Launching today
Leni
The world’s most accurate AI for investors
520 followers
The world’s most accurate AI for investors
520 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.
Leni
Hey Product Hunt 👋
I’m Arunabh, Co-Founder & CEO of Leni.
Three years ago, we started with a simple observation:
The smartest people in investing were spending an absurd amount of time moving data between systems, fixing spreadsheets, validating reports, and checking the outputs of tools that were supposed to save them time.
Everyone was talking about AI.
But when real money was involved, most professionals still didn't trust it.
And honestly, they were right.
In high-stakes work, "mostly correct" isn't good enough.
A wrong number, a missed assumption, or a hallucinated fact can cost millions.
So instead of building another chatbot, we spent years working alongside sophisticated investors, operators, lenders, and asset managers to understand what trustworthy AI actually looks like.
Since then, we've supported more than $80B in assets, processed over 100 million rows of investment data every day, built proprietary verification systems, and tested relentlessly against real-world workflows.
The result is Leni.
THE most reliable and accurate AI infrastructure platform for investors and back office work that can analyze hundreds of files simultaneously, reason through complex tasks, validate its outputs, and deliver finished work instead of just generating responses.
In independent testing, Leni now ranks among the top AI systems for spreadsheet analysis, reasoning, and resistance to hallucinations. That work also led to our selection as one of the few companies invited to Google's Gemini Forum, where we've had the opportunity to collaborate with the DeepMind team.
But what excites me most isn't a benchmark result.
It's seeing professionals finally trust AI with the work that actually matters.
Huge thank you to our team, customers, advisors, investors, and everyone who helped us get here.
We’re excited to finally put Leni and its API portal into the hands of the broader Product Hunt community and see what you build with it.
We'll be here all day answering questions, gathering feedback, and learning from the community.
My team and I are here all day. Ask us anything 🙌
P.S. 🎁 Exclusive for the Product Hunt community: Try Leni.co directly on the platform or via APIs today with code PHLENI to get 90% off your 1st month's subscription on any plans, valid till the end of the day!
Pixelesq
@arunabh_dastidar Congrats on the launch!!
Two things I'm curious about. The model-agnostic routing, how does Leni decide which LLM handles what? Is it task-based, like one model for number-crunching and another for writing memos, or something more dynamic? And does the user get any say in that or is it fully behind the scenes?
Also, as a founder myself, I'm curious how you got the first few institutional customers to actually trust AI with real money decisions. That's probably the hardest cold start problem in enterprise AI. Did you have to start with low-stakes work and earn your way up, or did one customer go all in early?
Leni
@devanandb thank you! Devanand, really thoughtful questions. Let me answer in two parts.
1) “Model-agnostic routing”: how does Leni decide which LLM handles what, and does the user have a say?
At a high level, it’s more dynamic than “one model for math, one model for writing,” but we do use that spirit (specialization) under the hood.
We use a planner/executor architecture:
Planner: breaks your request into a typed step graph (for example, “retrieve the right source data,” “compute and reconcile numbers,” “write the memo,” “validate outputs”).
Executors: each step is dispatched to the best “worker” for that job, which can be a different model (or tool) depending on the step’s requirements (reasoning depth, speed, cost, strictness, context window, etc.).
Results flow back to the planner, which can adapt the remaining plan based on what came back, including running verification passes.
So yes, it’s task-aware, but also context-aware and adaptive step-by-step, not a static mapping.
On user control, we do both:
For most people, it’s behind the scenes with a strong Auto default (so you don’t have to become an LLM ops engineer).
But we also believe enterprises should be able to standardize on approved models/providers, and in some cases force routing constraints for compliance, security, procurement. The workflow should not change when your firm’s model policy changes.
2) How did we get the first institutional customers to trust AI with real money decisions?
You’re exactly right: trust is the cold start problem.
The honest answer: we didn’t start by asking anyone to “trust the AI.” We started by earning trust operationally, in a few deliberate steps:
Start where the pain is high but the blast radius is controlled
Early use cases were time-sink analyst work (data pulls, consistency checks, first-pass drafts, reconciling numbers across sources) where the team could still review outputs quickly.
Win on verifiability, not vibes
Institutions don’t care if the answer sounds smart. They care if it’s right, and if you can show why. So we focused on:
deterministic data retrieval from their systems,
explicit calculations,
consistency checks,
trust-building behaviors like surfacing assumptions and tying outputs back to source artifacts.
Meet them where their data already lives, and keep it secure
A lot of early trust came from being able to operate inside the reality of institutional stacks (property management, reporting systems, deal docs, models) and being clear on security boundaries (no cross-client leakage, no training foundation models on client data, etc.).
Expand scope only after repeated “no-surprise” outcomes
Once teams saw the same level of quality across multiple cycles (monthly reporting, portfolio monitoring, underwriting support), they naturally moved from “low-stakes” to “real decisions,” because the system had already proven it could behave like a reliable analyst.
So no single customer “went all in” on day one. It was more like: prove accuracy, prove security, prove repeatability, then scale.
If you want, I can share a concrete example of what a routed step graph looks like for something like “build an IC memo, tie-out numbers to the model, generate a lender-ready package.” That tends to make the routing concept click fast.
Pixelesq
@arunabh_dastidar Really appreciate the detailed breakdown!
he planner/executor architecture makes a lot of sense, especially the part about enterprises being able to force routing constraints for compliance without changing the workflow.
'Win on verifiability, not vibes' is a great way to put it.
Wishing you guys a great launch!"
Arunabh congrats on the launch! The accuracy-first framing really stands out. Most tools chase fluency and quietly hope the numbers are right, so flipping that order (retrieval and extraction before generation) feels like the correct instinct for finance. Curious how the verification layer handles a conflict - if two source documents disagree on a number, does Leni surface the discrepancy or resolve it for you?
Leni
@tom_palmer_ux thank you! Tom, really appreciate that, and +1 on “retrieval/extraction before generation” being the right instinct for finance.
On conflicts: Leni surfaces the discrepancy first rather than silently picking a winner. When two sources disagree, we:
Show both values side by side, tied back to the exact underlying excerpts (and source docs) so you can see why they differ.
Run a verification step that tries to explain the conflict (for example, timing/cutoff dates, pro forma vs. actual, consolidated vs. property-level, unit mix changes, definition mismatches like NOI vs. NCF, etc.).
If it’s resolvable with clear rules/evidence, we’ll propose a recommended resolution (with rationale + trace). If it’s not, we’ll flag it as “needs human judgment” and let you choose which to carry forward (or keep both with a note).
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..
Leni
Leni
@priya_kushwaha1 - thank you! Trust is the bottleneck with most AI doc tools.
We built Leni for the messy reality: imperfect PDFs, complex spreadsheets, and real-world models at scale, without breaking when inputs aren’t pristine.
If you hit anything tricky while testing today, send it over and we’ll make sure it handles your use case.
@arunabh_dastidar That's exactly the problem most tools miss. Excited to put Leni through its paces.
Leni
Hello Product Hunt, excited to be live today with Leni. I'm Gaurav, co-founder at Leni.
Leni is an accuracy-first AI platform for investment finance and real estate teams. It helps you go from messy documents & siloed systems to structured, verifiable answers with analysis you can actually trust.
AI tools optimize for fluent responses. Leni obsesses over accuracy.
• With verification layers that validate outputs instead of "guessing."
• Decision traces so you can see how an answer was formed and what it was grounded in
• A context graph + Unified Data Model (UDM) that keeps information consistent across documents, models, and entities
• A focus on retrieval + extraction (getting the right facts) before generation (writing the response)
If you work in investments, asset management, credit, capital markets, valuation, or any workflow where a single wrong number can derail a deal, Leni is for you.
Over the years, especially in the last 6 months, it's been rewarding to see skeptics become believers. Teams that started with us as an experiment now rely on Leni for mission-critical work. That trust came from obsessing over accuracy, building robust verification systems, and learning through real implementations.
We'd love feedback from the Product Hunt community:
What workflow are you trying to make "AI-native" today?
Where do existing tools break down on trust/accuracy?
Thanks to our customers, team, advisors, investors, and early supporters who believed in us before this became obvious.
We're here all day so fire away with questions 🙌
P.S. 🎁 Exclusive for the Product Hunt community: Try Leni.co directly on the platform or via APIs today with code PHLENI to get 90% off your 1st month's subscription on any plans, valid till the end of the day!
@gaurav_madani05 wait does it actually remember a project's context over time, or does every question start from a cold search? been burned by "knowledge" tools that forget everything the second i close the tab
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.
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.