Professional Services · Contract analysis
The clause that hurt you was buried in a contract someone assumed was standard
Banao builds contract analysis AI that reads every clause against your own playbook — flagging deviations, extracting obligations, and handing your lawyers an exception list instead of a full document to mark up from scratch.
The model trains on your own signed contracts and your own standard form, not a generic corpus. It runs inside your document environment, and every flag links back to the source clause so a qualified fee-earner can sign the output rather than re-read the original.
A mid-market M&A advisory team— deviation flagging and obligation extraction deployed on live deal documents before signing.
The first call is free · 45 minutes · no obligation
What we build
What a Banao contract analysis deployment covers
Contract AI that only extracts clauses is half the job. We wire flagging, obligation tracking, and renewal alerting together so a single review cycle replaces the three separate passes your team currently runs.
Clause-by-clause deviation flagging
Every clause is checked against your own standard form and negotiating playbook. Accepted language passes silently; deviations surface in a ranked exception list so your lawyer reviews the delta, not the document.
Obligation extraction and deadline tracking
Payment terms, notice periods, indemnity caps, reporting dates, and renewal windows are pulled from every contract and written into a tracked obligation register — no more missed deadlines buried in a schedule.
Portfolio-wide contract inventory
Bulk ingestion of your existing contract archive — PDF, Word, and DMS-native — produces a searchable inventory of parties, terms, governing law, and expiry dates across your entire portfolio.
Playbook gap detection
The model flags clauses your playbook does not address yet — a second-order output that shows you where your standard positions have silent gaps before the next counterparty exploits one.
Redline generation from your own standard form
Where a counterparty draft deviates, the system generates a first-pass redline substituting your preferred positions. Your lawyer edits the redline rather than authoring one from scratch.
Renewal and expiry alerting
Tracked obligations and term dates feed into a calendar of upcoming renewals, notice deadlines, and automatic-extension windows — surfaced to the responsible fee-earner before the window closes.
Receipts
Where this is running now
Metrics shown dotted (··) are being finalised in our case-study pack — published only when verified. Client names are withheld where confidentiality terms require it.
Deviation flagging on live deal documents before signing
Deal teams were reviewing every counterparty draft in full on short timelines. Banao deployed a deviation model trained on the firm's own playbook and historical redlines. Lawyers now review an exception list rather than the whole document, and every flag traces to the source clause so sign-off is auditable.
Lease portfolio mapped from hundreds of contracts in weeks
Hundreds of lease agreements in PDFs, some scanned, sat in a shared drive with no searchable inventory of break options, rent-review dates, or service-charge caps. Banao bulk-ingested the archive, extracted key terms into a structured register, and surfaced upcoming renewal windows — several of which were within days of expiry.
Dogfooding
We run our own firm on the same AI before you do
Banao is a ~300-person professional-services firm that wins work on proposals, negotiates its own vendor and partnership agreements, and operates across five jurisdictions. Contract analysis AI runs on our own agreements before it reaches a client.
InterviewGod screens our engineering hires. Vikaas runs our demand-gen and proposal pipeline. When AI has to survive a live commercial operation — not a controlled pilot — the edge cases surface early. That is the standard we hold our client deployments to.
Screens Banao's own engineering hires every week.
Runs Banao's own demand-gen and proposal pipeline end to end.
The honest version
When contract analysis AI is the wrong call
Not every contract problem is a volume problem. We will tell you if AI is not the answer — it is why managing partners take our second meeting.
- Low volume: if your team reviews a handful of contracts a month, a skilled paralegal is cheaper than a trained pipeline. We will say so in the Discovery Sprint.
- Highly bespoke commercial terms: where every deal is genuinely one-of-a-kind with no repeating playbook, there is no deviation pattern for a model to learn from — and we will tell you that before you spend on training data.
- Confidentiality walls: if deal documents cannot leave a tightly governed environment, week one is deployment architecture inside your walls — sometimes the honest answer is not yet, pending an approved environment.
How we start
How we start — prove it on your own documents first
We do not quote a contract AI build off a brochure. We look at a sample of your real agreements first — how consistent your standard form is, how many deviation patterns exist, and where the extraction breaks down.
- 01
AI Discovery Sprint
2 weeks · fixed price
We ingest a sample of your real contracts, run a baseline extraction and deviation pass against your playbook, and hand back an accuracy estimate, a gap analysis, and a ranked ROI model — yours to keep either way. If you proceed, the Sprint cost is credited against the build.
- 02
Build
Playbook ingestion, model training on your signed contracts, DMS integration, and a deviation dashboard. Obligation tracking and renewal alerting are included in the build deliverable.
- 03
Production & ongoing learning
Fee-earner overrides and corrections feed back into the model, so accuracy improves as it sees more of your negotiating history. Audit trails for every flag are retained for privilege and compliance review.
FAQ
Frequently asked questions
How does the model learn our playbook?
We ingest your standard form, your preferred positions, and a sample of your historically signed contracts. The model learns what your firm accepts — not an industry average — so deviation flags reflect your actual positions, not a generic benchmark.
Can it process redline Word documents and scanned PDFs?
Yes. The pipeline handles native Word, PDF, and scanned documents with OCR. Redlines in Word are stripped to clean text before extraction; tracked changes are flagged separately so the model sees both the original and proposed language.
Where does our contract data sit during analysis?
Inside a boundary you approve — your cloud tenant or an isolated environment we provision and hand over to you. We do not route client documents through shared infrastructure, and access is scoped to named users at your firm. Deployment architecture is agreed in the first week, before any document is touched.
What happens when a clause has no match in our playbook?
The model flags it as a gap — a clause your standard positions do not currently address — rather than silently passing it. Playbook gaps are a second-order output of the Sprint and one of the more commercially useful things we surface before a build starts.
How many historical contracts do you need to start?
Enough to cover your real negotiating patterns — typically a few dozen to a few hundred signed agreements, depending on how varied your deal types are. The Discovery Sprint establishes whether you have enough signal or whether we train from your playbook alone and accumulate history over time.
Get started
Put your most complex contract in front of our baseline model
Bring a redacted sample from a live deal or your hardest contract type. In 45 minutes we will tell you whether AI contract analysis makes economic sense for your firm and what a build would take.
Book a Discovery Sprint