Ertas for Legal

The Challenge

Law firms and corporate legal departments generate enormous volumes of contracts, briefs, memos, and case law research every day. Extracting patterns from this data — identifying unfavorable clauses in vendor agreements, summarizing depositions, or surfacing relevant precedent across thousands of filings — is exactly the kind of task large language models excel at. Yet generic models trained on internet text routinely misapply legal terminology, confuse jurisdictions, and produce citations to cases that do not exist.

The confidentiality problem is even more acute than the accuracy problem. Attorney-client privilege and work-product doctrine impose strict obligations on how legal data is handled. Sending client contracts or litigation strategy documents to a third-party API — where data may be logged, cached, or used for model improvement — creates privilege-waiver risks that no general counsel wants to accept. Many firms have blanket policies prohibiting the use of external AI services on client matters, leaving lawyers without access to the productivity gains the rest of the enterprise enjoys.

The Solution

Ertas lets legal teams build AI models that are both domain-accurate and privilege-safe. Using Ertas Studio, legal technologists can fine-tune foundation models on curated corpora of anonymized contracts, case law databases, and internal knowledge management content. LoRA adapters keep training efficient, so a mid-size firm can produce a contract-analysis model in hours rather than weeks. The resulting models understand legal citation formats, contractual boilerplate, and jurisdiction-specific terminology at a level that generic models simply cannot match.

Once trained, models are deployed entirely within the firm's own infrastructure — on-premise servers, a private cloud VPC, or air-gapped environments — using Ertas Cloud's private endpoint capabilities. Ertas Vault provides encrypted storage for training data and model weights, with role-based access controls that mirror the firm's existing ethical-wall structures. Every inference request and data access event is logged in a tamper-evident audit trail, giving compliance officers and ethics partners the documentation they need to satisfy bar association requirements and client audit requests.

Key Features

Example Workflow

A mid-size corporate law firm wants to accelerate contract review for M&A due diligence. The legal technology team exports 30,000 anonymized commercial contracts from the firm's document management system as a JSONL dataset and uploads them to Ertas Vault, which encrypts the data and scans for any residual PII. In Ertas Studio, the team selects a Mistral-7B base model from Hub and launches a LoRA fine-tuning run focused on clause classification and risk flagging. After three hours of training, the adapter is merged and deployed as a private REST endpoint on the firm's on-premise servers via Ertas Cloud. Associates on the deal team now upload target-company contracts to an internal review tool that calls the private endpoint, receiving clause-by-clause risk assessments in seconds. The model flags change-of-control provisions, indemnification caps, and non-standard termination clauses with 90% accuracy — reducing first-pass review time from days to hours while keeping all client data within the firm's infrastructure.