Self-Hosted eDiscovery AI for Mid-Market Law Firms

Yes. Document ingestion, embedding generation, the vector index, predictive-coding training and LLM summarization all run inside the firm's VPC, on-prem environment or air-gapped enclave, so privileged documents, embeddings, prompts and outputs stay with the firm. With SSO, matter-level access control and an AI-layer mirror of the firm's ethical walls, the firm has a documented basis for its ABA Formal Opinion 512 evaluation and its Model Rule 1.6(c) duty to protect client information. The audit log lives in the firm's tenant under its own retention policy and records every model invocation, retrieved chunk, predictive-coding state transition, redaction event and reviewer override with timestamp, user, model version, matter ID, privilege tier and citation chain. It is exportable on demand for a TAR challenge or privilege dispute.

The litigation support team selects a regime per matter: TAR 1.0 (passive learning from a fixed seed set) or TAR 2.0 (continuous active learning). A partner or senior associate codes the seed set, the classifier is trained on those decisions and applied to the rest of the corpus, and under TAR 2.0 it keeps learning from every reviewer override. Model coefficients are scoped to each matter, and the audit log captures every state transition, so the firm can defend the predictive coding decision if it is challenged at trial. Predictive coding recall and precision are benchmarked against linear review on the matter's labeled hold-out set.

Relativity, Reveal, DISCO and Everlaw offer mature reviewer interfaces and well-known production formats on vendor-hosted platforms, and their AI now comes in the base offering. Relativity announced in October 2025 that aiR for Review and aiR for Privilege would become standard in RelativityOne, and DISCO's February 2026 platform bundles Cecilia AI and agentic eDiscovery tools. Everlaw added integrations with Harvey, Thomson Reuters CoCounsel Legal, Microsoft 365 Copilot and Google Cloud's Gemini Enterprise for Legal in August 2026. A self-hosted AI layer runs predictive coding, LLM summaries and AI redaction inside the firm's perimeter, with the audit log, model coefficients and review prompts in the firm's own systems, while the firm's eDiscovery platform stays in place for processing, hosting and production. Firms choose it when data residency, audit ownership or outside counsel guidelines call for matter content to stay in-house.

Target: twelve weeks across four phases. Discovery (weeks 1–2) maps the firm's matter mix, custodian profile and privilege-tier taxonomy. The pilot (weeks 3–6) deploys the AI layer against one representative matter and benchmarks predictive-coding recall and precision. Production (weeks 7–12) hardens the stack against the firm's SSO, ethical walls, OCG requirements and SOC 2 documentation. Continuous improvement then extends the seed library across matters and tunes LLM review prompts as case law and bar opinions evolve. Small firms running 1–3 matters often reach production sooner, and large litigation teams with multi-terabyte collections use the full twelve weeks.

Hosted eDiscovery platforms price processing, hosting and their built-in AI through their own plans, so the firm's current platform contract is the baseline. The self-hosted AI layer is a one-time build (deployment, predictive-coding setup and LLM review tuning) plus GPU or hosting capacity and an optional flat managed retainer, and it serves every matter the firm runs on that capacity. The firm's platform stays in place for processing and production, and a strategy session sizes the AI layer for the firm's docket.