AtlasAI is an AI platform for law firms and in-house legal teams that integrates document analysis, legal research, enterprise search, drafting support, and agentic workflow automation within a single environment. All capabilities draw from a shared, per-tenant knowledge graph built from the firm's own documents and matter history.
Key Features & Functions:
The Foundation
Ontology and Knowledge Graph
All firm data is mapped and normalized onto an ontology the firm defines. Ontologies are expressed in natural language, established at the global level and extended by practice area, and created or revised on demand; the firm is not required to adopt a pre-existing legal taxonomy. The resulting graph organizes matters, parties, clauses, obligations, clients, and defined terms, and every answer the platform returns is grounded in it and cited to source.
Enterprise Search Across the Corpus
Search runs across the firm's connected document management systems, email, and file shares as a single corpus, ranked by relevance, recency, and matter context, and navigable through the graph. Users may pose a question in natural language, for example surfacing every share purchase agreement that references a given term, and move between matters, documents, parties, and concepts in a visual interface. Documents may be opened in their native system or drawn directly into an AI workflow.
Agents that Build and Reason
Ingestion agents read from connected sources continuously. A component the firm configures, the Librarian, works alongside them to extract entities and relationships, resolve synonyms, eliminate duplicates across matters, and map each document to the ontology, discarding what does not fit, so the graph remains structured as it grows. A confidence-based review queue preserves human oversight before data is committed. Research agents and multi-step workflow agents then operate over the curated graph, and, through the Reframe harness, a firm may connect its own coding agents to it.
Capabilities
Research and Drafting
Attorneys may pose substantive questions and receive answers cited to the firm's own documents, precedent, and prior treatment, and may draft agreements within the platform or directly in Microsoft Word. Every draft passes an adversarial verification step before it reaches the attorney. Tracked changes are native, and documents export to Word, PDF, or Markdown.
Document Analysis and Tabular Review
Instruments may be examined for clause extraction, risk, non-standard provisions, and omissions, with side-by-side deviation reports. A defined set of questions may be applied across a collection of documents, returning a sortable, citation-backed table that can be interrogated conversationally and exported.
Legal Research
Research agents produce cited memoranda across statutes, case law, and regulation, joined to the firm's prior treatment of the same questions, with case-law verification supported through integrations including Lawstronaut.
Playbooks and Assistants
Firms may codify approved positions and preferred language into playbooks applied to documents in real time, and configure purpose-built assistants with defined instructions, data sources, and output formats, assignable by practice group.
Word Plugin
Within Word, an attorney may select a clause, retrieve comparable language from the firm's own history through the graph, review recommended approaches, and redline against the applicable playbook, without leaving the document.
Workspaces and Agent Workflows
Documents may be organized by matter and subjected to multi-step pipelines that chain research, extraction, comparison, and reporting into complete work product, including due diligence checklists, memoranda, and gap analyses, from a single instruction. Workflows may be saved to a library and reused.
Document Management and Data Analysis
AtlasAI connects natively to iManage, NetDocuments, SharePoint, OneDrive, Outlook, and file shares, and honors existing access controls through ingestion that operates under each user's permissions. Structured data, including billing and transaction records, may be analyzed for patterns and anomalies and modeled for scenario analysis.
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