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Overview

An enterprise set out to modernize its legal operations with AI to speed up contract review and reduce manual effort. The potential was clear: faster turnaround, higher accuracy, and more time for strategic work. But despite multiple pilot attempts, the team struggled to move beyond experimentation. Tools were difficult to integrate, required external expertise, and raised compliance concerns about data exposure. As a result, progress stalled and the promise of AI remained out of reach within their own infrastructure.

The legal team faced mounting contract volumes and slow, manual reviews that delayed business processes. Previous AI efforts failed due to privacy risks, long deployment cycles, and lack of in-house expertise. The enterprise needed a secure, accurate, and efficient way to automate contract analysis without external dependencies or extra hires.

Challenges

Data Sensitivity & Compliance

Contracts contain regulated, confidential terms. Third-party AI was off-limits—training and inference had to stay on-prem with full auditability.

Generic Models, Poor Fit

Off-the-shelf/open-source models missed legal nuance. Real accuracy required in-house fine-tuning on internal documents without data exposure.

Infrastructure Overhead

Standing up training/serving stacks slowed progress—prototypes stalled before production due to orchestration, monitoring, and cost friction.

Solution

Full Data Sovereignty

Fine-tune and serve models inside your environment. Sensitive data never leaves your infra, meeting privacy and compliance requirements.

Simple, Reusable APIs

Plug into out-of-the-box endpoints—no orchestration or pipeline boilerplate. What was bespoke becomes fast and repeatable.

Smaller, Task-Specific Models

Train compact models for legal tasks to boost accuracy while cutting inference and infra costs substantially.

Faster Time to Production

Go from idea to deployment in days, not months—iterate safely with versioning, RBAC, logs, and monitoring built in.

Conclusion

This enterprise didn’t need another AI tool. It needed a way to securely build with the resources it already had—its own data, team, and infrastructure.

Protean provided exactly that: a unified, self-hosted platform to fine-tune, deploy, and scale AI in-house—with the simplicity, cost-efficiency, and performance that domain-specific legal use cases demand.

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Contract Review

Secure, Accurate, In-House Legal AI

Streamline contract review with AI fine-tuned on your own documents—achieving faster reviews, higher accuracy, and full compliance without sending data outside.

Manual reviews slowed procurement, compliance, and sales workflows. Legal teams had to scan high volumes of contracts for clauses, risks, and exceptions—consuming weeks of effort and creating approval delays.

Contracts contained sensitive terms, pricing, and obligations. InfoSec and compliance teams prohibited sending data to third-party AI services, so external SaaS solutions were off the table.

Generic models struggled with domain-specific language—contractual clauses, obligations, and risk markers. Fine-tuning on internal agreements was required to reach 90%+ accuracy.

They lacked ML/MLOps expertise and infrastructure. Toolchains were fragmented, proofs-of-concept were fragile, and setting up training environments required resources they didn’t have.

Protean runs fully in-house. Contracts never leave the enterprise’s infrastructure. Sensitive terms and documents are protected, ensuring compliance with internal and external regulations.

Using simple, reusable APIs. Teams integrated clause extraction, risk flagging, and classification directly into review tools—without managing infrastructure or writing pipeline code.

Task-specific models focused on clauses and risks achieved higher precision than generic LLMs, while requiring far less compute—cutting costs and latency.

With Protean, deployment cycles dropped from months to weeks. Fine-tuning and rollout of pilot workflows were achieved in days instead of quarters.

The legal team achieved 4× faster review cycles, 90%+ accuracy on clause extraction, and eliminated exposure of contracts to third-party services.

No. Protean allowed existing developers to fine-tune and deploy models. No new hires or costly consulting engagements were required.

Every prediction is explainable and logged. Models are versioned and tied to data snapshots, ensuring auditability and defensibility in compliance reviews.

No. Protean augments legal teams by automating routine checks and extractions. Lawyers remain responsible for negotiation, judgment, and approval.

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