Leni - The world’s most accurate AI for investors
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Leni is the most accurate and verifiable AI for serious investment work. Built on 21,000+ decision traces and processing 100M+ rows daily, it delivers finance-grade outputs with full auditability through source links, timestamps, and grounded comps. Leni outperforms GPT, Claude, and Manus on independent benchmarks for accuracy, modeling, and valuation while giving teams the trust they need when millions are on the line. Leni is part of Google Startups and a serious machine for investors.
Replies
Mailwarm
Love the positioning. In investing, accuracy matters more than speed alone. A wrong model or uncited assumption can cost real money. Turning scattered docs into verified, cited memos feels like the right workflow for investment teams.
What’s the strongest early use case so far: acquisition memos, underwriting models, or portfolio reporting?
Leni
Great question, Thami. The biggest opportunity we're seeing is investor reporting and market research, the work that drives sound investment decisions. That's where scattered, uncited data costs the most, and where verified, source-backed output changes the game. Acquisition memos and underwriting matter too, but reporting and research are where teams feel the lift first.
Leni
Thanks@thamibenjelloun! The thing we’ve seen is that reporting (internal or external) tends to be a great wedge because it's recurring, painful, and very easy to judge. When Leni helps a team pull together a cleaner investor update, explain portfolio movements, cite the right sources, and catch inconsistencies before the meeting, the value is realized immediately.
Leni
@thamibenjelloun I’d add one reason reporting tends to be a strong early use case: it repeats.
An acquisition memo may be high-value, but reporting creates a cycle where the same team has to explain performance, variances, leasing movement, capex, occupancy, budget vs. actuals, and portfolio changes again and again.
When Leni helps carry forward the structure, pull the right sources, and show what changed since the last period, the workflow improves each cycle. That repeatability is where teams start feeling the lift quickly
Leni
@zain_nj - great add!
Unslack
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! 🍾
Leni
@ashamplifies - thank you, my man!! We're here to hear every feedback and suggestion!
Leni
@ashamplifies - Thank you! And Leni is all yours to use and abuse, would love for you to try it and share your feedback :)
Leni
@ashamplifies thank you, really appreciate it!
Commercial real estate teams were very clear with us about what “accuracy” means for them: numbers that tie back, assumptions that can be reviewed, sources that are easy to inspect, and outputs that hold up when someone is making a real decision.
We listened, built for that standard, and then challenged ourselves to take it further across spreadsheets, research, and reporting workflows.
Excited to have Leni live and get feedback from people outside our usual real estate and investment circle too!
EverTutor AI
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!
Leni
@suryansh_tiwari2 Thank you!
Biggest challenge was getting reliable accuracy under real-world messiness, then making that reliability provable. Accuracy and auditability are tightly linked: you need strong extraction/reasoning, plus verification checks and traceability so an investor can see what drove the answer and where it came from.
The “comfort relying on AI” part comes last in our experience. Once the outputs are consistently correct and explainable, trust follows.
EverTutor AI
@arunabh_dastidar love the insight that trust comes after consistently correct and explainable outputs.
Leni
@suryansh_tiwari2 I’d answer it a little differently: the hardest part was teaching the system when to stop.
In investment work, the dangerous failure mode is not always a bad final sentence. It is an earlier assumption that slips through and then infects the model, memo, market read, or reporting narrative downstream.
So a lot of the product work went into decision boundaries: when should Leni continue, when should it ask for a missing input, when should it run a check, and when should it say “this needs review”?
That is also why benchmarks matter to us. The useful test shows whether the system can complete multi-step work and still be checked.
This writeup covers some of that: https://dupple.com/blog/how-leni-beat-genspark-and-manus-on-gaia-benchmark
Comfort from users comes after they see that behavior: the system knows when the answer is not ready yet and will flag it.
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.
Leni
@ye_tao1 thanks for being the integral part of the journey.
Leni
@ye_tao1 really appreciate you saying this, and thank you for being part of that foundation!
@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
Leni
@arunabh_dastidar @millwiller this is one of the hardest parts of applying AI to M&A.
The system has to treat source quality as part of the job. In a real data room, the CIM, QoE, model, exports, and management deck may all be “official” in different ways, but they may not agree.
So Leni should first build a source map: what file says what, which period it refers to, whether it reconciles to the model, and where the breaks are.
If revenue by customer does not tie to the financial model, that should become an exception to resolve, not a part of a confident summary.
That is also why we obsessed with benchmarks around grounded research and spreadsheet execution.
The output has to be checkable across documents and calculations, especially when the source package itself is imperfect:
https://briefglance.com/articles/niche-ai-platform-leni-outperforms-openai-google-on-key-benchmarks
@arunabh_dastidar @zain_nj Got it thanks guys! So instead of ignoring discrepancies and producing some kind of (possibly flawed) answer, it will identify them so that they can be resolved. The structured outputs with the gaps marked seem very useful as well with coordinating with relevant parties to close everything out (i.e. CFOs, etc.)
Leni
@millwiller - Exactly. The goal isn't to pretend messy data is clean but to make the mess visible, structured, and actionable.
Leni
@millwiller Totally agree. In mid-market private M&A, the hardest part is often not the model, it’s the messiness of the source layer.
Where Leni does well is treating “data quality” as a first-class problem, not an afterthought:
Provenance + traceability: we keep citations back to the exact source snippets used, so you can see what the system relied on and where it came from (and quickly spot when a source is wrong or outdated).
Discrepancy detection: when multiple sources disagree (or values don’t reconcile), we flag it explicitly rather than forcing a single answer. You get a “here are the competing values + confidence + why” view.
Missing/partial data handling: we’ll return structured outputs with gaps clearly marked, plus a list of the specific fields and documents that would resolve the gaps (vs. vague “need more info”).
Efficient correction loop: the practical win is that once you correct or confirm a value, that resolution becomes a reusable context for the next deal and next report, instead of re-litigating the same discrepancy every time.
Net: the goal isn’t to pretend the bottom-of-funnel data is clean. It’s to (1) surface what’s unreliable, (2) quantify the uncertainty, and (3) make the “fix” workflow fast and auditable.
If you have a concrete example (e.g., NOI, rent roll, debt schedule, capex history) where sources commonly conflict, I can tell you how we typically set up the checks and the correction flow for that pattern.
Netlify
Hey PH fam 👋
I've been watching AI stumble in high-stakes professional work for a while now. Real estate and investment teams can't afford hallucinated numbers in an underwriting model or a memo that cites something that doesn't exist. The cost of that mistake isn't a slap on the wrist. It's a blown deal.
That's the exact problem Leni was built to solve.
Leni is an AI agent built specifically for real estate and investment teams. Not a general-purpose chatbot pointed at your files. A purpose-built system designed to handle the kind of work where accuracy is non-negotiable.
Here's what makes it different:
🏗 It connects to the actual systems your team already uses. Yardi, Entrata, RealPage, AppFolio, ResMan and more. No manual data wrangling. No explaining your world from scratch every time.
🔍 It doesn't just generate. It verifies. Multi-agent architecture cross-checks work and reduces hallucinations before anything lands in your hands.
📊 It delivers finished work products. Underwriting models, IC memos, lease abstracts, market research with cited sources. Not a 25-message thread you have to babysit.
🔐 It's built for sensitive data. Containerized models, strong guardrails, and a private institutional context graph that gets smarter about your firm over time.
And it's model-agnostic. Use your favorite LLM or let Leni route across models automatically for the best output. You're not locked into one model's limitations.
For anyone who's been burned by AI that sounds confident but gets the numbers wrong, this one's worth a serious look.
Big respect to @arunabh_dastidar and the Leni team for tackling one of the hardest problems in enterprise AI: not just being smart, but being trustworthy.
Check it out and drop your questions below!
Leni
@arunabh_dastidar @thisiskp_ I see amazing comments and feedback, and we are not half way through the day yet!
Keep them coming, guys!
I'm having the time of my life answering all these questions 🧐
Leni
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.
Wion - Audio Dating
Leni
@tanjum Thank You, Tanjum.
Earth.fm
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!
Leni
@1mirul - Thank you!
Leni
@1mirul Much appreciated
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.
Leni
@kevin_minn - Great question! We already have that baked in. A key part of leni is that all results come with sources and are independently verifiable.