Legal AI Firm Harvey to Open Dallas Office in April, Getting Closer to Lone Star State Clients

Harvey Plans Dallas Office in April: What Legal Teams Should Do Now

Harvey, a provider of AI products for legal services, is opening a Dallas office in April to get closer to clients across the Lone Star State. That move signals growing demand for practical AI support across Texas firms and in-house departments.

Local presence matters. It usually means faster support, on-site workshops, and better context for Texas-specific matters-energy, real estate, healthcare, and fast-moving corporate work.

Why this matters for law firms and in-house teams

• Speed: Easier access to demos, pilots, and training. Less back-and-forth, more hands-on sessions.
• Context: Vendors learn your workflows faster when they're in the same market and meet your team face-to-face.
• Change management: Adoption improves when lawyers can test tools with real matters, not hypothetical examples.
• Stakeholder alignment: IT, legal ops, and practice leaders can evaluate risk and ROI together, on a clear timeline.

High-value AI use cases legal teams are prioritizing

• First drafts for memos, motion shells, letters, and contract language (with attorney review).
• Research acceleration: summarizing authorities, comparing positions, and flagging gaps for deeper analysis.
• Contract review and diligence checklists to speed up issue spotting.
• Policy and compliance updates with quick summaries for business partners.
• eDiscovery triage: clustering, early case assessment notes, and faster culling decisions.

How to prepare before April

• Pick 2-3 pilot workflows: Choose repeatable tasks with measurable outcomes-time saved, quality, or cost.
• Define "done": Write a one-page pilot plan: scope, owners, guardrails, metrics, and a 30-60 day review date.
• Data rules: Create a "green/red" list for inputs (public vs. sensitive). Spell out what never goes into any external tool.
• Attorney oversight: Require human review and source checks for any output used in matters.
• Update policies: Revisit confidentiality and tech competence duties. See Model Rules on Confidentiality (Rule 1.6).
• Stack integration: List systems that must connect: DMS (iManage/NetDocuments), M365, SSO, records, and matter mgmt.

Vendor diligence checklist (use for any legal AI tool)

• Security: SOC 2 Type II, ISO 27001, encryption at rest/in transit, SSO (SAML/OIDC), audit logs, RBAC.
• Data handling: Does the vendor train models on your data? Retention periods? Data residency options (e.g., U.S.-only)?
• Confidentiality walls: Workspace isolation for clients/matters; conflict checks and user-level restrictions.
• Controls: Admin console for permissions, content filters, and output redaction where needed.
• Accuracy: Evaluation methods, citation handling, and processes for error reporting and remediation.
• Model choices: Which models are used, how updates are managed, and fallback options if a model underperforms.
• Deployment: Cloud, private cloud, or on-prem options; SLAs and support response times.
• Integrations: DMS, eDiscovery, CLM, and enterprise tools you already use.
• Commercials: Pricing model, usage caps, indemnities, and exit terms (portability and data deletion).

What a Dallas presence could mean in practice

• On-site workshops for pilot setup, prompt patterns, and review protocols.
• Faster procurement cycles with local meetings between legal, IT, and procurement.
• Closer feedback loops to shape features your teams will actually use.
• Potential partnerships with Texas firms and corporate departments for targeted solutions.

Action plan for Texas legal teams

• Week 1: Identify pilot matters and owners. Draft success metrics and guardrails.
• Week 2: Prep a sanitized test dataset. Confirm prohibited data categories.
• Week 3: Line up IT, security, and legal ops for a 60-minute vendor session.
• Week 4: Schedule hands-on demos in April; set a 30-60 day pilot review on the calendar.

Bottom line

Harvey's Dallas office is set for April. If you're in Texas, this is a good window to scope pilots, tighten policies, and set clear metrics so you can show results, not slides.

If your team wants structured training to speed up adoption and governance, explore our role-based resources here: AI courses by job.