Legora vs Harvey vs Claude + MCP: A Small Firm Guide

By Jude Lee · · Comparison

Three legal professionals comparing AI-assisted document review on laptops in a law firm conference room

Legal AI vendors keep announcing large funding rounds at large valuations. Harvey, Legora and several others have raised repeatedly, and the specific figures move fast enough that anything printed here would be stale within a quarter — read them off each vendor’s own newsroom or press release rather than an aggregator, and treat round size as a weak signal about product fit either way.

The direction is the part worth acting on: capital is concentrating in the platform model. For a firm with twelve lawyers and a Clio subscription, that means platforms will keep improving, keep getting harder to ignore, and keep being priced primarily for buyers much larger than you. It does not mean buying one is automatically right. We walked through the same dynamic the last time a legal-AI valuation made headlines; the strategic question for small firms hasn’t changed.

Three different products hiding under one label

“Law firm AI software” currently describes three genuinely different things, and most buying mistakes come from comparing across categories as if they were interchangeable.

The legal AI platform. Legora and Harvey both give lawyers a workspace: load a document set, ask questions across it, draft from precedent, and in some configurations reach into your document management system. They are not the same shape, though. Per each vendor’s published product documentation — verify directly, because this moves monthly, and this piece is written as of 2026 — Legora’s centre of gravity is tabular review (rows are documents, columns are questions you define) plus a Microsoft Word add-in so drafting happens where lawyers already draft. Harvey leads with a document-vault model for bulk workflows alongside pre-built workflow templates, and has publicly announced a content alliance with LexisNexis. Thomson Reuters CoCounsel competes in the same category with deeper ties into Westlaw and Practical Law. For a small firm the useful question is narrower than “which is best”: which one connects to the DMS you actually run, and what does its smallest deployment cost?

The general assistant, connected. Claude, ChatGPT and Copilot are not legal products. They become useful on legal work when you connect them to your systems through MCP — the Model Context Protocol, an open standard for giving an assistant governed access to specific tools and data. Support is not uniform: Anthropic’s Claude supports MCP natively (Anthropic published the standard), and OpenAI and Microsoft have both announced MCP support in their agent and Copilot tooling — but connector maturity, admin controls and audit logging differ materially between them, so check current vendor documentation before assuming parity. Instead of uploading files to a legal workspace, you let the assistant read the matter in Clio via an MCP connection and write back a draft, a time entry or a note.

The custom agent. Code that runs a multi-step workflow unattended — pull new filings, classify them, name them to your convention, file them to the right matter folder, queue a deadline for a human to confirm. Nobody sells this for your exact intake form or your exact trust-accounting quirk, which is the whole point.

Legora / Harvey (platform)

Strengths: Legal-specific UI your associates understand on day one. Review and citation handling built for lawyers. Vendor carries security review, SOC reporting and model governance. Fast to value for research, diligence and bulk document Q&A.

Limits: Pricing is quoted rather than published, so you cannot know the floor without asking — treat any secondhand claim about seat minimums, including ours, as something to confirm on the call. Your workflows bend to the product. Your own systems are reachable only through connectors the vendor built. Exit means leaving behind prompts and review templates refined inside their walls.

Claude + MCP (assembled)

Strengths: Works against the systems you already pay for — practice management, DMS, email, billing. Skills (reusable packaged instructions) are files you own and can version. Per-seat cost is ordinary assistant pricing plus your build time. Easy to extend one workflow at a time.

Limits: Someone has to build and maintain the connection, define permissions, and write the skills. No legal-specific interface — it’s a chat window with power behind it. You own the governance burden the platform would have carried. Weaker out of the box for large-scale structured review.

A worked example: sixty contracts and a Friday deadline

A 14-lawyer firm takes a mid-market acquisition. Sixty-plus supplier agreements need change-of-control, assignment, term and exclusivity pulled into a grid by Friday.

On a platform, this is the native use case: define columns, load the set, review the grid, spot-check every cell that drives a deal point. Assembling an equivalent yourself is real work — assume a few developer-days to chunk the PDFs, run per-column extraction, write results to a sheet and give the reviewer a way to click back to the source clause, plus more if you want citation-level linkbacks. Those are planning assumptions, not a quote. Against a Friday deadline on a single deal, the platform wins on time-to-answer.

Now change the job. The same firm wants an assistant that answers “what’s outstanding on the Alvarez matter?” by reading open tasks in Clio, the last three emails and unbilled time. No platform knows your matter hygiene, task naming or billing quirks. That is a custom MCP server over your own matter data — the workflow a subscription will never cover.

Buy the platform for work that looks like every other firm’s work. Build for the work that looks like yours.

Modeling the payback without making numbers up

Ignore any vendor figure about hours saved until you’ve measured your own. Model it with your inputs:

(hours on the task today − hours after) × your realization-adjusted rate × matters per month − monthly software and build cost

Here is the shape of the answer, with every assumption labelled. Say a diligence abstraction takes 6 hours today and 2 hours with tooling (4 saved). Say your realization-adjusted rate is $250. Say you run 3 such matters a month. Say the build costs $12,000 once, amortized over 12 months ($1,000/month), and software is whatever your quote says. Recovered capacity is 4 × $250 × 3 = $3,000 a month, minus $1,000 build amortization, minus software. Then add captured revenue — time entries that would have gone unrecorded — which is often the larger line.

6 → 2 hrs
per-matter hours in the illustrative model above
Worked-example assumption, not measured data
2–3
matters to pilot on before any firmwide rollout
Illustrative planning range, not a benchmark
4–8 wks
planning window to judge a legal AI pilot honestly
Illustrative planning range, not a benchmark

Where every option still needs a lawyer

ABA Formal Opinion 512 (2024) addresses generative AI directly, covering competence, confidentiality, communication with clients, supervision, and fees. Model Rule 1.1, Comment [8] already requires keeping abreast of the benefits and risks of relevant technology. Your state bar may have issued its own guidance that differs — check it as the controlling source, and confirm with a qualified professional before changing how you bill for AI-assisted work.

Our editorial position, offered as opinion rather than a measurement: sampling is fine for internal triage, but every piece of AI output that reaches a client deliverable needs lawyer review — all of it, not a sample. The failure mode is rarely an invented case. It is the model being quietly, plausibly wrong about an exclusivity clause in document 43. Platforms and assistants both fail this way, and the discipline that holds is the one in our document review skills piece.

How to decide in thirty days

  1. Name one workflow, not a strategy

    Pick a repeating job with a clear output — diligence abstraction, discovery triage, matter status answers. “Adopt AI” is not a workflow.

  2. Classify it

    Does it look like every firm’s version of this job? Lean platform. Does it depend on your systems, forms and naming? Lean assistant + MCP or a custom build.

  3. Price and vet both honestly

    Platform quote versus assistant seats plus a scoped build, with maintenance included on the build side — connectors break when vendors change APIs. On the buy side, run the trial properly and work through a vendor security, SOC 2 and data-handling review before signing.

  4. Run a parallel pilot

    Two live matters, same work, one lawyer reviewing both outputs. Record time actually spent, not time you felt you saved.

  5. Decide on fit, then governance

    Before rollout, settle who can approve write actions. Our read-only vs write access breakdown is the shortest version of that conversation.

The money flowing into Legora, Harvey and their competitors is real, and it will keep improving those products. It is not evidence that your firm needs one. Pick the workflow first, and let the shape of the workflow pick the tool.

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