Custom AI vs. Off-the-Shelf Legal AI Tools: Harvey, Lexis+, and When to Build Your Own
An honest comparison of buying Harvey, Lexis+ Protégé, or CoCounsel versus building custom legal AI. When off-the-shelf wins, when custom wins, and how to decide.
The legal AI landscape in 2026
The market has exploded. Harvey raised over $100M and hit recurring revenue milestones faster than most enterprise SaaS companies. Thomson Reuters spent $200M acquiring Casetext and integrating CoCounsel into Westlaw. LexisNexis launched Protégé. Luminance, LegalOn, and dozens of smaller players compete for every corner of the legal workflow.
If you’re a technology leader at a law firm, legal publisher, or legal services company, you’ve got more options than ever. The question isn’t “should we use AI?” — it’s “should we buy, or should we build?”
We’ve done both. We’ve built custom legal AI systems — ChatBook tools for the AAA, the PlanYourSunset estate planning platform, AI engines for law firms — and we’ve also evaluated off-the-shelf options for clients and recommended them when they were the better fit. So our perspective here is pragmatic, not self-serving.
What the off-the-shelf leaders actually do well
Harvey excels at general legal research and drafting. It’s trained on a broad legal corpus and handles common workflows — memo drafting, contract review, case research — competently. For a large firm doing high-volume, varied work, it reduces research time meaningfully.
Lexis+ Protégé and Westlaw’s CoCounsel sit on top of the world’s largest legal databases. When your queries map to published case law and statutes, these tools deliver well-cited results. The integration with existing Westlaw/Lexis workflows is seamless.
Luminance focuses specifically on contract intelligence. For organisations processing high volumes of contracts, it’s purpose-built and effective.
These are real products solving real problems. We recommend them to clients when they’re the right fit. The question is: when aren’t they?
Five factors that determine the answer
The first is proprietary data. If your AI needs center on publicly available legal information — case law, statutes, regulations — buy. If your competitive advantage comes from proprietary content — internal research archives, unpublished arbitration awards, firm-specific precedent banks, specialized industry knowledge — build. Off-the-shelf tools don’t have your data. Some let you upload documents, but they treat uploads as supplementary, not primary. This is exactly why we built a custom system for the AAA rather than deploying Harvey — their journal archive and arbitration materials represent decades of proprietary knowledge that no off-the-shelf tool could leverage.
The second is workflow specificity. If your workflows align with common patterns — research, drafting, contract review — buy. If you need AI integrated into specialized workflows — custom intake processes, proprietary scoring models, specific compliance procedures — build. PlanYourSunset is a clear example. Estate planning involves jurisdiction-specific rules (NY only, for now), complex family dynamics, and workflows that combine legal document generation with client-facing questionnaires and an AI assistant named Larry who explains legalese in plain English. No off-the-shelf legal AI tool handles this.
The third is cost at scale. At fewer than 50 users with standard usage, buying makes sense. At 100+ users, the math changes fast. Harvey and similar tools charge $100–$300/user/month for enterprise plans. For 200 attorneys, that’s $240K–$720K annually — every year. A custom solution costs $100K–$200K to build, with $30K–$100K in annual infrastructure and maintenance. By year two, custom is cheaper. By year three, dramatically so. And the custom solution appreciates — it gets better with data and user feedback. The SaaS subscription stays the same.
The fourth is accuracy requirements. Off-the-shelf tools optimise for breadth across all practice areas. Custom solutions optimise for depth in your specific domain. A custom RAG system built on your content, with domain-tuned embeddings and a verification layer, will outperform a general tool on your specific use cases — often dramatically. If general accuracy (85–90%) is acceptable, buy. If you need near-perfect accuracy for specific content types, build.
The fifth is control and compliance. If standard data handling meets your requirements and you’re comfortable with data flowing through a third-party provider’s infrastructure, buy. If you need full control over data residency, model behavior, audit trails, or regulatory requirements demand it — build.
“The build vs. buy decision isn’t really about technology. It’s about where your competitive advantage lives. If AI is a productivity tool for your team, buy it. If AI is your product or directly enables your product, build it. The difference matters enormously.”
The hybrid approach most organisations miss
The smartest organisations don’t choose one or the other — they do both.
Use off-the-shelf tools for general legal work. Let your attorneys use Harvey or CoCounsel for routine research and drafting. These tools are good at this, and the per-user cost is justified by time savings.
Build custom for your differentiator. Invest development resources in the AI capabilities that set your organisation apart — making proprietary content searchable, automating specific workflows, embedding AI into client-facing products.
This approach gets you 80% of the value from off-the-shelf tools while building defensible competitive advantages with custom development. And it lets you start custom projects with narrower scope, reducing risk and cost.
Decision framework
Answer honestly: Does your AI need depend on proprietary data? Are your workflows specialised or industry-specific? Will you have 100+ regular users? Do you need domain-specific accuracy above 95%? Do you have strict data control or compliance requirements?
Three or more “yes” answers point toward custom development. One or two: start with off-the-shelf and build custom only for specific differentiating use cases.
What custom legal AI development actually involves
If the framework points toward building: 6–10 weeks for an MVP covering your core use case. 3–6 months for a production system. This assumes a team that’s done it before — first-timers should double these estimates.
Budget: $40K–$80K for MVP, $80K–$200K for production. Ongoing costs of $2K–$8K/month for infrastructure and maintenance.
What you need from your side: access to your content in digital format, 3–5 domain experts for requirements and testing, a designated product owner who can make scope decisions, and clear success criteria.
What you get: a system tailored to your content, your workflows, and your accuracy requirements, running on infrastructure you control, with no per-user licensing fees.
Not sure which approach is right? Book a consultation — we’ll review your use case, evaluate off-the-shelf options honestly, and recommend the path that makes the most sense. No obligation to build with us.