Ertas for Contract Review & Analysis

The Challenge

Contract review is one of the most time-consuming and high-stakes activities in legal practice. Attorneys and paralegals manually review every clause of incoming contracts against their organization's approved positions, standard playbooks, and regulatory requirements. A single missed non-standard indemnification clause or an overlooked change-of-control provision can expose the organization to millions in liability. Yet the volume of contracts continues to grow: procurement agreements, vendor contracts, partnership deals, NDAs, and employment agreements pile up faster than legal teams can review them.

Generic AI legal tools provide surface-level contract analysis that identifies clause types but cannot evaluate them against an organization's specific standards. They flag that an indemnification clause exists but cannot determine whether it is mutual (as your playbook requires) or one-sided (a deviation that needs negotiation). This lack of organizational context makes generic tools useful for clause identification but inadequate for actual contract review, the part that consumes most of a lawyer's time.

The Solution

Ertas enables legal teams to fine-tune contract review models on their organization's specific contract playbook, approved positions, and historical redline patterns. The model learns what each clause should look like according to your standards and can identify deviations, missing provisions, and risk areas with explanations that reference your specific policies. This turns contract review from a clause-by-clause manual comparison into an AI-assisted process where the model handles initial review and attorneys focus on judgment calls.

With Ertas Studio, legal teams train on annotated contracts where each clause is labeled with whether it matches the playbook, the nature of any deviation, and the recommended response. In Data Craft, Studio's guided dataset builder, attorneys edit these examples, keep versions, and approve them before training. The trained model can then review new contracts and produce a structured risk report: a clause-by-clause comparison with the playbook, flagged deviations with severity ratings, and suggested negotiation language drawn from previously approved redlines. Training runs on Ertas's cloud GPUs, so the annotated contracts are uploaded to Ertas; remove counterparty names and deal terms the model does not need first. After export, the model runs on the legal team's own servers through Ollama, llama.cpp or vLLM, so the contracts it reviews day to day stay in-house.

Key Features

Example Workflow

A technology company's legal department processes 300+ vendor contracts quarterly. The team annotates 2,000 historical contracts with clause-level assessments against their 85-position contract playbook, strips counterparty names and commercial terms the model does not need, and uploads the dataset to Ertas Studio. They fine-tune a model that takes contract text as input and outputs a structured review memo: each clause is identified, classified, compared against the playbook position, and flagged as matching, minor deviation, or material deviation. Suggested redline language from previously approved negotiations is included for material deviations. Before relying on it, the team compares candidate runs against a set of recently reviewed contracts held out from training. The chosen model is exported and run on a server behind the corporate firewall. During the next quarter, the model pre-reviews incoming vendor contracts, producing draft review memos that paralegals validate and attorneys finalize, and the deviations attorneys correct are added to the dataset for the next run.