“Legaltech” is the umbrella category for any technology used in the practice or operation of law — CLM, eDiscovery, matter management, legal research platforms, e-billing, practice management, and more. “Legal AI” is the subset of legaltech specifically focused on machine learning and (increasingly) generative AI applied to legal tasks. Most legaltech is now adding AI features; many legal-AI vendors are evolving into broader legaltech platforms. The distinction is blurring.
The legaltech landscape
Legaltech historically organized into seven main categories:
| Category | Examples | Primary user |
|---|---|---|
| Contract management | Ironclad, Agiloft, SirionLabs, Concord | In-house Legal Ops |
| eDiscovery | Relativity, Everlaw, DISCO, Logikcull | Litigation teams, in-house and firm |
| Practice management | Clio, MyCase, Filevine | Solo and small firms |
| Legal research | Westlaw, LEXIS, Bloomberg Law, Casetext | All practicing attorneys |
| Matter and spend management | Onit, Mitratech, BusyLamp, Brightflag | In-house Legal Ops |
| Document production | Litera, iManage, NetDocuments | Mid-to-large firms |
| Court filing | One Legal, File & ServeXpress, ECF systems | Litigation teams |
Each category has its own vendor landscape, sales cycle, and integration patterns.
Legal AI as a layer
Legal AI organizes by capability rather than category:
- Drafting AI. Harvey, Spellbook, Casetext Cocounsel — AI for drafting contracts, briefs, memos.
- Review AI. BlackBoiler, Luminance, LawGeex — AI for reviewing inbound contracts against playbooks.
- Research AI. Thomson Reuters CoCounsel, Lexis+ AI, Casetext — AI for legal research.
- eDiscovery AI. Relativity AI, Everlaw AI, DISCO Cecilia, Reveal Brainspace — AI for privilege review, document classification, case analysis.
- Knowledge management AI. Litera Foundation, increasingly direct Claude Skills — AI for knowledge retrieval.
- General-purpose AI. Claude, ChatGPT (with appropriate enterprise terms) — used across categories.
How the categories converge
Three patterns:
- Legaltech adds AI. Ironclad ships Ironclad AI; Relativity ships Relativity AI; Litera ships AI features across products. The legaltech vendor becomes a legal-AI vendor by extension.
- Legal AI broadens scope. Harvey started as drafting AI; now spans research, contract review, document analysis. The legal-AI vendor builds toward a broader platform.
- General-purpose AI enters legal. Claude, ChatGPT with custom Skills replace specialized legal-AI tools for some use cases. The general-purpose platform competes with specialized legal-AI vendors directly.
When to choose legal-AI specialists vs general-purpose AI
The strategic question for Legal Ops:
| Use case | Legal-AI specialist | General-purpose AI + Skills |
|---|---|---|
| Highest-bar drafting (M&A, complex commercial) | Harvey, Spellbook | Borderline; fine-tuned playbook needed |
| Routine NDA review | LawGeex, BlackBoiler | Claude + contract redline Skill |
| Legal research with citations | Thomson Reuters CoCounsel, Lexis+ AI | Not viable — need verified sources |
| Knowledge retrieval from firm corpus | Litera Foundation | Claude + custom Skills against firm DMS |
| First-pass eDiscovery review | Relativity AI, Everlaw AI | Not viable — production-grade scale needed |
| Generic summarization, drafting, analysis | Specialist overkill | Claude is the right answer |
Specialists win when the data, the workflow, or the integration is legal-specific. General-purpose wins when the task is generalizable and the data flows in normally.
How to think about the budget
Most in-house legal AI budgets in 2026 have three line items:
- Enterprise general-purpose AI. Claude Enterprise or equivalent — covers the broad use cases at predictable per-seat pricing.
- One or two legal-AI specialists. Typically Harvey or Spellbook for drafting, plus one CLM-integrated AI (Ironclad AI or similar) for contract workflow.
- Specialty AI for high-leverage use cases. Casetext for research-heavy practices, AI eDiscovery features in the matter platform when discovery is recurring.
The over-buying pattern is purchasing every legal-AI specialist; the under-buying pattern is trying to do everything with general-purpose AI alone.
Related
- AI policy for legal teams — governs which tools are authorized
- Legal Ops maturity model — describes when AI investment compounds
- What is Legal Ops? — function that owns legaltech vs legal-AI strategy
- Best AI tools for legal ops — head-to-head comparison