For M&A counsel and sell-side advisors
Tie out every figure to its source, and prove it again years after close.
doloop pulls the figures and checks the clauses in the deal's documents, and it works outside the model's loop, a separate deterministic process that reports only what the pages can prove. Every number points at the exact page and box it came from. When the room can't prove an answer, the cell stays empty. Same documents in, same answer out, two years from now in front of a regulator or opposing counsel.
No model on the answers you sign off on · no per-answer variance · a trail an indemnity dispute can stand on
The problem you already feel
The numbers you must trust are the ones nobody has time to check.
A data room now holds more pages than any team can read end to end. Financials, contracts, cap tables, disclosure schedules: the documents have outrun the people checking them. The common fix is an AI assistant that answers with a citation. But a citation only tells you where an answer claims to come from. It doesn't tell you the answer will be the same on the next run, that the number was read correctly, or that you can defend it in a dispute. An AI that won't repeat its own answer can't be the thing you tie out against.
What doloop does differently
It reads the room four ways. Run it twice on the same corpus and you get the same answer.
If it can't be proven from the documents, it says so; it doesn't invent an answer. And the answers you sign off on come from parsing and rules, not from a model writing the number.
Find it
"Where does the room address change-of-control?"
Points you to the exact document and page, every time, or refuses when the room doesn't cover it.
Check the clauses
"Is this representation identical everywhere it appears?"
Shows whether a clause is the same across every document, and the exact word where it drifted. A match or an exact diff. The score is the position, not a probability.
Pull the numbers
"Give me this schedule, and prove each figure."
Extracts every cell, and click any value to see the source page render with that word boxed. You verify it on screen before you ever export it. No separate reconciliation pass.
Benchmarked on 106,150 cells across 75 synthetic financial documents (trial balances, payroll registers, general ledgers, margin summaries, and bank statements), each processed 3 times: zero variance, identical on every rerun. (Whether a cell is ever silently missed is the one axis we're still measuring; see below.)Know the document
"What is this, and what are its parts?"
Recognizes a document's layout and labels its sections (header, line items, totals, signatures), so the right answer comes from the right place. Learned once from the first document, then reused as a hard rule on every later one of the same shape.
Live now. See it work on a real statement: the layout is learned on the first page and reused at zero cost on the next, and a region you exclude is skipped on every future document of that shape.
The proof
Where the clause changed
The clause reads the same to a tired eye at 2 a.m. It isn't, and that single word is what a dispute turns on.
Same input, same result, every run. A finding is reproducible two years after close, in front of whoever is asking. That reproducibility is the difference between a citation and evidence.
Two kinds of answer, and the output says which.
When it cannot locate a value, the cell is empty.
The answer you can sign off on
Exact, repeatable, traced to source. Use it for tie-out, disclosure schedules, and anything that has to hold up.
The answer to think with
When the room can't prove something and you still want a view, you can ask for an unverified draft answer, clearly marked. Use it to explore. Sign-off still needs a human.
That line, proven on one side and flagged on the other, is the whole point. A citation-based assistant gives you only the second kind, dressed as the first.
Why it compounds
It gets sharper with every deal.
When your team corrects something, or confirms a draft answer was right, that judgment is kept and applied next time. The questions the tool answers with certainty grow deal over deal, while everything it reports stays traceable to its source. Every correction your team makes speeds the next deal. The record stays inside your firm.
Provenance, consistency, and provable answers across the room, the same way every time.
An exception, a house convention, a confirmed draft answer. The judgment, captured once.
Recorded and applied to every deal after. It does not leave your firm.
See it run
See it run, live.
The extraction reader, running live on a real bank statement: every value pulled into clean columns and checked back against the page (nothing invented), each row labelled, and the layout learned so the next statement of the same shape is recognised for free. Drag to exclude a region and the rule sticks for every future document. Nothing leaves the machine that produced it.
It runs on the deal's own documents, so the answers come from the filings. Want it on yours? That's the design-partner conversation below.
What we don't claim
The honest fence.
We'd rather you trust the boundary than oversell past it. Three things we're explicit about:
- On the financial tables, we stand behind every extracted value and an identical rerun. What we're still measuring: whether a cell can ever be skipped without flagging it.
- The "know the document" step now ships: it learns a document's layout, labels its sections, and reuses both on the next document of the same shape (try it in the sample report). What it recognizes is the layout fingerprint, not yet a deep semantic document type. And the "find it" depth on your deal depends on setting it up on your documents first, a short attended step we do with you.
- We haven't yet proven this closes deals faster or catches more on a live deal. That's exactly what we're establishing with a design partner, on real diligence.
Where it fits first
Financial tie-out and disclosure-schedule preparation.
The work where a wrong number carries real liability, and a junior team spends most of its hours cross-checking figures against source. doloop does that checking at the source, the first time, and produces the trail you'd have built by hand. The result you keep is one you can stand behind long after the deal closes.
What does it cost to run a business process
on data you can’t fully trust?
Most teams count the extraction cost. The real number is what happens downstream - delayed decisions, wrong values, and liability you can’t see until it surfaces. Answer three questions.
People check, re-enter, or QA this data before it’s used
Human review, verification cycles, correction loops - whether it’s one analyst or a team.
Pipeline cost
Revenue, approvals, or decisions wait on this data
Quotes, underwriting, diligence, reporting - the process stalls until the data is ready.
Opportunity cost
A wrong value here has a regulatory, legal, or audit consequence
Filings, contracts, systems of record - where an error is not just embarrassing, it’s a liability.
Risk cost
Design partners
Verify your next data room with us.
We're taking on a small number of design partners: M&A counsel and sell-side advisors who run a real verification pass and want it deterministic, defensible, and compounding. We set it up on your documents and run it alongside your team on a live deal, under NDA.