This looks great.
Making sure that the data is pulled directly from the sources seems obvious and somehow all the other products in the space, Perplexity included, don't do this.
Incredible seeing the accuracy of the co-analyst. I've used several tools and have always faced problems with data accuracy and retrieval.
Congratulations to the team.
Managing user accounts often feels messy, but this approach really streamlines the whole process.
Congrats on the launch! Really like how you’re tackling the “last mile” of research, extracting the metrics and nuance that usually fall through the cracks in terminals.
From my own experience, the real pain is never just getting numbers; it’s structuring them in a way that supports confident decisions. We’ve seen a similar thing on the strategy side with Escape Velocity AI: founders don’t just want data, they want frameworks that help them test assumptions and see if the story holds up.
Do you see early adopters leaning on Co-Analyst more for speed (cutting 2 weeks to 2 hours), or for surfacing insights they couldn’t realistically get before?
Want to know whether the hallucination generated by its AI is strong? After all, financial markets change all the time. If it can balance accessibility and depth, I believe that Hudson Labs can be suitable for investors and academic researchers, useful for quick investment decisions, and rigorous enough for academic research.
Hey Product Hunt 👋,
I’m Kris, co-founder of Hudson Labs with @piesauce. After years in finance and AI research, we’re excited to introduce the Co-Analyst — our high-precision AI platform built for institutional investors, hedge funds, and asset managers
Why we built this
We built the Co-Analyst after seeing the limits of traditional terminals like Bloomberg and FactSet. They’re great for standardized data, but analyzing guidance trends, niche operating metrics, supply chain exposure, product-level results etc. still require painfully manual workflows.
Generalist AI tools haven’t solved this. They break down in the workflows that matter most to investors — multi-document and multi-period analysis, guidance extraction, and numeric accuracy — failing to meet the reliability standards of institutional-grade research.
So we built the Co-Analyst:
- 🎯 Terminal-grade precision + AI adaptability
- 🗂 Any metric, any source, any format — filings, transcripts, presentations, press releases
- 📈 Soft guidance captured — every hedge, every forward-looking statement
- 📝 Verbatim call summaries — no spin, no paraphrase
- ⚡ 2 hours instead of 2 weeks to get up to speed on a new name
All direct-from-source, with no hallucinations, no prompting gymnastics.
Feedback:
- Any big wins you’ve had using AI in investment research?
- Any frustrations?
- What do you want from an AI tool for investing?