Launched this week
Leni
The world’s most accurate AI for investors
1.3K followers
The world’s most accurate AI for investors
1.3K followers
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.
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..
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
Leni
@fberrez1 thanks and great pushback. You’re right that for others “most accurate AI for finance” claims usually collapse as soon as you introduce (a) multi-period reasoning and (b) conflicting sources.
Here’s what we mean, concretely:
1) Benchmark, baseline, query types
We benchmark on investment workflow tasks, not generic QA. These are multi-step jobs that require retrieval, extraction, and deterministic computation across periods. Examples include roll-forwards, bridge analyses, same-store calculations, covenant and debt schedule math, lease abstraction, and memo reconciliation across multiple documents.
Baseline is frontier LLMs + standard RAG. Those stacks are strong at summarizing, but they tend to fail on exactly these tasks because they silently fill gaps, miss constraints, or do “plausible math.”
2) Why our accuracy claim holds (and why it doesn’t break on multi-period / reconciliation)
We don’t treat the LLM output as the final answer. Leni runs a separate verification step using our own verifier models trained on 31k+ decision traces, built specifically to check outputs for inconsistency, missing evidence, and violated constraints.
For numeric work, we use a deterministic math engine so calculations are executed and reproducible instead of “LLM arithmetic.”
3) What happens when sources disagree
When filings, models, memos, or third-party data conflict, Leni does not choose one silently. We:
surface the conflict explicitly (what differs, by how much, and where it came from)
keep provenance down to the exact source artifact (document section / table / cell where possible)
apply policy-driven resolution if the customer configures it (for example “audited overrides unaudited,” “newer period overrides older,” “filing overrides deck”), otherwise we flag it as requires review and route it as an exception
Benchmark coverage (third-party writeups)
https://briefglance.com/articles/niche-ai-platform-leni-outperforms-openai-google-on-key-benchmarks
https://www.wallstreetmojo.com/leni-benchmark-results-financial-research/
https://dupple.com/blog/how-leni-beat-genspark-and-manus-on-gaia-benchmark
Net: the claim is not “we have a magic model.” It’s that we built the workflow so the model cannot get away with being plausibly wrong, especially on the multi-period and reconciliation cases you mentioned.
Leni
@fberrez1 one more way we think about accuracy: it has to hold at different layers of the workflow.
There is extraction accuracy, calculation accuracy, reconciliation accuracy, and delivery accuracy. A system can be strong at retrieval and still fail when the answer depends on tying a debt schedule to a model, or reconciling actuals vs. budget across periods.
That’s why we care about benchmarks that test different failure modes: GAIA for long-horizon task execution, SpreadsheetBench for cell-exact Excel work, Bullshit Benchmark for rejecting false premises, and DRACO for research quality. The common thread is whether the system can be checked at each step.
Please test it hard and let us know your feedback! :)
@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.
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.
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.
The strongest part of this framing is “finished work products with auditability,” not just a smarter finance chatbot.
For investor workflows, I’d be curious how you expose the reasoning boundary in the final memo/model. Source links and timestamps are necessary, but the trust question often becomes: which assumptions came from source data, which came from Leni’s judgment, and which were explicitly approved or edited by the analyst?
If those three states stay visible, the output becomes much easier to defend in an IC meeting because people can challenge the assumption layer without distrusting the whole artifact.
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!