How to Evaluate Legal AI Without Getting Sold

Every legal AI vendor will tell you their product is the best. Most of them will show you impressive demos. Some of them will hide their pricing until you're deep in a sales cycle. This guide gives you a framework for evaluating legal AI platforms based on what actually matters to your firm — not on what looks good in a demo.

Start With Your Constraints, Not Features

Before evaluating any platform, answer these questions for your own firm:

Where must your data reside? If your firm handles matters where attorney-client privilege is paramount, where client agreements prohibit cloud storage, or where regulatory requirements dictate data locality, you need on-premise deployment. Not all platforms offer this. Most don't.

What's your real budget? Not your aspirational budget — your actual budget. Per-seat pricing at enterprise rates can run $200 to over $1,200 per attorney per month. If you're a 10-person firm, the difference between $199/month for the firm and $225–$1,200/seat/month is the difference between adoption and abandonment.

How many attorneys need access? Some platforms have seat minimums designed for enterprise deployments. If you're a solo practitioner or a three-person boutique, ask explicitly whether the platform serves firms your size.

What documents do you need AI to work with? Published case law? Your own contracts? Internal briefs? Deposition materials? Different platforms excel at different source materials.

The Questions Every Vendor Should Answer

On citation accuracy: "How does your platform prevent hallucinated citations? Can you show me the source tracing mechanism?" Any platform that can't show you exactly how citations are validated should be disqualified. Hallucinated case law isn't an inconvenience — it's a malpractice risk.

On data security: "Where specifically are my documents processed? Can I verify this independently?" Don't accept "we take security very seriously" as an answer. You need architecture details: where data lives, who can access it, what happens during a breach, and whether your data can be subpoenaed from a third party.

On deployment: "Can this run on-premise? If not, what's your strongest argument for why cloud is acceptable for my most sensitive matters?" This isn't a trap question — cloud deployment is genuinely acceptable for many use cases. But the vendor should be able to articulate why, specifically, for your use case.

On pricing: "What's the total cost for my firm at our current size? What happens if we grow?" Hidden pricing is not a sign of sophistication. It's a sign that the number is high enough to require a sales pitch before you see it.

On independence: "Do you build your own AI models, or do you rely on a third party? What happens if that third party changes terms, raises prices, or discontinues the capability you depend on?" Vendor dependency isn't theoretical. It's a strategic risk.

Red Flags During Evaluation

Demos that only show best-case scenarios. Ask to see how the platform handles ambiguous questions, documents with poor formatting, or queries where the answer isn't in the source material.

Pricing that requires multiple meetings to discover. If a vendor can't tell you what their product costs in a 15-minute conversation, the product is either overpriced for your firm or the sales process is designed to create commitment before revealing cost.

Claims without evidence. "99% accuracy" means nothing without methodology, test conditions, and benchmarks. "Enterprise-grade security" means nothing without architecture details. Press them.

No on-premise option combined with vague security claims. If a vendor says "your data is secure" but can't explain where your data physically resides and who can access it, keep looking.

A Framework for Decision

Rate each platform you evaluate on these dimensions, weighted by your firm's priorities:

Data residency and security (weight: high for sensitive matters). Does the platform offer the deployment model your most sensitive work requires?

Citation accuracy and traceability (weight: high for all firms). Can every AI-generated citation be traced to a specific source? Is there a verification mechanism?

Cost at your scale (weight: depends on firm size). What's the real monthly cost for your specific firm? Including all seats, all features, any required content subscriptions?

Ease of adoption (weight: medium). How much will this disrupt existing workflows? What's the ramp-up time?

Vendor independence (weight: medium-high). Is the platform's technology proprietary, or does it depend on a third party?

Frequently Asked Questions

How long should a legal AI evaluation take? A thorough evaluation of 2-3 platforms should take 4-6 weeks, including demos, trial periods (if offered), and internal review.

Should I involve my IT team in evaluation? Yes, especially for data security and deployment questions. Your IT team can assess architecture claims that a managing partner may not be equipped to verify.

What's the minimum a legal AI platform should be able to do? At minimum: generate cited answers from your documents, trace every citation to a specific source, and prevent hallucinated references. Everything else is valuable but secondary.

How do I know if a platform's citations are reliable? Ask the vendor to process documents you know well. Then verify the citations manually. If the platform cites accurately on materials you can check, that's evidence. If it doesn't, that's disqualifying.