LeasePilot + Harvey and Legora: Where Each One Belongs in Your Lease Workflow

Some teams arrive with a version of the same question. Should we adopt Harvey or Legora instead of LeasePilot?

It's a reasonable question to ask out loud. It's also framed wrong. The word doing the damage is "instead."

Harvey and Legora are not competing with LeasePilot for the same job. They're competing for a different one. Once you separate the jobs, the choice stops being either/or, and a single answer falls out on its own.

This is the complementary case. If you want the head-to-head version, that lives in LeasePilot vs. AI lease drafting tools. This piece is about the stack, not the fight.

Harvey is a legal AI platform used across the Am Law 100 and by more than 500 in-house legal teams. It ships a handful of products that work together: Assistant for chat, drafting help, and document analysis; Vault for review across a set of documents; Knowledge for legal research with cited sources; and agents that chain steps into a workflow. Attorneys reach for it on research, due diligence, and first passes at language.

Legora (formerly Leya) is a collaborative legal AI platform used by more than 800 firms and in-house teams. Its tabular review treats a set of documents like a spreadsheet you can interrogate, row by row. Its monitors scan regulation and surface what changed. Lawyers use it to review, research, and draft together in one place.

Both are strong. Neither is a "negotiation tool," and it would be lazy to label them one. The honest description is more useful: each is a probabilistic generalist. It reads, summarizes, drafts, and compares across many document types, and it earns its seat wherever "approximately right, then an expert checks it" is genuinely how the work gets done.

That describes most of the legal workflow. It does not describe one specific job.

Lease work divides into two kinds of task, and the divide is sharper than most tool comparisons admit.

First-draft production. Turning a set of deal terms into a complete, internally consistent lease draft. Your form, your fallback provisions (the pre-approved alternative language you fall back to in negotiation), your exact rent math, the decisions your team has already made about how this landlord papers a deal.

Everything downstream. Redlining the counterparty's markup, researching a novel provision, comparing a clause against your library, supporting the negotiation, running due diligence across a portfolio during an acquisition.

The downstream tasks are analysis. The output is informational. A human expert is already in the loop, reading closely, exercising judgment. That's exactly the shape where a probabilistic generalist shines, and it's where Harvey and Legora belong. I've made the longer version of this argument in what AI can and cannot replace in commercial leasing.

First-draft production is a different animal. It's a production task, not an analysis task. And it has two properties that break the "approximately right, then check it" model.

A commercial lease is not generic prose. Your form reflects years of negotiation, positions your team calibrated deal by deal, and calculations that have to be exact: rent escalation (the built-in rent increases over the term), CAM (Common Area Maintenance, the shared operating costs a tenant reimburses), pro-rata share (the tenant's percentage of those costs), percentage rent breakpoints (the sales threshold above which a retail tenant pays extra rent), TI allowance (the tenant improvement dollars the landlord contributes to build-out).

An LLM is not grounded in any of that. It was trained on a statistical blend of everyone's language. Ask it to produce your first draft and it draws on documents from other landlords, other property types, other jurisdictions, other risk tolerances. The output reads like a lease. It just isn't your lease.

You can narrow the gap by feeding the model your forms as context. You can't close it. The model still predicts the most probable next token rather than applying the rule your team wrote down. Grounding by suggestion is not the same as grounding by construction.

LeasePilot is grounded by construction. The draft is assembled from your approved language, your fallback provisions, and your conditional logic, with the calculations computed deterministically from the deal terms. Same inputs, same correct output, every time. That's the first-draft advantage: grounding by construction, not by suggestion.

Here's the property that settles the question.

Downstream, an expert reads the AI's output anyway. That's the job. The reading isn't overhead; it's the work. So a 95% accuracy rate helps, because the human was going to check it regardless.

First-draft production doesn't work that way. The point of a first draft is that most of it is settled and you move on. If you can't trust the draft until you've re-read all sixty pages, checked every calculated number, and confirmed every cross-reference, then the model didn't produce a first draft. It produced something you now have to audit line by line.

That audit erases the time the model saved. Draft in seconds, verify for hours. A probabilistic first draft has to be read with the same suspicion you'd bring to opposing counsel's markup, because you can't tell by looking which 5% went wrong. The one number that's off is embedded in a document that reads perfectly. I broke down this trade-off in the practitioner's guide to automation versus AI.

A deterministic draft carries no such tax. Every word came from language you approved. Every number came from arithmetic your team validated. You know where it all originated, so you can trust it without re-deriving it.

Now the division of labor is clean enough to draw.

Look at the table and the answer writes itself. A team that runs both gets the deterministic specialist for the one job that demands it and the probabilistic generalist for everything else. That's the strongest stack, and no line item overlaps.

So the honest recommendation is to run both. Not because budget is unlimited, but because the tools do different jobs and both jobs are real. Harvey and Legora make your attorneys faster at the analysis. LeasePilot makes the first draft correct before anyone reads it.

And if you can only fund one tool for the first draft this year, the choice isn't close. A probabilistic generalist cannot produce a lease grounded in your exact template and calculations that you don't have to re-read to trust. That's the definition of the job, and it's the definition of a deterministic system. Fund the draft with LeasePilot. Add the generalist for the analysis when you're ready.

Harvey and Legora are worth the attention they get. They're strong where legal work is probabilistic, and most of it is.

First-draft lease production is the exception. It has to be grounded in your forms, exact in its math, and correct before anyone reads it. That's a deterministic job, and it stays with LeasePilot. Teams that draft their first version in under 30 minutes do it on their own language, their own calculations, their own prior decisions (see what that changes).

Different tools for different jobs. The first draft is the one an LLM can't own.