AI for Litigation, Self-Hosted Inside Your Firm's Tenant
AI for litigation from NeuralChainAI adds a private, self-hosted AI layer to the case management, document management and eDiscovery systems your litigation team already runs. Case chronologies, deposition summaries with page:line citations, document review support, motion and brief drafting support, and expert and medical record review all run inside the firm's own tenant, and every output cites its source for a lawyer to verify.
What AI for Litigation Covers Across a Case
One private AI layer serves the litigation team from the first document review to trial preparation, with each workflow drawing on the same case data inside the firm's tenant.
Case Chronologies
The AI layer reads pleadings, correspondence, records and transcripts and drafts a dated chronology of events. Each entry links to the Bates number or transcript page and line it came from, and conflicting accounts of the same event sit side by side for the case team to resolve.
Deposition Summaries
Page-line, topical or narrative summaries drafted from the reporter's transcript, with every entry cited to page and line. Summaries re-run against the errata sheet after the witness reviews the transcript, and a paralegal or associate checks each one before it goes into the trial notebook.
Document Review Support
Issue summaries, hot-document memos and Q&A over the production set, working alongside the firm's eDiscovery platform. Review, coding and production stay in that platform, and the AI layer answers questions about what the coded documents say.
Motion and Brief Drafting Support
First drafts of statements of fact, discovery letters and brief sections built from the record, with each factual assertion cited to an exhibit or transcript. Authorities are flagged for checking in the firm's research platform, and the drafting attorney reviews before anything is filed.
Expert and Medical Record Review
Expert reports compared against the record and the expert's prior testimony, and medical records turned into treatment timelines with page cites. Inconsistencies go to the attorney preparing the cross-examination or the expert outline.
Inside the Firm's Tenant
The models, index, prompts and audit log run in the firm's VPC, on its own servers or air-gapped. Material covered by a protective order stays on infrastructure the firm controls, with per-matter access mapped to the firm's SSO groups and ethical walls.
Why Litigators Choose Self-Hosted AI for Case Data
A litigation file holds some of the most sensitive case data a firm handles: deposition transcripts, medical records, an opposing party's confidential production, expert drafts and work product. Much of it arrives under a protective order that limits who may see it. Hosted legal AI assistants process documents on the vendor's infrastructure, and firms add a self-hosted AI layer when the order, the client or the firm's own policy calls for case data to stay in the firm's environment:
One private AI layer across the litigation file.
Chronologies, deposition summaries, document Q&A, drafting support and expert review run in one deployment inside the firm's tenant, connected to the case management system, the DMS and the eDiscovery platform the firm already uses. Every output cites its source, and the audit log records who asked what, on which matter and against which model version.
Inside the AI Litigation Layer: 8 Capabilities We Deploy
Eight capabilities make up the AI litigation layer, from ingesting the case file to the audit log. Each one runs inside a single tenant boundary and connects to the systems the litigation team already uses.
Case file ingestion that keeps Bates numbers and page:line structure
Pleadings, productions, exhibits, medical records and reporter transcripts in PDF, ASCII text and Word formats come in from the DMS, the case management system and the eDiscovery platform's exports. Scanned pages run through OCR inside the tenant, Bates numbers carry through, and transcripts keep their page and line structure so every later citation points to a real location.
Chronology builder
The model extracts dated events, people and documents from the file and drafts a chronology the case team can sort by date, witness or issue. Each entry cites its source, and where two documents or witnesses disagree on a date or a fact, both entries appear with their citations.
Deposition summaries with page:line citations
Page-line, topical or narrative summaries, chosen per witness. A citation check confirms that each cited range contains the testimony it summarizes, uncited statements are flagged for the reviewer, and the summary re-runs when the errata sheet arrives.
Document review support alongside the eDiscovery platform
Hot-document memos, issue summaries and Q&A over the production set, drawn from documents the review team has already coded. Predictive coding, review and production stay in the firm's eDiscovery platform, and the AI layer reads from its exports.
Motion and brief drafting support
Statements of undisputed fact, discovery letters and brief sections drafted from the record, with each factual assertion cited to an exhibit or to a page and line. Cited authorities are flagged for verification in the firm's research platform, and the drafting attorney edits and signs.
Expert and medical record review
Expert reports are compared against the record, the expert's prior testimony and the opposing expert's report, and medical records become treatment timelines with page cites. Gaps and inconsistencies go to the attorney preparing the cross-examination or the expert outline.
BYO-LLM routing per matter
Self-hosted Llama, Mistral or Qwen via vLLM or Ollama for privileged and protective-order material, and the firm's enterprise API contract for non-confidential work such as public-record research. The litigator uses one interface, and the model behind it follows the matter's confidentiality setting.
Per-matter access, SSO and audit log
Each matter's index is scoped to its case team through the firm's SSO groups, and ethical walls carry into the AI layer. Every query, retrieved passage, draft and reviewer edit is logged with user, matter and model version inside the tenant.
Talk to a Litigation AI Engineer
Bring the firm's litigation mix (commercial, employment, product liability, personal injury), the systems the case team uses today, the protective-order terms the firm typically works under and the tasks you want AI to draft first. A scoping call comes back with a deployment shape, a model-routing plan and a pilot plan for one active matter.
Book a Strategy Session →When Litigators Add a Self-Hosted AI Layer
Case management systems, eDiscovery platforms and hosted legal AI assistants each cover part of the litigation workflow. Firms add a self-hosted AI layer across them at these points:
A self-hosted AI litigation layer gives the case team chronologies, summaries and drafts it can verify, built on the firm's own case data and run inside the firm's own tenant. The firm owns the deployment, the prompts and the audit log.
Frequently Asked Questions on AI for Litigation
AI for litigation applies large language models and retrieval to the litigation file: building case chronologies, summarizing depositions, answering questions over a document production, drafting motion and brief sections from the record and reviewing expert and medical records. A self-hosted deployment runs those workflows inside the firm's own tenant, and every output cites the page and line, Bates number or document it relied on so a lawyer can verify it before use.
The eDiscovery platform stays the system of record for collection, processing, review, coding and production. The AI litigation layer reads exports from it (coded documents, hot-document sets, transcripts and exhibits) and drafts chronologies, memos and summaries from them. Firms that also want predictive coding and LLM review inside their tenant add a self-hosted eDiscovery AI layer, which we build as a separate deployment.
Every entry carries a citation to the transcript page and line or to the Bates-numbered document it came from. An automated check confirms the cited range contains the statement before the draft reaches a reviewer, and anything uncited is flagged. A paralegal or attorney then verifies the draft against the source. ABA Formal Opinion 512 ties the right amount of review to the tool and the specific task, so the firm sets review levels per workflow, with full citation checks on anything that will be filed.
Cost depends on the number of active matters and transcripts, the systems in scope (case management, DMS and eDiscovery exports) and whether models run on the firm's own GPUs or in a cloud tenant the firm controls. The deployment is a one-time build plus hosting or GPU costs and an optional flat managed retainer, so the cost stays steady as the docket grows. A strategy session sizes the build for the firm's caseload.
Target: four to six weeks from kickoff to a case team using the layer on one active matter. The pilot connects the DMS and the eDiscovery export for that matter, ingests transcripts and key documents, and runs chronologies and deposition summaries alongside the team's existing work so the two can be compared. Further matters and workflows follow on the same deployment once the case team signs off.
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Learn more →AI Transformation Workshop
Half-day strategy workshop to map the firm's litigation workflows, case data sources and the right self-hosted deployment shape for the first matters.
Book a workshop →AI Strategy Session
30-minute scoping call. We'll talk through the firm's caseload, deposition volume, protective-order terms and current systems, then sketch the right private deployment.
Book a session →Legal AI Solutions
Custom AI and ML solutions for law firms, litigation teams and legal services providers.
See legal AI solutions →Ready to Add Private AI to Your Litigation Practice?
A 30-minute strategy call. We walk through the firm's caseload, deposition calendar, case data sources and the protective-order terms the team works under, then come back with a deployment shape, a model-routing plan and a pilot plan for one active matter.