The legal department of a large agro-holding was spending up to 50% of its time on routine document verification against 1С (1C) and ЕГРН (Russian real estate registry). The AI assistant extracts data, verifies against deterministic rules, and prepares packages — the legally significant decision stays with the lawyer.
The legal department works at the intersection of contract, corporate, land, and litigation practice. The land function bears the primary workload — thousands of lease agreements with private landowners, constant changes in ownership, verification of cadastral boundaries, and the currency of ЕГРН extracts.
The client is the legal department supported by the IT division, which is responsible for integration with 1С and for information security. The goal was to build a digital core for the function, on top of which modules can be added without rewriting the infrastructure.
Up to 50% of time is spent on mechanical operations: open a PDF, extract fields, compare against the 1С record, check the currency of the ЕГРН extract, assemble a package from a template. Qualified lawyers are occupied with low-qualification work, and the risk of missing a discrepancy in a cadastral number, share, or name is growing.
Documents arrive as PDFs of varying quality — from digital files to low-contrast scans. The approval routing has no unified status layer: the state of a task is visible only from its message thread.
AI covers three legal workflows: extracts fields from title documents, cross-checks against 1С (1C) and ЕГРН (state real estate register) using a rules matrix, and prepares document packages.
Deployed: authentication and role model, a case engine with status tracking, an OCR pipeline (PyMuPDF for digital PDFs, Tesseract for scans), an AI backend with a prompt registry and schema-based response validation, and an immutable audit log. All three processes run on a single infrastructure; a new case type is added as a module, not a separate application.
The lawyer uploads the draft agreement; the AI extracts the material terms (type, parties, subject, amount, term) and deterministically classifies it as a major transaction or related-party transaction, then assembles the approval package — the Board of Directors agenda, draft resolution, and document checklist. The cycle goes from several days to hours.
The AI extracts the full name, cadastral numbers, shares, and legal basis from the title document and ЕГРН extract, then pulls the 1С record. Six rules run on top of this (name match, ЕГРН confirmation, cadastral verification, ЕГРН↔1С reconciliation, share, extract currency ≤90 days) — triggering auto-approval, escalation, or blocking. The cycle goes from tens of minutes to 1–2 minutes.
Upon a payment overdue event from 1С, the system calculates the principal debt, contractual penalty, and court fee (under Art. 333.21 of the Russian Tax Code), generates a claim with legal grounds (Art. 309, 310, 330 of the Russian Civil Code), tracks the 30-day pre-trial deadline, and upon non-payment assembles the court filing package with attachments under the Russian Commercial Procedure Code.
The platform forms the digital core of the legal department, on top of which modules are added without rewriting the infrastructure — contract work, lease expiry monitoring, land portfolio analytics, counterparty event monitoring.
The department gains a work environment with a task kanban and full traceability — transitioning from reactive document flow processing to a managed process with predictable load and measurable metrics.
We were not looking for a standalone AI tool but a digital core on which we can build new modules. The key point is that AI does not make the legal decision on its own — it prepares data and drafts, and every action it takes is recorded in an immutable audit log. That is exactly what removed the risks for us.
The metrics and results presented reflect outcomes of a specific project and depend on its initial conditions. They are provided for informational purposes only, do not constitute a public offer, and do not guarantee similar results in other projects. Supporting materials are available on request.
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