Provenire ingests the full data room and returns a prioritized list of diligence findings: risks and quantified cost-out, each cited to the exact document, page, and clause, plus a first-draft IC memo. Benchmarked against comparable deals, including the org and headcount efficiency generic document AI can't see.
Provenire reads the data room and tells you which findings actually move the price.
Including cost-out you can't see from the documents alone, each one cited back to the exact page. The shift is from finding everything to judging what's material: the few findings worth a partner's attention, with a number you can put in the model.
And the part everyone used to pay for, reading the data room and pulling the facts out, is becoming a built-in feature of the data room itself.
Extraction used to be the work. It is becoming a checkbox the buyer already pays for.
Virtual data rooms now include built-in AI assistants that summarize and extract across the documents, bundled into the subscription the buyer already holds.
As VDRs expose programmatic interfaces, any AI assistant can plug straight into the data room to summarize and extract, further commoditizing retrieval.
"We read your data room and cite the source" is no longer a product. It's a feature of the platform the deal already runs on.
The hard part is judging which findings are material: which clause actually threatens the deal, and what the upside is worth in dollars.
One engine (finding, benchmark, quantified impact, citation) applied from the first read to the committee memo. The receipts travel with every number.
Point Provenire at the full deal corpus: financials, contracts, the cap table, customer data. Connect securely or upload.
Provenire scores each finding for materiality against comparable deals, and benchmarks the target's org and economics against peers.
A ranked findings list of risks and quantified cost-out, each cited to document, page, and clause, plus a first-draft IC memo.
Assembled from the findings, not generated free-hand. Every line traces to the data room, so edit and sign before it goes to committee.
Finding, benchmark, quantified impact, and citation: carried straight through to the output a partner signs.
Ranked, cited, and benchmarked against comparable deals.
A board-ready first draft, assembled only from the findings. Editable, every line cited.
Projected vs. realized value tracked post-close, shown with confidence ranges.
Extraction is now table stakes; the VDR does it too. Provenire scores each finding against comparable deals and surfaces the few material enough to change the price or the structure, so the team reads "this is the one that matters," not 400 highlighted clauses.
Most tools only flag risk. Provenire quantifies the opportunity too: cost-out and synergy, from org redesign where span of control and layers run heavy against comparables to duplicated functions and procurement overlap. Each is a number you can put in the model, and together they set a defensible valuation range, not another summary to read.
No finding stands without a citation to the specific document, page, and clause. Verify any line in seconds before it goes in front of a partner. Auditability is how Provenire earns trust, not a disclaimer in the footer.
Target runs 8 management layers at an average span of control of 3.1, vs. a comparable-deal median of 5.2. Rightsizing toward peer spans removes ~1.5 layers.
Top 3 customers = 41% of revenue vs. a comparable-deal median of 23%; two contracts include change-of-control consent.
The material findings (quantified cost-out and synergy, plus risk adjustments) roll up into a range you can defend in the room, each input cited to its source. It is not a DCF or LBO model and does not replace one; it is the evidence-backed bracket those models get tested against.
Every input is cited. The range moves only when a finding moves it: adjust or reject any line and the bracket recomputes. Provenire proposes the range; the deal team owns the number that goes in the model.
Extraction is the commodity layer the data room now owns. The judgment layer, materiality and benchmarking against comparable deals, is the part that still has to be built.
Extraction is now the commodity layer. Materiality and benchmarking are the judgment layer.
One engine: finding, benchmark, quantified impact, citation. Here is what runs today and what comes next. Roadmap items are forthcoming, not available today, and listed plainly so you always know where the line is.
Live today
Risks and opportunities scored against comparable deals, each cited to document, page, and clause.
Span of control, management layers, headcount-to-revenue, and cost-per-FTE against peer deals, the view generic document AI cannot reach.
A board-ready draft assembled only from the findings, editable, with every line cited back to the data room.
On the roadmap
Quality-of-earnings and financial-diligence findings, cited to the workpapers, built to inform the analysis, never to sign off on it.
Customer, pricing, and compensation benchmarks against comparable deals, extending the org view across the full operating model.
Findings organized to inform the letter of intent. Provenire informs the LOI; it never drafts or negotiates it.
Projected vs. realized value tracked after close, shown with confidence ranges, so the committee sees what landed against what was modeled.
The boundary is part of the product. Provenire sharpens the judgment behind the decision; it does not take the actions that require a signature, a mandate, or a model of record.
Provenire proposes the number and shows its work. A human signs it.
Your VDR answers "what's in here?" Provenire answers "what's material, and what's it worth?" A VDR can find every change-of-control clause; it can't tell you which one actually threatens the deal, because it has no benchmark of comparable deals, and it never sees org structure to quantify cost-out. Extraction is the commodity layer the VDR owns; materiality and org benchmarking are the judgment layer it structurally can't reach. We sit on top of the data room, not against it.
Built around citations, not generation: every finding links to the source document, page, and clause, so you can verify any line in seconds. A faster, benchmarked first pass you sign off on, not an autopilot.
Today it runs locally: documents are parsed and stored on your own machine, scoped per deal, and the only data leaving it is the retrieved chunks sent to the model for a given call. Production controls (encryption at rest, authentication, access control, audit logging) are not yet implemented, and the repository says so explicitly. Treat it as a prototype, not a system of record.
They do, and a first pass costs days of associate time, much of it spent reading to find the few things that matter. Provenire does that first pass fast so the team judges instead of hunts, and adds org and cost benchmarking against comparables that headcount has no dataset to run.
Provenire is an independently built, functioning implementation of the pipeline described above: it ingests real documents, retrieves per category, produces grounded findings, enforces the review gate, and assembles a memo. It has no customers, no design partners, and no traction to report, and nothing on this page claims otherwise.
The source is public, including the analysis prompts and the code that enforces citation grounding. Read the code on GitHub
I built Provenire end to end: document parsing and retrieval, the grounded-findings pipeline, the review gate, and the memo assembly and export.
Tell us about a recent deal and we'll show you Provenire's findings and benchmark output on a comparable engagement. No prep required from your team.
A member of our team will reach out within one business day to schedule your walkthrough.