Document intelligence · Contract analysis AI
Your contract review is slow, expensive, and still lets clauses slip through
Banao builds contract analysis AI that reads every agreement you receive, classifies the clauses that matter, surfaces the obligations and deadlines you must act on, and flags the terms your legal team should examine — before a human opens the file.
The system is not a search box with an LLM attached. It is a full extraction and risk pipeline: trained on your contract types, integrated with your CLM or ERP, and built with a confidence gate so your reviewers only touch the cases the model is genuinely unsure about.
Enterprise technology company (anonymized)— Vendor agreement obligations and auto-renewal clauses extracted and routed to the responsible owner before the review queue was opened.
What a Banao contract analysis pipeline covers
Contract analysis AI is not one model reading a PDF. It is classification, structured extraction, risk scoring, obligation tracking, and integration with the systems your legal and procurement teams already use.
Contract classification and segmentation
Identifying whether a document is an NDA, MSA, SOW, lease, or employment agreement — and splitting a mixed upload into its constituent contracts before any field is read.
Clause extraction and structured mapping
Pulling parties, governing law, payment terms, IP ownership, termination rights, limitation of liability, and jurisdiction into structured fields your downstream systems can act on.
Obligation and deadline identification
Finding every date-bound obligation — renewal windows, notice periods, delivery milestones, SLA thresholds — and posting them to a calendar or ticketing system so nothing lapses by silence.
Risk clause detection and scoring
Flagging unusual liability caps, one-sided indemnification, missing IP assignment, uncapped penalties, and clauses that deviate from your standard playbook — ranked by the severity your legal team defines.
Comparison against your standard terms
Diffing an inbound agreement against your preferred positions clause by clause, so your team opens the redline already knowing which terms are acceptable and which need negotiation.
Multi-document cross-reference
Checking an SOW's scope against the parent MSA, or a renewal against the original agreement — catching the inconsistencies that appear only when two documents are read together.
Exception queue and reviewer routing
Passing only the cases below your confidence threshold or above your risk score to a human reviewer, with the flagged clauses pre-highlighted so the review is minutes, not hours.
CLM and ERP integration
Writing extracted fields, risk scores, and obligation dates directly into your contract lifecycle management platform, ERP, or ticketing system — so the insight reaches the people who need it.
What separates production contract AI from a search tool with an LLM on top
Most contract AI pilots stall at the demo: you ask a good question and the model answers well. The problem appears when you run 300 vendor agreements through it, half scanned at poor resolution, with non-standard clause numbering and two different governing-law jurisdictions. That is the production case, and it is a different engineering problem from a well-prompted chatbot.
Production contract analysis requires a classification layer that handles document variety before extraction starts, a grounding layer that keeps the model reading your contract rather than inventing a standard clause, a confidence threshold that decides what a human ever sees, and an audit trail that lets your legal team explain every flag to a counterparty. Banao builds the whole pipeline, not the model layer in isolation.
Layout-invariant extraction
Contracts arrive in dozens of templates. The extraction layer reads the clause regardless of whether it sits in section 4.2(b) or buried in a schedule — without a hand-built template per counterparty.
Grounded against your playbook
The risk flags your legal team cares about are not the same as a generic legal AI tool's flags. We train the risk layer on your preferred positions, your historical redlines, and the clause deviations that have actually caused problems for your business.
Confidence gates, not blind automation
Every extraction carries a confidence score. Clauses the model is uncertain about go to a reviewer. You set the threshold — broader coverage with more review, or high precision with less. Either way, the system does not silently guess.
Contract volume is not the only trigger — coverage and speed are
Teams with a modest contract volume still lose value when review takes two weeks, when auto-renewals slip through, or when a reviewer misses a liability cap on a contract read under deadline pressure. Contract analysis AI is as valuable at 50 agreements a month as at 5,000 — the question is what a missed clause costs, not how many documents you process.
We build for the coverage case and the speed case together: every agreement reviewed within hours of receipt, obligations posted to the responsible owner the same day, and your legal team's attention directed to the small set of agreements that actually need it.
We apply the same document pipeline to our own vendor agreements
Banao processes its own vendor contracts and partnership agreements through the document intelligence stack we build for clients. New agreements are classified, key obligations extracted, and auto-renewal dates posted to the responsible owner before any person opens the file.
Running the pipeline on our own operations means we encounter the edge cases — the oddly formatted PDF, the clause buried in a schedule, the multi-jurisdiction agreement — on our contracts before we encounter them on yours. That experience informs how we scope and build yours.
- Internal document pipelineVendor agreement obligations and renewal windows extracted and routed automatically across Banao's ~300-person operation.
When contract analysis AI is not the right first step
We will tell you before you commit a budget to a contract AI build whether the problem warrants one:
- Volume is genuinely low and clauses are standard: if you review a dozen similar agreements a year, a well-maintained checklist and a trained paralegal is cheaper and more auditable than a model.
- The contract type is too specialized: some highly bespoke agreement types — complex derivatives, novel project finance structures — have too few training examples for the model to generalize reliably, and a confident-but-wrong flag is worse than no flag.
- Your CLM vendor already covers it: some mature CLM platforms include extraction and basic risk scoring. We will tell you if your existing software handles your use case before we propose a custom build.
- The bottleneck is not review, it is negotiation: if agreements move slowly because counterparties are slow to respond, faster review does not fix the delay. We scope the actual bottleneck before we size a build.
How we start — scope the pipeline before we build it
A contract AI build scoped off a brief is a build scoped off assumptions. We test your real documents first.
- AI Discovery Sprint2 weeks · fixed price
We run your actual contract sample through extraction and risk scoring, map the clause types and edge cases, and hand back a pipeline design, an accuracy baseline, and ROI maths — yours to keep. If you proceed, the Sprint fee is credited against the build.
- Build
We develop the classification layer, extraction, risk scoring, confidence gates, reviewer queue, and the integrations to your CLM, ERP, or ticketing system — accuracy and integration are deliverables, not afterthoughts.
- Production and continuous improvement
We ship with full extraction audit trails, monitor accuracy on live contracts, and retrain on the cases your reviewers correct — so the pipeline gets more accurate the longer it runs.
Frequently asked questions
What does "contract analysis AI" actually do?
It classifies your agreement type, extracts structured fields — parties, payment terms, obligations, dates, risk clauses — compares the terms against your standard positions, and routes exceptions to a human reviewer. The result is a structured record of each agreement and a list of the clauses that need attention, posted to your systems automatically.
Can it handle our non-standard contract formats?
Yes. The extraction layer reads clause content, not fixed positions, so it handles agreements that arrive in different templates, numbering schemes, and layouts. We test it on a sample of your actual contracts in the Discovery Sprint before we quote the build.
What contract types can you analyze?
NDAs, master service agreements, statements of work, employment agreements, leases, vendor agreements, purchase orders, and most standard commercial contracts. Highly specialized agreement types — complex derivatives, project finance — are scoped case by case because the training data requirement is higher.
How do you handle scanned contracts and poor-quality PDFs?
We include an OCR and image-normalization stage before extraction, tuned for scanned documents and phone photographs. Quality affects confidence scores — low-quality pages surface with lower confidence and route to the human queue automatically rather than producing a silent wrong answer.
How accurate is the extraction, and how do you measure it?
We establish an accuracy baseline on your contract sample during the Discovery Sprint, report field-level precision and recall, and set the confidence threshold that determines what goes to a reviewer. You see the accuracy number before you decide to proceed with the build.
Does it integrate with our existing CLM or ERP?
Yes. We write extracted fields, obligation dates, and risk scores to your contract lifecycle management platform, ERP, or ticketing system through their APIs. If your CLM has a standard connector, we use it. If not, we build the integration as part of the pipeline.
What happens when the model is uncertain?
Extractions below your confidence threshold go to a reviewer queue with the relevant clause pre-highlighted. The model does not guess silently — uncertainty surfaces explicitly so a person can confirm or correct it, and those corrections feed the next retraining cycle.
How long does a contract analysis AI build take?
A common path is a 2-week Discovery Sprint to scope and baseline, then an 8–12 week build covering classification, extraction, risk scoring, reviewer tooling, and integrations, then a staged rollout. Banao's delivery bench in Bangalore and Chandigarh means the build starts within weeks of the Sprint.
Bring your messiest contract type and your current review time
In 45 minutes we will tell you what a production contract analysis pipeline would achieve on your real agreements — and what building it would take.
Book a 45-min scoping call