Percipient

Percipient is a technology-enabled ALSP offering outsourced legal operations support to corporate legal departments and law firms. The company combines AI-assisted software with attorney-led review workflows across document-intensive legal functions.

Key Features & Functions:

AI-Augmented Contract Review
Percipient provides outsourced contract review services using AI software paired with attorney oversight. Contracts are processed by an AI model trained to the client's playbook and fallback positions, which identifies clauses, flags potential issues, and generates redlines. A legal professional reviews and validates AI output before delivery. The service supports high-volume third-party paper contracts including NDAs, MSAs, SaaS agreements, leases, and vendor agreements. Contracts can be submitted via dedicated email, Slack, or Jira; API connections to CLM platforms are also available.

Managed Document Review
Percipient provides end-to-end managed document review services, including review protocol development, reviewer training, quality control workflows, and status reporting. The team includes attorneys experienced across multiple e-discovery platforms.

Subpoena Response and Compliance
Percipient manages the full subpoena response lifecycle (from collection and review through production) for governmental and litigation-related third-party data requests. The service includes AI-assisted identification and redaction of PII and PHI, customized workflow design, and a subpoena tracking tool with real-time project metrics. The company addresses HIPAA-related requests and Stored Communications Act (SCA) compliance matters.

Electronic Discovery and Digital Forensics
Percipient supports the preservation, collection, processing, and analysis of electronically stored information (ESI). Targeted device collection capabilities include data from smartphones, tablets, cloud services, and social media platforms.

Expert Evaluation, RLHF, and Fine-Tuning
Percipient offers legal AI data services, providing expert legal evaluation for reinforcement learning from human feedback (RLHF) and model fine-tuning workflows.