Professional Services · Document review automation
A 200,000-page discovery set should not take three associate-weeks
Banao builds AI document review pipelines for law firms, accounting practices, and advisory firms — ranking documents by relevance, flagging privilege, and extracting key facts from discovery sets, data rooms, and regulatory submissions.
Every reviewed document stays linked to its source page and reviewer decision. The system runs inside your own environment, on your matter data, integrated with the litigation support and DMS tools your team already uses.
A mid-market disputes firm— first-pass review on live discovery, every privilege flag linked to the source page.
The first call is free · 45 minutes · no obligation
What we build
What a Banao document review deployment covers
A document review pipeline is more than a model reading PDFs. It is the ranking logic, the privilege workflow, the integration with your existing tools, and the audit trail your partners need to sign off on the output.
Relevance ranking and priority triage
The model reads your entire document set and ranks by relevance to the matter — putting the 5% that matters on top, not buried inside a pile of vendor invoices and calendar entries.
Privilege detection and log
Attorney-client and work-product flags are identified and held for senior review, with every call linked to the passage that triggered it — so privilege review is a decision, not a re-read of the whole set.
Redaction marking
Personally identifiable information, court-ordered redaction classes, and sensitive commercial terms are flagged in the document and staged for human approval before any production copy goes out.
Key-fact and timeline extraction
Named parties, dates, financial figures, and asserted facts are pulled into a structured timeline and entity map — giving fee-earners a head start on the chronology rather than building it from scratch.
Regulatory and compliance document screening
For regulatory submissions, audit packs, and disclosure bundles, the model checks completeness against your checklist and flags gaps or contradictions before the document leaves the firm.
Continuous reviewer calibration
When a senior reviewer overrides the model's call, that decision feeds back. Review accuracy improves matter by matter, and the calibration log becomes an internal quality record.
Receipts
Deployed on live matter work
Metrics shown dotted (··) are being finalised in our case-study metrics pack. The deployments are live; we will not publish a number before it is verified.
First-pass discovery review replaced associate triage
A litigation practice was absorbing associate-weeks on first-level review of 150,000-document discovery sets. Banao deployed a relevance-ranking and privilege-detection pipeline over the firm's matter data — running inside the firm's own cloud environment — so senior reviewers work from the ranked set and exception queue rather than the full pile.
M&A due diligence data room reviewed at deal speed
Due diligence data rooms with 40,000 documents were arriving the same week as deal kick-off. Banao built a pipeline that ingests the data room, ranks documents by deal-team interest, and surfaces a structured key-fact and obligation extract — so the deal team starts analysis on day one instead of day eight.
Dogfooding
We run our own firm's document work on the same AI
Banao is a ~300-person engineering services company that wins work on proposals, manages client contracts, and handles a steady flow of supplier and partner agreements. The document-review and extraction capabilities on this page run against our own commercial and procurement documents before they reach a client matter.
InterviewGod screens our own hires. Vikaas runs our own demand-gen and proposal pipeline. We are not selling document AI from the outside — we use it in-house, which means the failure modes arrive on our own desk first.
Screens Banao's own engineering hires every week.
Runs Banao's own demand-gen and proposal pipeline end to end.
The honest version
When document review AI is the wrong tool
Document AI is not always the right answer. We will tell you before you buy one:
- Low document volume: if a matter averages under 5,000 documents, a single associate with a good search query is faster and cheaper than a trained pipeline. We will say so.
- Unstable review criteria: if what counts as relevant is contested between your fee-earners, the model inherits the disagreement. Getting alignment on the review standard comes before the build, not after.
- Hard privilege complexity: AI flags likely privilege; it does not give the legal opinion. Every flag still needs a qualified reviewer. If you are expecting a hands-off privilege log, we will correct that expectation in week one.
How we start
How we start — fixed price, no commitment beyond the Sprint
You have a live matter and a document set that needs reviewing. Let us look at it before quoting a system.
- 01
AI Discovery Sprint
2 weeks · fixed price
We take a sample of your real documents — redacted if needed — run them through a baseline model, and hand back accuracy numbers, a privilege-detection assessment, and an integration map against your DMS and litigation-support tools. Yours to keep whether you build or not. If you proceed, the Sprint cost is credited against the build.
- 02
Build
Data and security boundary first, then the model. We train on your matter types and review criteria, integrate with your existing DMS, litigation support, and case-management tools, and build the privilege queue and audit trail your partners need.
- 03
Production & adoption
Roll-out with senior-reviewer sign-off built into the workflow. Change management is part of the delivery — associates and paralegals need to trust the ranked output, not work around it.
FAQ
Frequently asked questions
How do you handle attorney-client privilege?
The model flags likely privilege — passages involving counsel, legal advice, and work-product markers — and routes them to a senior-reviewer queue. Every flag is linked to the source passage and the rule that triggered it. No document is marked non-privileged by the AI; a qualified reviewer makes that call.
What document types can the pipeline process?
PDFs, Word documents, emails and email attachments, scanned documents with OCR, Excel files, and structured XML. Handwritten documents go through an OCR stage first; accuracy on handwriting depends on scan quality. The Discovery Sprint establishes what is workable for your matter.
Does it integrate with our existing DMS and litigation support tools?
Yes — integration with your DMS, litigation support platforms, and case-management tools is part of the build, not a later phase. The review queue surfaces inside the tools your team already uses, with actions writing back to the same system of record.
Where does our matter data go?
It stays inside a boundary you approve — your own cloud tenant or an isolated environment we stand up inside your infrastructure. We do not train shared models on your matter data, and the deployment architecture is agreed and documented before any document is ingested.
How do we measure accuracy against human reviewers?
The Discovery Sprint includes a baseline accuracy assessment on a sample of real documents, scored against your own senior reviewers' decisions. That gives you a calibrated accuracy number — not a vendor benchmark — before you commit to a build.
Get started
Let us run your hardest document set through a baseline
Bring a sample from a live matter — redacted if needed. In 45 minutes we will tell you whether AI document review makes economic sense for your firm and what a baseline pipeline would cost to build.
Book a Discovery Sprint