Launching today
Leni
The world’s most accurate AI for investors
1.2K followers
The world’s most accurate AI for investors
1.2K 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!
Product Hunt
Leni
@curiouskitty Great question. We treat both decision traces and the private context graph as versioned, auditable artifacts, not “free-form memory.”
1) What gets stored (and what doesn’t)
We store evidence + decisions, not guesses: extracted facts (with source pointers), intermediate calculations, and the final outputs/claims.
We also store the reasoning structure as a trace (what was considered, what was ruled out, and the assumptions made), but we don’t blindly promote it into reusable “truth.”
Anything that’s low-confidence, speculative, or user-specific can be tagged as ephemeral (session-scoped) vs. durable (approved to persist).
2) Preventing bad assumptions from becoming institutional memory
Every node/edge in the graph carries provenance + confidence + freshness (where it came from, how sure we are, and when it was last validated).
Nothing becomes “institutional” without a gate: either explicit human approval, or repeated confirmation across independent sources / repeated workflows.
We use contradiction detection and “challenge” steps: if new evidence conflicts with something previously stored, we don’t overwrite silently — we create a fork / flag and force reconciliation.
3) Handling changing definitions (NOI, occupancy, same-store) across teams
Definitions are treated as first-class, versioned objects (basically a “semantic layer”): NOI_v1, NOI_v2, etc., each with its formula, inclusions/exclusions, and scope (fund, asset class, team).
Any metric in the graph is linked to the definition version used at the time. So historical analyses remain reproducible, and you can re-run with a new definition intentionally.
Cross-team alignment is done via mapping + governance: you can declare “Team A NOI” ↔ “Team B NOI” with explicit deltas, rather than forcing a single ambiguous field.
Net: the system behaves more like an audit-grade knowledge + semantic layer with controlled memory persistence that's transparently shown to teams, versioned, and reversible.
Leni
@curiouskitty @arunabh_dastidar Thanks for asking this.
One thing I’d add is that this was a huge product design decision for us: memory can’t behave like a black box in investment work.
Teams need to know what was remembered, where it came from, and whether it is still valid. That’s why we think of context as controlled institutional knowledge.
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!"
Leni
@arunabh_dastidar @devanandb Firstly, love this question, especially the second part. On getting the first enterprise customers: honestly, there was no magic trick. It was hard.
A few tactical things helped us early, though. We leaned on our network, started with people who already trusted us as operators/founders, and were very clear about expectations. We didn’t go in saying “trust AI with million-dollar decisions on day one.” We positioned it more as: let us help with the painful, repetitive work first.
We also made a deliberate choice to pursue a few more respected institutional names early, even though those cycles were harder. The thinking was: if we could earn trust with sophisticated teams first, it would create confidence for everyone else later.
Pixelesq
@arunabh_dastidar @gaurav_madani05 Really appreciate the honesty here!
The part about targeting respected names early even though those cycles are harder goes against most of the advice out there, which is usually just go after whoever converts fastest. But it makes sense, if the sophisticated teams trust you, that bar carries over to everyone else. Smart sequencing!
Leni
@arunabh_dastidar @gaurav_madani05 @devanandb jumping in here too. We started our efforts thinking the hardest part would be convincing people that AI could be useful but most teams already believed that. The harder part (but most valuable feedback) was earning permission to sit close to workflows where numbers get reviewed by partners, lenders, ICs, and clients.
Like everything in life, you have to start smll and build your way up. We had to prove reliability in the boring places first: tie-outs, source checks, repeat reporting, and first-pass analysis where the team could verify quickly.
In business, once the system kept behaving correctly across cycles, you'll be surprised to see how the scope can expand naturally.
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.
Leni
@grover___dev Great question. I think about this every day - Leni’s governance protocol is a set of technical controls that keep outputs accurate, bounded, and auditable in real investment workflows:
Access & action controls: role/permission-based data access plus strict tool allowlisting so the AI can only take approved actions within defined scopes.
Provenance by default: every output carries traceability to the exact source documents/data (and versions) and the extracted fields/mappings used.
Deterministic math: financial calculations run in a controlled execution environment so numbers are reproducible and inspectable, not guessed.
Verification gates: outputs must pass consistency, definition, and reconciliation checks before they ship; if evidence is insufficient or conflicting, the system surfaces the gap instead of improvising.
Definition governance: key metrics and underwriting/reporting conventions are encoded as explicit definitions/rules so the system uses institutional meaning, not generic interpretations.
Closed-loop feedback governance: users correct issues in-line; feedback is structured into error types and updates the context graph + verification layer to prevent repeat errors over time.
Auditability & evaluation: end-to-end logging of inputs, sources, computations, and verification outcomes supports audits and continuous quality measurement.
Leni
@grover___dev this is exactly the layer that gets interesting after source traceability.
A source link shows where a number came from, but why a team accepted the assumption, who reviewed the risk, or what changed between the first memo and the version that went to IC.
The way we think about it is that the trace has to capture more than evidence. It should capture the decision context around the evidence: accepted assumptions, open questions, reviewer notes, definition versions, and the point at which a flagged item moved from “needs review” to “approved for use.”
In underwriting, for example, a market rent assumption might be supported by comps, but the insight relies on why the team chose one comp set over another. That is a part absolutely worth preserving, because it is what people come back to later when a deal changes, a lender asks a question, or the next committee wants to know how the conclusion was formed.
And you can have that answer at your fingertips, it's just one ask away.
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).
Leni
Arunabh @tom_palmer_ux the important thing here is that we don’t think resolution should always mean the system picks one number and moves on. In finance, two conflicting numbers can both be “right” depending on context. One might be from the latest model, another from a signed operating statement. One might be actuals, one might be budget.
So the first job is to make the conflict obvious and explain why it may exist. Then, where there is enough evidence, Leni can recommend which number to use and why. But if the conflict requires judgment, we’d rather surface it rather than hide it behind a confident answer.
Leni
Arunabh @tom_palmer_ux
Thanks Tom! We appreciate it!
This is one of the exact places where finance workflows break if the AI tries to be too clever. We don’t want the system to hide disagreement. Surfacing the conflict is often more valuable than forcing a single answer too early.
Leni
@lakshminath_dondeti - its free to try once you are in you can see all the plans starting at $25 a month.
Leni
@arunabh_dastidar @lakshminath_dondeti Do share your experience with us :)
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
@priya_kushwaha1 if you test it, I’d try the annoying case first: one imperfect PDF, one spreadsheet with formulas, and one question that requires both.
Let us know how that goes!