AI Contract Review Software: 10 Compared

"AI contract review software" is one search term covering three different products. Buy the wrong shape and it doesn't matter how good the model is. A solo transactional lawyer who ends up in a CLM implementation, or an in-house team that buys a drafting add-in when their problem is volume, has wasted the budget either way.

So this comparison is organized by shape, not by a numbered ranking. Ten tools, three groups, every one verified active in our directory. No affiliate links; nobody paid to be here.

At a glance: which shape fits which problem

The fastest way through this category is to name your bottleneck and read across. Every tool named here is verified active in our directory, and the group matters more than the individual pick.

Your bottleneck	Shape to buy	Start your shortlist with
I draft contracts myself and it is slow	Word add-in	Spellbook, Gavel Exec
I need to trial something without a sales call	Published-price add-in	Gavel Exec free tier, Genie AI free plan
I have no template library to start from	Assistant with templates	Genie AI
The same NDAs arrive every day	Playbook review	Ivo, Robin AI
Volume is high and my team is not growing	Playbook review plus managed service	Robin AI
I want the AI to take the first pass unattended	Autonomous review	Luminance
Contracts get lost between email and signature	Full CLM	Ironclad, SpotDraft
I am buying for research and litigation too	Broad platform	Harvey, Legora
I need to answer questions across contracts I already signed	Playbook review or CLM repository	Ivo, Ironclad
Legal is not the only team that needs governing	Adjacent category	AI governance platforms
The bottleneck is discovery, not contracts	Adjacent category	AI ediscovery software

The last two rows exist because a fair number of people arrive at this category having diagnosed the wrong problem. Contract review software will not help a team drowning in document production.

How does AI contract review software actually work?

Worth understanding before a demo, because the stages fail in different ways and vendors tend to describe the whole thing as one capability.

Ingestion and structure recovery. The tool has to turn a file into something it can reason over. A clean Word document is easy. A scanned PDF of a signed counterpart, or a third party's paper with tracked changes from three rounds of negotiation, is where products separate. Ask what happens to a scan, because that is a large share of real contract sets.

Clause identification. The system segments the document and labels the parts: indemnity, limitation of liability, termination, governing law, assignment. This is the layer everything else depends on. A clause the tool does not recognise is a clause it cannot check.

Comparison against a standard. For a playbook tool the standard is your written position: preferred wording, fallback wording, and the point past which a lawyer must be involved. For a drafting add-in the standard is looser, closer to general market practice plus whatever the model has learned. This is the single biggest functional difference between Group 1 and Group 2, and it is why a playbook tool is useless until somebody writes the playbook.

Redline generation. The tool proposes actual edits rather than a list of observations. Generating text is the part large language models do best, and it is also where an unreviewed output is most convincing and most dangerous.

Routing and sign-off. Who sees the exception, who approves the fallback, where the signed copy lands. In Group 1 this is you. In Group 2 it is a workflow. In Group 3 it is the product.

Two things follow from the pipeline. First, most of the value sits in stages two and three, not in the model doing the writing, which is why two tools running the same underlying model can perform very differently on your paper. Second, every stage inherits the errors of the one before it, so a mis-segmented clause produces a fluent redline of the wrong thing. That is the failure mode to look for in a trial, and it does not announce itself.

Group 1: Word add-ins for the lawyer doing the drafting

These tools assume a lawyer is at the keyboard and make that lawyer faster.

Spellbook is the purpose-built option: a contract drafting and review assistant that runs inside Microsoft Word, suggesting redlines, flagging risks, and generating clauses for transactional work. It runs on multiple large language models, including GPT-5 and Claude, rather than betting on a single provider. Pricing is on request.

Gavel Exec comes at the same problem from the document automation world. Gavel's Workflows product assembles legal documents, and Gavel Exec adds AI drafting and redlining in Word. Two things make it stand out in this group: real published pricing ($160/user/month for the paid tier) and a genuine free tier of 25 queries, which makes it one of the only ways to trial serious contract AI without a sales call. Worth knowing: Gavel was acquired by Relativity in June 2026 and continues to operate.

Genie AI rounds out the accessible end, with drafting, review, editing, and negotiation with a large open template library, a free plan, and published paid tiers (Pro at $75/month, Enterprise from $600/month). If budget is the constraint, start here; we covered the genuinely free options in Free and Low-Cost AI Legal Tools That Actually Exist.

Group 2: Playbook review for in-house teams

These tools assume the problem is volume: the same contract types arriving every day, reviewed against the same standards. The AI applies your playbook; lawyers handle exceptions.

Ivo, formerly Latch, redlines against playbooks, maps relationships between contracts, and answers questions across your whole set of agreements, working in both Microsoft Word and Google Docs.

Robin AI does playbook-driven redlining in Word and adds an option the others don't: a managed service that combines AI output with human legal review, useful for teams that want throughput without staffing up.

Luminance pushes furthest toward autonomy. Its models handle drafting, negotiation, and analysis, including autonomous first-pass redlining where the AI takes the initial cut before a lawyer sees the document. It serves both in-house teams and law firms.

All three price on request, which is the norm for this group.

Group 3: Full contract lifecycle management

Here AI review is one feature inside a platform that owns the entire contracting process: intake, negotiation, e-signature, repository, reporting.

Ironclad is the established CLM name, with AI for review, redlining, and repository insights, used mainly by in-house legal and business teams.

SpotDraft covers the same lifecycle, drafting through e-signature to a searchable repository, with its AI review layer branded VerifAI.

Choose this group only if you're actually standardizing company-wide contracting. A CLM you implement halfway is worse than a Word add-in you use daily.

The two that don't fit the boxes

Harvey and Legora both handle contract work as part of broader legal AI platforms spanning research, drafting, and document analysis. If contracts are one of several workflows you're buying for, they belong on your list, and we compared them in depth in Harvey vs CoCounsel vs Legora. If contracts are the whole job, a specialist from the groups above is the simpler purchase.

Quick reference

Tool	Shape	Pricing
Spellbook	Word add-in	On request
Gavel Exec	Word add-in + automation	Free tier (25 queries); $160/user/mo
Genie AI	Assistant + templates	Free plan; Pro $75/mo; Ent. from $600/mo
Ivo	Playbook review	On request
Robin AI	Playbook review + managed service	On request
Luminance	Autonomous first-pass review	On request
Ironclad	Full CLM	On request
SpotDraft	Full CLM	On request
Harvey	Broad platform	On request
Legora	Broad platform	On request

Contract review, contract analysis, or contract analytics?

These three phrases get used interchangeably in marketing and they describe genuinely different jobs. Buying against the wrong one is the second most common mistake in this category after buying the wrong shape.

Contract review is forward-looking and document-by-document. A contract has arrived, or you are writing one, and the question is whether the terms are acceptable. Output is a redline and a list of issues. Everything in Groups 1 and 2 above is built for this.

Contract analysis is backward-looking and set-wide. The contracts are already signed and the question is what is in them. Which agreements auto-renew in the next ninety days, which ones lack a limitation of liability, which counterparties hold a most-favoured-nation clause. Output is an answer across a corpus rather than an edit to a document. Ivo answers questions across an agreement set, and the repository layer inside Ironclad and SpotDraft exists for exactly this.

Contract analytics is the reporting layer above analysis: cycle times, where negotiations stall, how often a fallback gets used, which clause you concede most. This is a CLM feature and effectively nothing else offers it, because it needs the whole process in one system to have data to report on.

The practical test is what you want at the end. If you want a marked-up document, you want review. If you want a list of contracts meeting a condition, you want analysis, and a drafting add-in will not give it to you no matter how good it is at redlining. Teams that buy a Word add-in and then ask it which contracts renew in March have bought the wrong product, and it is an easy mistake to make, because both products are sold under the same three words.

What AI contract review software costs

Pricing in this category is mostly hidden, and that is itself a signal about who each tool is sold to. Of the ten above, two publish a number: Gavel Exec at $160 per user per month with a free tier of 25 queries, and Genie AI at $75 per month for Pro and from $600 per month for Enterprise. The other eight quote on request, which usually means a sales process, an annual commitment and a seat minimum.

Two practical consequences:

• If you want to trial without a sales call, your options are the published-price tools. That is a short list, and it is why Gavel Exec and Genie AI turn up on so many solo and small-firm shortlists regardless of how they compare on capability.
• If you are budgeting before you shortlist, work from figures that have been verified rather than from vendor landing pages. We track them per vendor in Harvey AI pricing, CoCounsel pricing and Spellbook pricing, and the shape of the market in the legal AI pricing guide.

Budget the review time as well as the licence. Nothing in this category removes the qualified human who signs off, so a tool that halves drafting time and adds a verification pass is not a straight 50% saving.

Which group fits your team?

The three groups above map onto three quite different buyers, and the mismatch is where budget gets wasted.

• Solo and small-firm transactional work. A Word add-in. You are the person doing the drafting, the volume does not justify building a playbook, and a CLM implementation would consume more time than it returns. We looked at the whole stack for this case in AI for small law firms.
• In-house legal with repeating contract types. Playbook review. The real test is whether your standards are stable enough to write down. If a lawyer cannot articulate the playbook, the AI cannot apply it.
• In-house legal standardizing contracting company-wide. CLM, and only with an executive mandate behind it. The AI review layer is not the reason to buy one.
• A firm buying for several workflows at once. The broad platforms, because contract review alone will not justify the price.

Whichever group you are in, run the same evaluation. The full version is how to evaluate legal AI; the short version is further down this page.

Security, confidentiality and what procurement will ask

Client contracts are among the most sensitive documents a firm holds, and this is the part of an evaluation that most often gets left until last and then blocks the purchase. Four questions are worth asking on the first call rather than the fifth.

Where does the document go, and what happens to it there? You want a written answer on whether documents are retained after processing, for how long, and whether anything you upload is used to train a model. A vendor that cannot answer that in writing is not ready to hold client paper.

Which certifications does the vendor actually hold? SOC 2 Type II and ISO/IEC 27001 are the two that come up in nearly every legal procurement questionnaire. ISO 27001 is the international standard for an information security management system, and the word that matters is "certified" rather than "aligned with" or "built to the principles of". Ask for the report or the certificate number.

Where is the data stored? Data residency is a hard requirement for a great many in-house teams, especially anyone handling EU personal data or working under a client's own contractual commitments. It is a yes or no question with a country attached, and it is far cheaper to ask in week one than to discover in week six.

What is the governance story for the model itself? NIST's AI Risk Management Framework is the reference most procurement teams now point at. It is voluntary guidance rather than a certification, so nobody can be "NIST certified". What a good vendor can do is describe how they map, measure and manage the risks of their own system. Vague reassurance here is a signal. If AI governance is becoming a formal programme where you work rather than a purchasing question, we compared the tooling for that separately in AI governance platforms.

None of this is exotic. It is the same due diligence any legal technology purchase gets. The only reason it is worth calling out is that vendors in this category are young enough that the answers vary far more than they do for established software, and the ABA's annual Legal Technology Survey Report is a useful reminder of how quickly adoption in the profession has outrun the diligence around it.

Before you evaluate anything: the accuracy baseline

Every vendor in the table above will tell you their output is grounded, verified, or hallucination-free. There is published, independent evidence on how much weight that claim carries.

Stanford's RegLab and Institute for Human-Centered AI tested the leading professional legal research tools across 202 queries and published the results as Hallucination-Free? Assessing the Reliability of Leading AI Legal Research Tools (later peer-reviewed in the Journal of Empirical Legal Studies). They found hallucination rates of 17% for Lexis+ AI and 33% for Westlaw AI-Assisted Research, against vendor marketing that claimed "100% hallucination-free linked legal citations." Their summary: providers' claims are overstated.

Those are research tools rather than contract reviewers, so don't transfer the percentages directly. Transfer the lesson: a retrieval-grounded architecture and a confident accuracy claim are not the same as measured accuracy, and no vendor in this category publishes independent error rates for their own product.

This is also now an ethics question with an answer. ABA Formal Opinion 512 (July 2024) was the ABA's first formal ethics opinion on generative AI, and it holds that the existing duties of competence, confidentiality, client communication and reasonable fees all apply to AI-assisted work unchanged. Competence, in the ABA's own framing, includes understanding the benefits and risks of the technology you use to deliver legal services, which in this category means knowing how your tool fails rather than taking a vendor's word that it does not. Whatever you buy, a qualified human still signs off, so price the review time into the ROI case, because the tools that claim it away are the ones the ABA opinion is about.

How to run the evaluation

Four suggestions from watching this category consolidate:

1. Trial on your own paper. Every one of these tools demos well on a clean NDA. Bring the ugliest third-party paper you reviewed last quarter and watch what happens.
2. Ask for an error rate, in writing. Not an accuracy claim, but a measured miss rate on a defined test set, and who measured it. The answers you get to that question are the most informative part of any demo.
3. Count the exceptions, not the automations. For playbook tools especially, the question isn't what the AI handles. It's how gracefully it hands off what it can't.
4. Check the ownership. This corner of legal AI changes hands constantly, and Gavel to Relativity is just the latest. Our acquisition tracker lists every move we've verified, because the company you sign with may not be the company that owns the product at renewal.

Common questions

What is AI contract review software?

Software that reads a contract, flags risk, non-standard language and missing terms, and suggests changes. In practice the phrase covers three distinct product shapes: a drafting assistant inside Word, a playbook engine that redlines against your written standards, and a contract lifecycle platform where review is one feature alongside intake, signature and reporting.

Can AI review contracts accurately?

Accurately enough to be worth using, and not accurately enough to sign off unsupervised. No vendor in this category publishes an independent measured error rate for its own product, and the nearest independent evidence in adjacent legal AI is the Stanford study cited above. Treat the output as a first pass by a fast, tireless junior.

Is there free AI contract review software?

There are free tiers rather than free products. Gavel Exec offers 25 queries free and Genie AI has a free plan, and both are substantial enough to evaluate the tool on your own paper, which is more than most of this category allows. The wider set is in free and low-cost AI legal tools.

What is the difference between contract review AI and CLM?

Scope. Contract review AI acts on the document in front of you. CLM owns the process around it: how the contract arrives, who approves it, where it gets signed, and where it lives afterwards. Buying CLM to get better redlining is buying a filing system to fix a pen.

Does using AI on client contracts create an ethics problem?

It creates a duty, not a prohibition. ABA Formal Opinion 512 holds that the duties of competence, confidentiality, client communication and reasonable fees apply to AI-assisted work unchanged, and that competence includes understanding the benefits and risks of the technology you are using. The practical reading: know your tool's failure modes, check what it does with client data, and keep a qualified human on the sign-off.

How much does AI contract review software cost?

Two of the ten tools here publish a price: Gavel Exec at $160 per user per month with a free tier of 25 queries, and Genie AI at $75 per month for Pro and from $600 per month for Enterprise. Everything else quotes on request, which in practice means an annual commitment, a seat minimum and a sales cycle measured in weeks. Verified per-vendor figures are in our Harvey AI pricing, CoCounsel pricing, Spellbook pricing, Luminance pricing and Legora pricing breakdowns, and the shape of the whole market is in the legal AI pricing guide.

What is contract analysis software, and is it the same thing?

No. Contract review acts on a document in front of you and hands back a redline. Contract analysis answers questions across contracts you have already signed, such as which agreements renew this quarter or which lack a liability cap. A Word add-in will not do the second job however well it does the first, and the two are sold under the same phrase constantly. The section above sets out the distinction in full.

Do these tools work on scanned PDFs and third-party paper?

This varies more than any other capability in the category and it is rarely on a feature page. A clean Word document is the easy case every demo uses. A scanned counterpart, or a document carrying three rounds of somebody else's tracked changes, is where clause identification degrades, and a tool that mislabels a clause will redline the wrong thing fluently. Bring both to a trial and make the difference visible before you sign.

Which is better for a small firm, a Word add-in or a CLM?

A Word add-in, in almost every case. CLM earns its cost when contracting is a company-wide process with intake, approvals and a repository that several teams depend on. A small firm buying CLM for better redlining is paying for a filing system to solve a drafting problem, and a half-implemented CLM is worse than no CLM at all. The full picture for that buyer is in AI for small law firms.

How long does it take to get value from a playbook tool?

Longer than from a drafting add-in, because the playbook has to exist first. The AI applies standards you have written down, so the real project is the writing-down: preferred position, acceptable fallback, and the threshold past which a lawyer must look. Teams that already negotiate from a settled position get there quickly. Teams where each lawyer holds their own view discover that the tool has surfaced a disagreement rather than a workflow, and that is much better to know before the contract is signed than after.

Which AI contract review tool is best?

There is no single answer, which is why this comparison is grouped by shape rather than ranked. What suits a solo lawyer drafting in Word is a poor fit for an in-house team clearing NDAs at volume, and both are wrong for a company standardizing contracting end to end. Pick the group first, then trial two tools inside it on your own worst contract.

The full, filterable list, including tools that didn't fit this comparison, lives in the contract review category. Listings are free and verified; if we're missing one, tell us.