How to Evaluate Legal AI Platforms: A Buyer's Checklist

1. Security and data protection

Security is the threshold issue. If a platform cannot protect client confidentiality, nothing else matters. Attorneys have ethical obligations under the Rules of Professional Conduct to make reasonable efforts to prevent unauthorized disclosure of client information. Using an AI tool that compromises confidentiality is not just a security risk; it is a professional responsibility violation.

Data isolation. Verify that your data is isolated from other customers' data. The platform should not use your client information to train its models or improve its service for other users. Ask specifically: is our data used in any form of model training? The answer must be an unqualified no.

Encryption. Data should be encrypted both in transit (TLS 1.2 or higher) and at rest (AES-256 or equivalent). Ask about key management: who holds the encryption keys, and can you bring your own keys?

Compliance certifications. Look for SOC 2 Type II certification at minimum. For firms handling regulated data, ask about HIPAA compliance, FedRAMP status, or other industry-specific certifications.

Data residency. Know where your data is processed and stored. For firms with international clients, data residency may be subject to GDPR, data localization requirements, or client-specific restrictions.

2. Citation accuracy and verification

Citation accuracy separates professional-grade legal AI from general-purpose tools. Any platform you consider for legal research or drafting must demonstrate a clear approach to preventing hallucinated citations.

Grounded generation. Ask how the platform generates citations. Does it retrieve from a verified legal database, or does it generate citations from model weights? Retrieval-augmented generation, where the AI pulls from a curated corpus rather than generating from memory, is the minimum standard for professional use.

Verification layer. Does the platform include built-in citation verification? Can it check whether cited cases exist, are still good law, and support the propositions attributed to them? See our citation verification guide for the specific checks to evaluate.

Source transparency. Every citation should link directly to the source material. You should be able to click through to the full opinion and read the relevant passage in context. Platforms that provide citations without source links are not giving you enough to verify their work.

Accuracy metrics. Ask the vendor for citation accuracy data. What percentage of citations are verified as existing and correct? What is the false positive rate? A serious vendor will have this data. A vendor that cannot produce it has not measured the problem.

3. Context window and document handling

The context window determines how much information the AI can consider at once. For legal work, this matters enormously. A 50-page contract, a 200-page deposition transcript, or a set of related discovery documents all need to be processed in their entirety for the analysis to be reliable.

Effective context size. Do not just look at the advertised token limit. Ask how the platform actually handles documents that exceed the model's native context window. Does it truncate? Does it chunk and process in sections? Does it use retrieval augmentation to handle unlimited document sizes? The answer affects whether the AI sees your full document or only a portion of it.

Multi-document handling. Can the platform analyze multiple documents simultaneously? For due diligence, case preparation, or contract comparison, you need the AI to understand relationships between documents, not just analyze them in isolation.

Format support. Legal documents come in many formats: Word, PDF, scanned images, spreadsheets, email archives, and presentation files. Verify that the platform handles the formats you work with regularly, including scanned documents that require OCR processing.

4. Pricing model and total cost

Legal AI pricing varies widely and the total cost depends on the pricing model as much as the sticker price. Understanding the model is essential to comparing platforms fairly.

Per-seat pricing. A fixed monthly or annual fee per user. Predictable and easy to budget, but can be expensive for large teams and may discourage adoption if attorneys feel their usage does not justify the per-person cost.

Usage-based pricing. Charges based on queries, tokens, or documents processed. Can be cost-effective for light users but creates uncertainty and can discourage attorneys from using the tool thoroughly, since every query has a visible cost.

Hidden costs. Watch for onboarding fees, training costs, integration fees, minimum commitments, overage charges, and data egress fees. Ask for the total cost of ownership, not just the subscription price.

For a comprehensive breakdown of pricing models across the legal AI market, see our pricing models guide. Irys publishes its pricing openly on its pricing page, with no hidden fees or enterprise-only tiers.

5. Red flags to watch for

No published pricing. If the vendor requires a demo before sharing pricing, they are likely anchoring to your budget rather than competing on value. Transparent pricing is a signal that the vendor is confident in their offering.

Vague accuracy claims. Claims like "industry-leading accuracy" without supporting data are marketing, not evidence. Ask for specific metrics, test methodology, and whether accuracy data has been independently validated.

No free trial. If a vendor will not let you test the product with your own documents and your own questions, that is a risk signal. A confident vendor wants you to see the product in action because the product sells itself.

Training data opacity. If the vendor cannot explain what data their models were trained on, how they handle your data, and whether your inputs affect the model, proceed with extreme caution. Professional responsibility requires you to understand these risks before exposing client information.

Single-model dependency. Platforms built on a single AI model are vulnerable to that model's limitations and pricing changes. Model-agnostic platforms that can use multiple models provide more flexibility and reduce vendor lock-in risk.

6. Questions to ask every vendor

Keep this list handy during vendor evaluations. The answers will tell you more than any demo or marketing deck.

On security: Is our data used for model training? Where is our data stored and processed? What compliance certifications do you hold? What happens to our data if we cancel?

On accuracy: What is your citation hallucination rate? How do you prevent fabricated citations? Can we see accuracy benchmark results? What happens when the AI is uncertain?

On capabilities: What is the effective context window for a single document? Can the system analyze multiple documents simultaneously? What document formats are supported? How does the system handle scanned or image-based PDFs?

On pricing: What is the total cost for our team size and expected usage? Are there overage charges? What fees are not included in the subscription price? What is the contract commitment period?

On the product: Can we run a pilot with our own data? What does onboarding involve? How do you handle feature requests? What is on the product roadmap for the next six months?