"Raftlabs is not only our partner, but an integral part of our team and startup!"
Tedo S.
CCO and CTO at Fore Coffee
AI Contract Review Software Services
AI contract review software that finds the exception first.
When routine agreements are slowing sales or procurement, the answer is not a generic legal chatbot. We build AI contract review software around your playbook, preferred language, systems, and security requirements. It prepares the first pass. Your attorneys see the source, decide the risk, and approve every redline.
Bring the problem, the current workflow, or the existing code. We reply with a practical next step within one business day.
Trusted by
The brief
Start with what is not working.
Good software decisions begin with the constraint, not a list of features or a preferred technology.
01
Is the contract queue delaying routine sales or procurement work because attorneys must reread the same accepted positions?
02
Have polished demos failed to show misses, false flags, or what happens with heavily negotiated third-party paper?
03
Do your contracts, playbook, and reviewer decisions need to stay inside an approved data and access model?
Plain answer
AI contract review software extracts clauses, compares them with a legal playbook, and prepares findings or redlines for human approval. Custom development is most useful when an established legal team has repeatable contract volume but off-the-shelf tools do not fit its playbook, workflow, integrations, or data boundaries.
What to remember
- Useful review prepares a defensible first pass: source language, playbook position, deviation, fallback, and reason for escalation. The reviewer decides.
- Record the baseline first: monthly volume, review time, queue age, escalation rate, outside-counsel spend.
- A pilot must perform on your contracts under your risk rules, with representative agreements and an unseen test set.
- Discovery should be able to recommend a licence, an integration, a narrow custom workflow, or no project at all.
- Party-side awareness comes first: the system must know which side it is reviewing for and flag changes that benefit the other party.
The queue is routine. The risk is not.
A familiar agreement comes back from a customer or supplier. Ninety pages may be acceptable. One liability carve-out, missing security obligation, or changed renewal provision is not. The attorney still reads the whole document because the cost of the one miss can outweigh the time saved on everything else.
Useful AI contract review does not pretend to make that legal decision. It prepares a defensible first pass: the exact source language, the playbook position, the deviation, the permitted fallback, and the reason for escalation. The reviewer decides what this deal can accept.
Start with the operating problem, not the model
The business case is usually visible outside legal. Sales waits for a customer agreement. Procurement waits for vendor paper. A commercial lawyer spends the morning checking positions the team has accepted many times before. Outside counsel absorbs overflow while the backlog grows again next month.
Before choosing software, record the baseline for the contract type in scope: monthly volume, median and high-end review time, queue age, escalation rate, outside-counsel spend, and rework after the first redline. Those numbers answer two questions that an AI demo cannot: whether the problem is large enough to solve, and whether the new workflow improved it.
Custom contract review works when your legal positions are specific and repeated.
A product licence is usually the better answer when an established tool already fits the contract, workflow, and data boundary.
A fit
One or more repeatable contract types create a measurable queue, delay, or outside-counsel cost.
Legal can name approved positions, acceptable fallbacks, and escalation rules for the clauses in scope.
The review must connect with your Word, CLM, document store, matter data, or approval route.
Security, hosting, model, or retention requirements rule out a generic public AI workflow.
Not a fit
Most agreements are bespoke and every material clause requires fresh judgment.
Your team has no settled playbook, owner, or representative contract set yet.
An existing product meets the need without expensive integration or process workarounds.
The real bottleneck is intake, business approvals, signature, or renewal rather than language review.
The first decision is not what to build.
It is whether software is the right answer at all. We use four possible recommendations before discussing a development scope.
- 01
Buy
License a contract review product when its playbook coverage and security posture fit.
- 02
Repair
Keep the manual review when volumes are low and the backlog stays manageable. Start with evidence: the baseline, volume, review time, queue age, outside-counsel spend.
- 03
Build
Own the workflow when representative agreements, your risk rules, and your reviewers define the bar. Start with one contract type and its baseline.
- 04
Wait
Do not build for a contract type whose positions are still being negotiated internally.
Not sure which path is yours? Bring it to a 30-minute call. Leave with the answer in writing.
Buy, configure, or build: the first decision
- Buy
- Choose an established review product when its clause library, playbook controls, Word or CLM integration, security posture, and volume pricing fit. The product will usually reach users faster than custom software.
- Configure and integrate
- Use a product's review engine but connect it to your intake, permissions, contract record, and reporting. This often solves the operating problem without owning a new AI stack.
- Build a focused workflow
- Custom development earns its cost when proprietary playbook logic, uncommon documents, a strict data boundary, or deep system integration creates persistent product workarounds.
- Do not automate yet
- If positions vary by reviewer, the corpus is not representative, or nobody owns the playbook, resolve that process debt first. AI will reproduce ambiguity, not remove it.
We make this fit decision before proposing a build. A useful discovery should be able to recommend a licence, an integration, a narrow custom workflow, or no project at all.
Review scope
What a focused first system can cover
Clause and defined-term extraction
Each finding includes its clause type, exact source text, page or section reference, and confidence. Parties, dates, values, notice periods, defined terms, and cross-references can be extracted alongside the clause set. Uncertain or structurally unfamiliar results remain in the review queue.
Playbook comparison
The system compares the draft with your approved position, accepted variants, fallback language, and escalation rules. It should explain which rule fired and show the underlying contract text, not hide the reasoning inside a risk label.
Missing provisions and draft redlines
Required clauses that do not appear are flagged. When legal has supplied an approved fallback, the system can prepare a suggested redline and negotiation note. A reviewer accepts, changes, or rejects it before anything leaves the organisation.
A review queue inside the existing workflow
Contracts can enter from Word, CLM, document storage, email intake, or a matter workflow. Findings, reviewer decisions, and approved versions return to the system of record so the AI step does not create another inbox or an orphaned report.
What should an AI contract review pilot prove?
A demo shows whether the interface can produce a plausible answer. A pilot has to show whether the workflow performs on your contracts, under your risk rules, with your reviewers. We use representative historical agreements, hold back an unseen test set, and include the ugly documents: scanned pages, old vendor paper, negotiated NDAs, duplicate clauses, broken cross-references, inconsistent defined terms, side letters, and missing schedules.
- False negatives by risk
- Which deviations or missing clauses did the system fail to surface? A missed high-risk provision matters more than several correct standard clauses.
- False positives and reviewer burden
- How many harmless provisions did it flag, and how much time did reviewers spend dismissing them? Excess alerts can erase the promised time saving.
- Source and explanation
- Can the reviewer reach the exact provision, playbook rule, defined term, and fallback behind a finding without searching the document again?
- Document behaviour
- Does it handle tables, tracked changes, clause numbering, attachments, cross-references, repeated language, and clauses located outside their usual section?
- Redline quality
- Does the suggested language preserve formatting and land in the correct place? Is the negotiation note supported by an approved legal position rather than generated preference?
- Net reviewer time
- Measure the complete path from opening the contract to an approved first pass, including corrections. A faster model response is irrelevant if the lawyer spends longer verifying it.
There is no responsible universal accuracy target for every clause and agreement. We set acceptance thresholds by clause, error cost, and required reviewer response. High-risk findings can require stricter evidence and mandatory review. Lower-confidence results can be routed rather than guessed. The legal owner decides the threshold; the evaluation shows the trade-off.
Relevant delivery evidence
- internal validation measure reached in an adjacent AI document system
- Near 99%
- Sample and test method are not public; not contract review
- documents processed in the recorded first four weeks
- 1,610
- Receipt validation for a supermarket loyalty campaign
- to deliver that production AI document workflow
- 12 weeks
- From extraction and validation through operational rollout
These are not contract-review accuracy claims. We have not published a legal contract-review deployment. The adjacent AI document project shows the engineering discipline that transfers: extract structured fields from messy source documents, validate against known answers, route uncertain results, integrate the production workflow, and monitor it under live volume. Legal clauses still require their own labelled set and attorney-defined acceptance criteria.
Contract review AI and CLM solve different parts of the contract problem
AI contract review vs contract lifecycle management
| AI contract review | Contract lifecycle management | |
|---|---|---|
| Primary job | Analyse the language inside a draft | Control the agreement process from intake to renewal |
| Core input | Contract text and legal playbook | Request data, templates, approval rules, and contract records |
| Main output | Clause findings, risk flags, and suggested fallback language | A routed, approved, signed, and tracked agreement |
| Human owner | Attorney deciding the legal position | Legal operations or procurement managing the workflow |
| Use together | Can become the review step inside CLM | Can trigger review and store its approved result |
If the bottleneck is intake, approvals, signature, obligations, or renewals, start with contract lifecycle management software. If the job is generating agreements from approved templates and rules, use document automation. If the problem is finding evidence across a litigation population, use legal document review automation. This page is for the first-pass analysis of language inside repeatable commercial contracts.
How it works
From one playbook to a controlled release
- Phase 101
Choose one contract type
Select an agreement with enough volume, stable legal positions, and measurable delay. Map its current route from intake through first redline, including who reviews which issues and where the approved version is stored.
- Phase 202
Encode the playbook
Define clause types, approved positions, acceptable variants, fallbacks, risk levels, and escalation rules with the legal owner. Unresolved positions stay outside automation. Every rule has an owner and version.
- Phase 303
Run a blind evaluation
Label representative historical contracts, reserve unseen agreements, and include the documents most likely to expose failure. Report misses, false flags, redline corrections, and total reviewer time by clause and risk level.
- Phase 404
Integrate and monitor
Put the review step into the approved document flow, enforce human sign-off, and record reviewer decisions. Expand only after the first workflow meets its acceptance checks under real operating conditions.
Security and governance belong in the first scope
A contract can contain personal data, pricing, liability positions, security obligations, and information that carries privilege or confidentiality duties. The architecture should make the data path inspectable before the first production document moves.
The design records where the original file, extracted text, prompt, output, and reviewer decision are stored; which model or provider processes them; whether any data is retained or used for training; and how deletion works. Access controls, SSO, encryption, audit events, regional hosting, private networking, and customer-managed infrastructure are included when the organisation requires them.
Governance continues after launch. Model, prompt, parser, and playbook versions are tracked. A changed playbook triggers review and, where the risk warrants it, regression testing against the labelled set. Production monitoring looks for shifts in document mix, confidence, escalation, correction, and reviewer behaviour. See AI for legal teams for the wider governance and workflow approach.
Four failures that keep pilots from becoming working systems
- One accuracy number
- An average can hide poor results on rare, expensive clauses. Acceptance checks belong at the clause and risk level, with false negatives reported separately.
- A playbook nobody owns
- The model cannot settle a legal position the team has not settled. A named legal owner must approve each rule, fallback, and future change.
- A second place to work
- Even a strong model will be ignored if lawyers must leave Word or CLM, upload documents manually, and copy findings back into the contract record.
- Time saved before verification
- Model response time is not business value. Measure the reviewer correcting flags, checking sources, repairing redlines, and completing the first pass.
The first release should cover one contract type, a defined clause set, a versioned playbook, blind evaluation, human approval, and one working integration. We do not recommend adding another contract type until the first workflow meets its acceptance checks and lowers net reviewer time. No risk decision or suggested redline goes to a counterparty without the review step your legal team defines.
What it costs
Every project starts at $10000.
The first phase proves the workflow on your contracts: representative agreements, an unseen test set, and your reviewers' risk rules.
Starting investment
Starts at $10000
Scoped around one contract type and its baseline, priced in writing. After a 30-minute call, you get a written build, buy, repair, or wait recommendation plus first-phase direction. Free, whether or not you build with us.
Proof
What clients say about working with RaftLabs
Three-year average engagement. Founders and operators describing the work in their own words. No marketing varnish.
I was pleased with RaftLabs team's quality, consistency and execution.
Dr.J. Ayo Akinyele
President, Co-Founder, Yeletech Security Inc & Bolt
Work with us
Bring one contract type and its playbook.
We will assess the review volume, legal positions, sample quality, and integration path. You will leave knowing whether a custom review system is justified and what the smallest safe first release includes.
- Bring one repeatable contract type, the current playbook, and the point where review time is being lost.
- Leave with a buy, configure, integrate, build, or wait recommendation before implementation is proposed.
- If there is a fit, we will put the first scope, evaluation plan, dependencies, timeline, and cost in writing.
- An NDA is available before you share agreements, playbooks, or security details.
Common questions
AI contract review software extracts and classifies clauses, compares them with a legal team's approved positions, and flags missing or changed provisions. It can also draft fallback language when the playbook supplies it. An attorney reviews the findings, decides the risk, and approves any redline before it leaves the organisation.
Start with one high-volume contract type that follows a recognisable structure and has documented legal positions. NDAs, vendor agreements, SaaS subscriptions, and standard service agreements often fit. Bespoke M&A, financing, or heavily negotiated agreements may benefit from extraction, but they usually need too much judgment for full first-pass automation.
No. Contract review analyses the language inside a draft against a playbook. Contract lifecycle management controls the wider path from request and drafting through approval, signature, obligations, and renewal. A review system can sit inside a CLM workflow, but neither automatically replaces the other.
We agree the clause set and error costs first, then test extraction and deviation flags against labelled contracts the system did not train on. Results are reported by clause and risk level, including false negatives and false positives, rather than as one flattering average. We also measure the reviewer's total time because a system that creates too many false flags can be accurate on paper and still slow the team down.
Buy or configure an established product when its playbooks, Word or CLM integration, security model, and pricing fit the workflow. Consider custom development when your legal positions, document flow, data boundary, or downstream systems create expensive workarounds. A short fit assessment should compare those options before anyone writes custom code.
It can extract clauses, defined terms, and possible deviations, but bespoke agreements carry more context and a higher cost of error. They should not be the first autonomous workflow. Use AI to prepare evidence for a lawyer, keep the original language visible, and route unfamiliar structures, cross-references, and high-risk provisions to manual review.
Yes, when the relevant APIs and permissions are available. The review can start from Word, a contract lifecycle system, document storage, email intake, or a matter workflow. Findings and approved versions should return to the system of record so the team does not inherit a second inbox or an isolated AI report.
The design documents where files and prompts travel, which providers process them, retention and training settings, encryption, permissions, audit events, and deletion rules. Enterprise controls such as SSO, role-based access, regional hosting, private networking, or a customer-approved model may be required. Those choices are made before implementation, not added after a pilot.
Party-side awareness is the trust question to ask first: does the system know which side it is reviewing for, and does it flag changes that benefit the other party? Is it a purpose-built legal AI or a prompt wrapper? How was it trained for your jurisdiction, and on what data? No AI is 100 percent accurate; generative models hallucinate. Verify findings against the source and keep full human oversight.
If review is the job, a full CLM is usually more than the job needs. Playbook quality is the core evaluation criterion: does the tool ship with attorney-built review standards or leave you to build from a blank page? Your output is only as good as your playbook. Thin rules produce thin review, no matter how capable the underlying AI. Word-native workflow matters because lawyers redline where they already work. Explainable redlines: each flag cites the specific playbook rule behind it.
No. The system can prepare a first pass, surface source language, compare it with approved positions, and draft an allowed fallback. A qualified reviewer still decides what the provision means in this deal, whether the business should accept it, and what language can leave the organisation.
Start with one repeatable contract type, an agreed clause set, a legal owner, representative examples, a blind holdout set, and one working integration. Playbook complexity, redline generation, languages, security requirements, and document-management or CLM access determine the scope. RaftLabs prices the first phase after those inputs and acceptance checks are understood.