Self-Hosted AI eDiscovery Software and Services for Mid-Market Law Firms

Yes — it is, in practice, more defensible than vendor SaaS for privileged content. 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. No privileged document, embedding, prompt, or output ever crosses to a vendor LLM provider. The audit log is firm-owned and exportable. Combined with SSO, matter-level access control, and an AI-layer mirror of the firm's ethical walls, the posture meets the standard set by ABA Formal Opinion 512 and the inadvertent-disclosure variants of Model Rule 4.4(b).

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. For TAR 2.0 the classifier keeps learning from every reviewer override. Model coefficients are matter-scoped and never pooled across cases. The audit log captures every state transition, so the firm can defend the predictive coding decision if it is challenged at trial. Predictive coding AI supports recall and precision benchmarking against linear review on the matter's labeled hold-out set.

The vendor SaaS platforms are excellent for routine review against non-privileged content, with mature reviewer UIs and well-known production formats. Their AI is no longer an upsell. aiR for Review and aiR for Privilege ship inside the standard RelativityOne offering in 2026, and DISCO's February 2026 platform includes Cecilia AI and agentic eDiscovery at a single per-GB price on processed data with no ingest fees. Where they struggle is the part this stack solves: data residency (the LLM provider becomes a sub-processor on every matter), audit ownership (the firm cannot inspect the vendor's full logging schema), predictive-coding transparency (vendor model coefficients are opaque), and outside-counsel guidelines forbidding client data being routed to vendor LLMs. The self-hosted alternative reproduces the workflow inside the firm's perimeter — same predictive coding, same LLM-driven summaries, same AI redaction — with the privilege and audit posture the bar expects.

Yes — the audit log lives in the firm's tenant under the firm's retention policy. Every model invocation, retrieved chunk, predictive-coding state transition, AI redaction event, and reviewer override is captured with timestamp, user, model version, matter ID, privilege tier, and citation chain. The format mirrors what opposing counsel and the court expect on a TAR challenge or a privilege dispute. The log is exportable on demand and reviewable line by line; nothing is gated behind a vendor contract.

The standard rollout is twelve weeks across four phases. Discovery (weeks 1-2) maps the firm's matter mix, custodian profile, and privilege-tier taxonomy. Pilot (weeks 3-6) deploys the stack against one representative matter and benchmarks predictive coding recall and precision. Production (weeks 7-12) hardens against the firm's SSO, ethical walls, OCG requirements, and SOC 2 documentation. Continuous improvement (ongoing) extends the predictive-coding seed library across matters and tunes LLM review prompts as case law and bar opinions evolve. Small firms running 1-3 matters typically hit production faster; large litigation teams with multi-terabyte collections take the full twelve.

Vendor eDiscovery pricing is per-gigabyte and usage-based, and the AI is now folded into the base rate rather than billed separately. DISCO moved to one transparent per-GB price on processed data with no ingest fees in February 2026, and Relativity includes aiR for Review and aiR for Privilege in the standard RelativityOne offering in 2026. That is simpler, though the bill still scales with every matter and custodian the firm collects. A self-hosted stack inverts that curve. The firm pays once for GPU capacity, deployment, and tuning, then runs unlimited matters against it. The crossover point depends on collection volume, so litigation support teams carrying multi-terabyte collections across concurrent matters reach it fastest.