AtlasAI Singularity: Building a Firm's Knowledge Graph

Why Firmwide AI Needs a Curated Foundation

When a law firm deploys AI across all practice areas, the biggest risk isn't the AI itself, it's the data layer. Generic large language models know nothing about your firm's precedent, deal patterns, risk appetite, or closed matters. They hallucinate clauses, miss nuance in your market, and repeat mistakes you've already fixed in your playbooks.

AtlasAI Singularity solves this by building a per-tenant knowledge graph: a curated, continuously refined corpus of your firm's own work. Every response Atlas generates, every document it reviews, every clause it extracts, every precedent it suggests, runs against that graph. Not against the internet. Not against a generic model's training data. Against your work.

What We Shipped in v6

Singularity v6 ingests data from five sources: iManage (your primary document management system), SharePoint and OneDrive (where many firms store client materials and internal playbooks), NetDocuments (for firms using that platform), and email (Gmail, Outlook) for implicit matter context. We released consumer OAuth for Microsoft 365 and Google Drive this week, so users can browse and import specific folders and documents into the knowledge graph without IT intervention. The cloud picker surfaces a structured view of your file system, lets you select what to ingest, and queues the sync.

Once documents land in the graph, Atlas agents take over curation. They read each document, classify it (contract, memo, research, closing checklist), extract metadata (parties, effective date, jurisdiction), link it to related matters and precedents, and refine the graph as new content arrives. This happens continuously in the background, not as a one-time batch job. When a new M&A SPA lands in iManage, agents classify it, link it to your M&A precedent list, extract key terms, and make it queryable within minutes.

How Continuous Curation Works

The graph itself is stored as a per-tenant collection, updated by agentic workflows that run on every new document. When you upload a contract to SharePoint, an agent:

1. Reads and tokenizes it 2. Classifies it (SPA, NDA, amendment, etc.) 3. Extracts structured data (parties, amount, date, key clauses) 4. Finds similar precedents and closed deals in the graph 5. Flags conflicts, missing schedules, or non-standard terms based on firm playbooks 6. Links the document to related matters and people

That enriched metadata lives in the graph. Downstream, when an M&A team runs a Workspace to review a new deal, the Workspace agents query that graph to pull precedent, compare to firm standard, and generate redlines. The curated graph is the source of truth for every analysis.

Practical Impact for Firmwide Rollout

For a Magic Circle firm (or any AmLaw practice deploying AI across multiple groups), Singularity means:

• One curated corpus, not scattered AI instances. Instead of each practice group training their own model or using generic tools, the whole firm queries one graph that reflects collective firm knowledge.
• Fast onboarding for new matters. When a litigation team opens a new file, agents automatically surface relevant closed cases, parallel precedent, and privilege-review patterns from the graph.
• Consistency across teams. M&A teams use the same SPA precedent library. IP teams query the same patent-prosecution playbook. Litigators see the same adverse-case history.
• No hallucination on firm work. Claude, Atlas Agents, or any MCP-aware tool can query the curated graph directly. They never guess about your precedent, deal history, or risk patterns.

What's Next

This week we also shipped the SharePoint sync connector and a mock iManage server for testing, so teams can validate their ingestion pipeline before production rollout. The cloud picker lets you select specific folders to ingest, not forced all-or-nothing imports. And the knowledge graph ingestion table is now live in v6, storing every classified document, extracted term, and cross-link so agents continuously refine.

Singularity is the foundation. Workspaces (matter-scoped agentic review), Lists (reusable practice-area workflows), Agents (autonomous multi-step execution), and Claude integration (direct graph queries via API) all run on top of it. The curated graph is what makes all of those surfaces work at firm scale.

If you're rolling out AI firmwide, the question isn't whether to use AI. It's whether to let a generic model guess about your work, or build a curated foundation that reflects your firm's knowledge.

See AtlasAI Singularity at https://atlas-ai.io.