Building a Firm's Knowledge Graph in Real Time: How AtlasAI Singularity Curates Your Data

The Problem with Static Ingestion

Most legal AI platforms treat data ingestion as a one-time event: connect to iManage or SharePoint, copy documents into a vector database, run retrieval-augmented generation (RAG) against it. But law firms don't work that way. Documents are created, revised, linked, and reclassified constantly. Precedent libraries evolve. Matter scopes shift. A static snapshot of your firm's data becomes stale the moment it's built.

Worse, generic RAG retrieval doesn't understand your firm's taxonomy, your matter relationships, or how a clause in one deal relates to language in your standard templates. The AI responds like a search engine, not like a lawyer who knows your practice.

Singularity v6: Continuous Curation

AtlasAI Singularity is a new platform that solves this by treating your firm's data as a live, continuously curated knowledge graph. Instead of a one-time ingest-and-forget pipeline, Atlas deploys agents that read, classify, link, and refine your data as it lands.

Here's what actually happens:

1. Ingestion — We connect directly to iManage, SharePoint, OneDrive, NetDocuments, and email. New documents are staged and queued for processing the moment they land.

2. Agentic Classification — Atlas agents read each document, extract metadata (matter, client, practice area, document type), and classify it within your firm's taxonomy. This isn't keyword matching; agents understand context and intent.

3. Graph Construction — Classified documents are written into a per-tenant knowledge graph (backed by Qdrant for vector storage and a dedicated ingestion table for structured relationships). The graph records not just documents but links: this SPA relates to this indemnification schedule; this privilege log entry relates to this email thread; this clause variant relates to your standard template.

4. Continuous Refinement — As new documents arrive, agents re-examine existing graph nodes, spot duplicate precedents, detect relationships your firm hadn't yet catalogued, and strengthen the links. The graph improves over time, not decays.

What Shipped This Week

We released the knowledge-graph ingestion table that powers v6, plus staged uploads to `atlas-attachment-staging`. Documents are now queued reliably as they arrive in your firm systems, rather than processed ad hoc. We also hardened M365 integration with app-only Graph authentication, so multi-tenant ingestion scales without hitting consent walls.

These weren't sexy features in isolation — no new UI, no flashy buttons. But they're the backbone: the graph can't be curated if documents aren't flowing in reliably, and the graph can't stay fresh if we're fighting Microsoft authentication on every sync cycle.

How Firms Use This

An M&A team uploads 300 documents from a new deal into a Workspace. Atlas agents classify them (SPA, schedules, diligence responses, side letters), extract key terms (purchase price, reps and warranties, indemnification caps), and link them to the firm's precedent library — all in parallel, in minutes. As the agents work, they're also refining the graph: they notice a rep-and-warranty cap in this deal matches one from a 2024 precedent, so they link them. Now when a junior associate asks "what indemnification language did we use last time we capped at 10%?", the graph knows the answer not because someone manually tagged it, but because the agents stitched the relationship.

A contracts team uses Lists (curated libraries of clauses and templates scoped to practice areas) that are built from the graph. The graph has learned which clauses your firm uses, which ones are negotiated, which ones stayed boilerplate. Lists surface that knowledge as queryable libraries that agents and humans can pull from.

A litigation team runs a privilege review on 500 emails. Atlas agents pre-screen for attorney involvement and work-product markers, then curate those results back into the graph so the firm's privilege log and internal knowledge base both improve from the review work itself.

The Four Surfaces Built on Singularity

The curated graph is the foundation for four product surfaces, all live in v6:

Workspaces — Matter-scoped environments where agents run document review at scale (pre-review, substantive review, post-review), then draft new client documents from the findings.

Lists — Curated libraries of matters, precedents, clauses, and templates scoped to practice areas, queryable from chat and populated from the graph.

Agents — Both in-Workspace agents (document review, drafting) and KG-curation agents (reading and refining the graph itself).

MCP + API for Claude — Programmatic access to the firm's curated graph via the Model Context Protocol and a public REST API.

Each surface rides on the same curated knowledge graph. Each one is grounded in your firm's actual work, not a generic LLM.

Why This Matters

You can't outrun data velocity with static pipelines. As your firm generates more work — more deals, more cases, more discovery — the graph has to improve, not degrade. By making curation agentic and continuous, Singularity ensures that the longer you use Atlas, the sharper your knowledge graph becomes. And the sharper the graph, the better every downstream feature works: better retrieval, better drafts, better agent execution.

If you want to see how this works in practice, visit https://atlas-ai.io and request a demo of v6.