The Legal Tech Buying Guide in 2026

Start with fit: what problem does this actually solve?

• “What specific workflows for litigators / deal lawyers / [your group] does your product improve?”
• “If we turned this on tomorrow, what are the top three use cases you’d roll out first?”
• “Which tools do your customers usually stop using once they adopt yours?”

AI basics: what does “AI-powered” mean here?

• “Where, specifically, does your product use AI? Drafting? Search? Classification? Summaries?”
• “Which models or vendors are you using (e.g., OpenAI, Anthropic, internal models), and when do you send data to them?”
• “Is the AI output deterministic or does it change each time? Can we control that?”
• “How do you reduce hallucinations and factual errors?”

• Clear architecture diagrams or docs.
• Guardrails: retrieval-augmented generation (RAG), rule-based checks, required human review for certain tasks.
• Real evaluation metrics: accuracy tests, benchmark tasks, red-team reports.

Data use and training: what happens to your client data?

• “Do you, or any AI model you call, train on our data—now or in the future?”
• “Can you contractually guarantee that our data won’t be used to train public models?”
• “What categories of data do you store (content, metadata, usage logs)? For how long?”
• “Can we opt out of data being used to improve your product models? What does ‘opt out’ actually change in your system?”
• “Where is our data stored geographically, and who are your sub-processors?”

• Prohibit training on customer data for public models and state that clearly.
• Give you a written data-use matrix (what they collect, why, how long, where).
• Provide a sub-processor list and commit to notice/approval on changes.

Security: can this vendor actually protect your crown jewels?

• “What security certifications do you hold (e.g., SOC 2 Type II, ISO 27001)? Can we see the reports or at least the summary letter?”
• “How do you encrypt data in transit and at rest? Which algorithms and key-management practices do you use?”
• “Do you support SSO and MFA? Role-based access control? IP whitelisting or zero-trust architecture?”
• “What is your incident-response plan? How quickly will you notify us of a breach? Can we see the policy?”
• “Do you conduct regular third-party security audits and penetration tests?”

Ethics and professional responsibility: how do they help you stay compliant?

• “How do you help firms comply with Model Rules on competence, confidentiality, and supervision when using your product?”
• “Do you provide configuration options that let us set firm policies for AI use (e.g., blocking certain tasks, requiring human review, logging AI usage)?”
• “Do you offer guidance, templates, or training on ethical use of your tool?”

• Acknowledges ethical rules explicitly.
• Provides admin controls so you can enforce your own AI usage policies.
• Supports audit logging so you can reconstruct what the tool did and who used it if something goes sideways.

Integrations and architecture: will this break your stack?

• “What out-of-the-box integrations do you support (DMS, email, Office 365, Google Workspace, practice-management, e-billing, SSO)? Which are most common for firms like ours?”
• “Is there a public API? Webhooks? How mature is it?”
• “How do you handle identity and permissions? Do you mirror our groups from Azure AD/Okta, or manage access separately?”
• “What happens if an integration fails or changes—how do we avoid data loss or corruption?”

• A clear architecture diagram.
• Written integration docs, not just “we integrate with everything.”
• A story about permissions: ideally, everything flows from your main identity provider so you’re not managing accounts in five places.

Accuracy, evaluation, and guardrails: how do they keep the AI from embarrassing you?

• “How do you test the accuracy and reliability of your AI features? What benchmarks or internal evaluations do you run?”
• “Do you provide citations, source documents, or confidence indicators with AI output?”
• “What safeguards are in place against hallucinations, bias, and leakage of confidential information?”
• “Can admins disable or limit high-risk features (e.g., ‘draft full brief’)?”

• Specific evaluation processes (test suites with legal tasks, human review, comparison to baselines).
• Design patterns like: RAG with your documents, explicit source citations, warnings on probabilistic content.
• Interfaces that make it easy to review, edit, and approve AI work products.

Implementation, change management, and support: who’s doing the heavy lifting?

• “What does a typical implementation timeline look like for a firm of our size? Who’s on your side of the project?”
• “What data migration or configuration work is required? Who does it?”
• “What training do you provide for lawyers, staff, and admins—live, on-demand, or both?”
• “What’s your support model: hours, SLAs, escalation path, dedicated customer success?”

• A repeatable implementation plan with milestones.
• Clear division of responsibilities between your firm and the vendor.
• Training material that works for lawyers (short, practical, scenario-based).

Contract terms, data ownership, and exit strategy

• “Who owns the data and any models or outputs derived from our data? Is that spelled out clearly?”
• “What happens at termination? How do we get our data out, in what format, and at what cost?”
• “What are your uptime and performance SLAs? What credits or remedies apply if you miss them?”
• “How do you handle IP and indemnity—especially for AI-generated content or third-party models?”
• “Do you permit audits, or at least provide regular compliance attestations?”

• Explicit language that you own your data.
• Export rights during and after the relationship, with practical formats.
• Clear limits on how they can use your data beyond providing the services.
• Reasonable caps and carve-outs around IP and security incidents.

Pricing, ROI, and experimentation

• “How is pricing structured (per-user, per-matter, consumption)? What typically drives cost up or down?”
• “What does a pilot or proof-of-concept look like? Can we test with real matters and a small group first?”
• “How do your customers measure ROI? Can you share examples or benchmarks for firms like ours?”

• Offer a scoped pilot with success metrics.
• Help you pick a narrow but high-impact use case to prove value.
• Be transparent about all add-ons (storage, integrations, support tiers, AI usage fees).

Red flags and a quick playbook

• They can’t explain where your data goes or how AI is used.
• No security certifications, no pen-test, no real incident-response plan.
• No mention of ABA or state ethics guidance, or how they help you comply.
• They resist giving references from comparable firms.
• The only answer to “how do you prevent hallucinations?” is “our model is very accurate.”

• Define your top 3–5 workflows to improve.
• Use the categories above to build a short RFP or question list.
• Run the same questions with every vendor so you can compare apples to apples.
• Involve IT, risk, and a practicing lawyer or two in each evaluation.
• Pilot, measure, then decide.