Needl.ai reads the documents, benchmarks the financials, flags red flags across ownership, margins, and concentration, and compiles a full, cited IC memo in hours. Causal AI simulates every assumption.
How a deal moves through Needl.ai, start to finish.
Contracts, financial models, partnership agreements, employee docs, and investor decks go in. Needl.ai builds a connected view of the deal in minutes.
In about 24 minutes, Needl.ai delivers a first-pass diligence assessment and a go / no-go checklist across the business.
Needl.ai checks the financial model against the underlying data, catching inconsistencies, like a projected $20M revenue against a realistic $2M.
Commercial drivers, partnership dependencies, legal and covenant exposure, and IP risks are reviewed automatically and consolidated into one view.
A fully cited investment committee memo, with thesis, risks, financial validation, and recommendation, in your preferred format.
Analysts dig deeper with follow-up questions, tracing every conclusion back to the supporting evidence.
From a fast first read to a full memo, with causal foresight on top.
Needl.ai reads the documents, benchmarks the financials, and flags red flags across ownership, margins, and concentration.
3 days to half an hourContracts, IP, call synthesis, and financial modelling across scenarios, all compiled into a complete IC memo.
Months to daysScenario simulation and risk surfacing that test how each assumption moves the outcome, before you commit capital.
Forward-lookingReal diligence, run on Needl.ai.
"81 files became a connected view in minutes. A first-pass assessment landed in 24 minutes, and the platform caught a ~$20M revenue projection against a realistic ~$2M, plus $60-75K in external advisory and legal costs avoided."
"Leverage, interest coverage, free cash flow, asset coverage, and covenant health surfaced in a single view, with what-if scenarios and a cited IC memo in under 30 minutes."
The sources it reads, and the output you get.
Workflows teams pair with diligence.
See it run on your own data, in your environment.