Legora

Legora, formerly Leya, is a collaborative AI platform for lawyers that combines legal research with verified citations, tabular contract review, and agentic workflows supervised by legal teams.

Pricing is consumption based for enterprises, with list rates reported around 3000 dollars per user per year and minimum commitments near ten seats, so access starts with a demo rather than a checkout page.

Legora spans the major common law and civil law markets across Europe, the Americas, and Asia, and multi jurisdiction research is a core use case for cross border firms.

Tabular review extracts clauses and data points from large document sets into a spreadsheet style grid, letting lawyers compare hundreds of contracts by row instead of reading each file start to finish.

The enterprise pricing model and minimum seats target firms and legal departments, so solo practitioners with light volume are generally better served by self serve legal research tools.

Agent Pro is the 2026 tier that pairs consumption pricing with the most capable agentic workflows, drafting and cross referencing documents under lawyer supervision.

Legal work demands strict confidentiality, so Legora offers enterprise agreements with controls designed for client matters and supports the security reviews firms require.

Lawyers share a collaborative canvas where research, contract excerpts, and Agent Pro outputs become joint work product, and tabular review spreads contract analysis across whole document sets for the team to divide. Pricing is quote based and consumption driven with no published rates, and the Financial Times reported in June 2026 that customers pay around $100,000 per year on average, which reflects the platform focus on firm wide adoption across jurisdictions rather than single seats.

Legora quotes enterprise pricing with no published list rates, and reported average contracts run around 100,000 dollars per year based on Financial Times reporting from June 2026. Pricing reflects firm size, matter volume, and which workflows are in scope. Treat the quote process as a scoping exercise: bring your document volumes, current research spend, and the use cases you want covered, because the contract is negotiated around those numbers rather than a per-seat menu.

The reported contract sizes point at large firms and in-house departments as the core market, and the collaborative features assume teams working shared matters. Solo practitioners are served better by per-seat legal research tools with transparent pricing. That said, mid-size firms handling cross-border work or high document volumes do reach this tier, since tabular review and verified citations pay off exactly when document counts and jurisdictions multiply.

Legora grounds its research citations in verified legal sources across its covered jurisdictions, so memos link back to the authority behind each claim rather than a bare model assertion. The practical value shows in review: a partner can check the cited source directly instead of re-researching from scratch. Still apply normal verification discipline, because coverage varies across jurisdictions and the platform works across multiple markets rather than one unified legal database.

Yes, tabular review is the designed workflow for volume: run a contract set through a table where each row is a document and the columns capture the clauses, dates, and terms you care about. It turns what used to be sequential reading into a sortable dataset, which is how in-house teams get through diligence or lease portfolios. Define your extraction columns before the first run, since the table structure determines what the review surfaces.

It runs alongside existing systems rather than replacing them: research, contract review, and agentic work happen on its collaborative canvas while filings, matter management, and billing stay where they are. Agent Pro workflows run under lawyer supervision, so the firm keeps control points. The adoption pattern that works is picking one repeatable work product, like a research memo or a first-pass contract summary, and standardizing that before spreading to riskier workflows.