Leni

Congrats on the launch. The auditability angle is what got me, since that's the part most finance AI tools gloss over. Curious how you're thinking about third-party verification down the road, like giving an auditor or LP a way to independently confirm an output and its sources. Either way this looks really strong.

The combination of AI-powered research and clear source attribution is refreshing. Looking forward to seeing how investors and analysts incorporate this into their workflows.

Most AI tools help you find answers. Leni seems focused on helping users understand why those answers make sense. That distinction is incredibly important in investing. Great launch!

@arunabh_dastidar congrats on the launch! How well does this handle source data quality issues / discrepancies / missing data / disparate sources that tends to always appear in middle-market private M&A transactions? This is part of the automation puzzle I feel is the most difficult - it's whether the source data at the bottom is any good and how to efficiently correct it if it isn't

Hey Product Hunt 👋 I’m Zain, co-founder at Leni. A lot of our work on Leni has come from sitting close to real investment and commercial real estate workflows and seeing where AI actually breaks. It usually isn’t the final paragraph. It’s the step before it: • Which rent roll did this number come from? • Did the model use the right NOI definition? • Why does the OM say one thing and the T12 another? • Is this based on the latest file, or the one someone uploaded two weeks ago? • Can this survive a partner review, lender question, IC memo, or investor update? That is the bar we built around. Leni helps investment and real estate teams move from scattered docs, spreadsheets, systems, and research into structured work products: underwriting support, market research, IC memos, portfolio reporting, diligence trackers, and source-backed answers. The part I’m most proud of is that Leni is designed to slow down in the right places. If the evidence conflicts, it should show the conflict. If the assumption is missing, it should ask. If a number is calculated, it should be reproducible. If a definition changes, the system should know which version was used. If the answer cannot be supported, it should say so. That sounds less flashy than “instant AI answer,” but it’s what serious teams kept asking us for. Commercial real estate teams taught us what accuracy really means in practice: numbers that tie back, assumptions that can be reviewed, sources that are easy to inspect, and outputs that hold up when real decisions are being made. We delivered against that standard, and then pushed ourselves to take it further across spreadsheets, research, reporting, and multi-step workflows. Excited to finally share Leni with the Product Hunt community today. Would love to hear what you would test first: • underwriting? • investor reporting? • market research? • document review? • internal knowledge / Q&A? • something else entirely? We’ll be here all day answering questions and learning from the feedback 🙌 P.S. Product Hunt community gets 90% off the first month with code PHLENI, valid today.

Very proud to see Leni launch today 🎉 Working with real estate and investment data has reinforced that accuracy starts long before analysis. Good data foundations make trusted answers possible, and it has been rewarding to contribute to that work.

Hallucinated figures are one of the top reasons that actually helpful platforms aren't adopted. Glad the Leni team has listened to the real estate and investment teams specifically to create something custom built and something you can trust in to give you dependable results every time. Congrats on the launch today! 🍾

Super interesting product, congrats on the launch!

How does your “decision trace” and private context graph work over time—what gets stored, how do you prevent bad assumptions from becoming institutional memory, and how do you handle changing definitions (e.g., NOI, occupancy, same-store) across teams?

This is a strong space to build in, especially because Leni seems to sit close to real investment workflows like underwriting, portfolio reporting, market research, document review, and IC memo creation.I was curious about the governance layer around this.You already mention structured traces and an institutional context graph, which is interesting. How are you thinking about the human approval side of that trace?For example, when an AI-generated underwriting note or market risk flag influences an investment memo, how do you capture who reviewed it, what assumptions they accepted, and why the team trusted that output at that point?In investment workflows i feel like auditability feels less useful if it only shows source links, timestamps, and model history. The harder part is tracing the judgment around the decision.Would love to understand how you are approaching this.

What are your subscription plans? Where can I find more information? I browsed through your site, but it wasn’t obvious.

Nice one @arunabh_dastidar ! Upvoted :)Question: How do you validate that there was no hallucination at all? Do you show an audit trail back to the exact cells/files used or something?

Most "most accurate AI for finance" claims fall apart the moment you ask something that requires reasoning across multiple time periods or reconciling conflicting signals in the data. What's the actual benchmark here, accuracy against what baseline, on what types of queries? And I'm curious how Leni handles cases where the underlying data sources disagree, like when reported earnings differ across filings or analyst estimates conflict with management guidance.Congrats for the launch tho

Congrats on the launch! 🎉Curious — what was the biggest challenge in building an AI that investors can actually trust with high-stakes decisions? Was it the accuracy, the auditability, or getting users comfortable relying on AI for investment research? 👀Looks like a really ambitious product. Wishing the team a successful launch day!

So true about the lack of trust. We tried a couple of AI document tools earlier this year and they completely lost the plot whenever a PDF layout wasn't perfectly clean or a spreadsheet had complex formulas. If Leni actually handles hundreds of files at once without breaking, it's going to save lean ops teams a ton of time. Love that you built a proper verification layer instead of another chatbot. Going to test this out today. Great work team..