Data Intelligence

Data lands in whatever shape the matter generates it. We unpack containers, walk the structures, recover the substance — and hash everything on receipt. The original is preserved bit-for-bit, every transformation is logged, and chain of custody is intact from the moment data enters the platform.

• Forensic images. E01/Ex01/AFF4 disk images, dd/raw, Cellebrite UFDR/UFD, Magnet AXIOM, GrayKey, and other mobile/computer forensic outputs. We mount, walk filesystems, and recover deleted/unallocated content where the chain of custody allows.
• Containers and archives. ZIP, RAR, 7z, TAR, ISO, VHD/VHDX, OST/PST, MBOX, EDB, NSF — recursively unpacked with depth limits, encryption-aware (passwords supplied or derived where lawful), and reconstructed back to attributable parents and custodians.
• Messaging. Email exports (PST, OST, MBOX, EML), chat archives (Slack, Teams, Discord, WhatsApp, Signal exports, SMS/MMS, iMessage), voicemail, and ephemeral messaging captures — preserving threads, reactions, attachments, edits, and deletion markers.
• Databases and tables. SQL dumps, Postgres/MySQL/SQL Server backups, SQLite from mobile artifacts, parquet/CSV/Excel exports, BI extracts, and ad-hoc tables — schemas captured, joins preserved, and rows rendered as auditable structured records rather than flattened text.
• Cloud storage. S3 buckets, Azure Blob, Google Cloud Storage, OneDrive, Google Drive, Dropbox, Box — pulled with versioning, ACLs, object metadata, and lifecycle state preserved, including tombstones for deleted objects where retained.
• Productions and native files. Concordance / Relativity / Eclipse productions (.DAT, .OPT, image stores), native Office, PDF, CAD, design files, transcripts (.PTX, .TXT), audio, and video — accepted as-delivered and reconciled back to load files.

Every artifact is hashed (MD5/SHA-256), tagged with custodian and source, and written into the chain-of-custody log. Encrypted containers, password-protected files, and access-controlled cloud sources are tracked explicitly — what was seen, what was decrypted, what was skipped — so nothing is silently dropped from the corpus.

Once data is ingested, an end-to-end AI intelligence pipeline turns raw evidence into a corpus your team and our agents can reason over.

• Deduplication, near-dedup, and email threading. Documents are normalized, hash-deduped, near-deduped, and conversations are reconstructed into clean threads — so a 100k-message custodian becomes a few thousand meaningful conversations.
• LLM OCR with vision models. Scanned pages, screenshots, photographed exhibits, and image-only PDFs are read by vision-capable language models — preserving layout, tables, headers, and footnotes far more faithfully than legacy OCR engines, and producing structured text rather than character soup.
• Audio transcription with diarization. Voicemails, calls, depositions, and meeting recordings are transcribed with speaker diarization — every utterance attributed to a speaker, time-aligned to the audio, and linked back to the source file.
• Image analysis and description. Photos, screenshots, charts, signatures, and embedded graphics are analyzed and described in natural language — turning otherwise opaque images into searchable context that downstream agents can reason over and attorneys can find.
• AI analysis and document summaries. Each document gets a structured summary — purpose, parties, key dates, key facts, salient excerpts — written into the metadata so reviewers can triage hundreds of documents in the time it would have taken to skim five.
• Chunking, embedding, and a vector database with agentic RAG. Every document is chunked along semantic boundaries, embedded with task-specific models, and indexed alongside the structured graph. Agentic RAG retrieves with grounding (entities, relationships, time windows, custodians) rather than free-text similarity alone — the foundation for accurate, citation-linked answers from any LexGo AI agent.

This is not the processing pipeline of legacy eDiscovery. Its purpose is not just to make documents searchable — it is to prepare your corpus for reliable intelligence. The output of this pipeline reveals insights, informs drafting and motion practice, sharpens strategic decisions, and feeds the winning narrative.

A coordinated set of screens runs during ingestion — not after — so by the time data lands in the repository it is already tagged with the handling rules that downstream review and AI workflows respect by default.

• Privilege screens. Counsel-of-record relationships, document types, communication patterns, and content signals are checked against the matter's privilege rules. Suspected privileged material is flagged for attorney review and auto-suppressed from downstream agents and reviewers until cleared.
• PII detection. Names, addresses, government identifiers, financial accounts, health information, minors' data, and jurisdiction-specific categories (HIPAA, GDPR, CCPA) are detected and tagged at the field level — enabling targeted redaction, masking, and access controls.
• Confidentiality screens. Trade secrets, NDAs in force, protective-order categories, and client-defined sensitivity tiers are recognized and propagated to every downstream view, so confidential material never silently leaks into a working set or production.
• Sentiment & tone signals. Each communication is scored for sentiment, hostility, evasion, and urgency — surfacing the small percentage of documents that often drive a case (the angry email, the conciliatory thread, the sudden change in tone) and giving attorneys a fast path to the substance buried in volume.

Every flag is reviewable, attributable, and auditable. False positives are tracked and tuned; nothing relies on a single black-box score, and attorneys see exactly which rule or model fired on which document.

The output of every Data Intelligence engagement is a deliverable, not just a process artifact: a single repository combining full-text search, structured metadata, and vector embeddings — handed to your matter team and engineered so that AI agents can retrieve with precision while attorneys can browse, filter, and search the way they always have.

The repository is the working surface for the rest of the case. It powers downstream review, motion drafting, deposition prep, and any LexGo AI agents you engage on the matter. Every retrieval is auditable back to the source document and the transformations applied to it, so anything that lands in a filing or expert report can be defended end-to-end.

On top of the repository we build a semantic layer: people, organizations, accounts, assets, transactions, communications, events, and the relationships between them — extracted from the documents and stitched into a case-specific knowledge graph. This is where raw data becomes case insight.

Every matter is different, so the schema is too. We start from a base ontology tuned for legal work and then extend it with custom entities and relationships specific to the case — counterparty roles in a contract dispute, expert specialties in mass tort, instrument classes in a securities matter, treating providers and procedures in a personal-injury portfolio. Attorneys and experts review the ontology before extraction runs at scale, so what is captured maps to how the team thinks about the case.

The resulting knowledge graph drives concrete deliverables — chronologies, communication maps, money-flow diagrams, witness influence networks — and powers grounded semantic search across the matter. Instead of guessing at keywords, attorneys can ask "every communication where Person A discussed Account B between these dates" or "every document that supports element X of claim Y" and get an answer linked back to the underlying evidence.

Because the graph is the substrate, downstream LexGo AI agents — for review, drafting, deposition prep, or strategy — retrieve against grounded entities and relationships rather than free-text alone. Hallucinations drop, citations sharpen, and every assertion can be traced back to the document, page, and span that supports it.

Many of the most valuable documents in a matter are also the hardest to parse — long, structured PDFs that mix text, tables, charts, graphs, and images, often with inconsistent formatting across files. Expert reports, financial statements, loan files, medical records, regulatory filings, technical specifications: each carries the substance of the case, but lives outside what generic OCR or text extraction can handle.

We use a two-stage parse & extract pipeline tuned for this work. The first stage segments and reconstructs the document structure — recognizing tables as tables (not as runs of text), graphs as graphs, image regions as images, and the relationships between them. The second stage runs targeted extraction against that structured representation: pulling line items from a P&L, exhibits from an expert report, payment schedules from a loan file, lab values and prescriptions from a medical chart.

Every extracted field is linked back to the page, region, and source document — defensible end-to-end and reviewable by an attorney or expert.

For a single high-stakes document — a 200-page expert report, a settlement appendix, a complex financial statement — we run on-demand AI analysis: structured extraction, summarization, cross-references, and Q&A grounded to the source.

When the same complex document type repeats across the matter — hundreds of expert reports in mass tort, thousands of loan files in a securitization, every medical record in a personal-injury portfolio — we fine-tune and train on a small sample, validate the output against attorney-reviewed gold-standards, and then run accurate extraction across the entire population. You get the precision of bespoke analysis with the throughput of an industrial pipeline.

Pricing can vary widely depending on the job — from $2.50 / GB for high-volume ingestion and processing to roughly $10 / doc for fine-tuned extraction across complex structured documents. Storage and active hosting are billed separately and scale to the matter footprint.

Every engagement starts with a complimentary scoping call. You receive a written estimate with the pricing model, expected volumes, and a not-to-exceed cap before any work begins — full transparency and predictable pricing before you commit.