Introduction

The rise of AI has transformed how companies interact with documents. Yet for regulated industries – banking, insurance, law, compliance – the challenge goes beyond automation. These organisations need systems that don’t just extract or summarise text but understand it, with traceable, verifiable reasoning.

Many “document intelligence” solutions promise this. Few actually deliver the level of explainability and governance that regulated workflows require. We compared Glean, Cognee, Writer, and Lettria to see which platforms are best suited for enterprise-grade document analysis and retrieval.

The specific needs of regulated industries

Document workflows in finance, insurance, and law are complex. They combine diverse formats (PDFs, tables, annexes, scanned attachments) and domain-specific terminology.

For AI tools, that means five non-negotiable requirements:

1. Structured understanding of knowledge – beyond embeddings
2. Domain-level semantics – via ontologies and taxonomies
3. Traceability – ability to see where an answer comes from
4. Compliance and governance – auditability of data and models
5. Accessibility – business users must operate the platform, not just engineers

Comparison methodology

This analysis is based on publicly available product documentation, demo materials, and verified product descriptions as of October 2025.

Each vendor was assessed on five pillars relevant to regulated workflows.

Tool-by-tool analysis

Glean: Enterprise Search with Expanding Compliance Capabilities

Glean positions itself as an enterprise search assistant connecting to internal data sources (Google Drive, Jira, Confluence, etc.) and delivering context-aware search results and summaries.

Strengths: integration breadth, ease of deployment, productivity impact.

Limitations: lacks graph reasoning or domain ontologies. Traceability is limited to citation-level references.

Fit: well-suited for enterprise knowledge discovery; less adapted for document governance in regulated workflows.

Cognee: Developer Platform Combining Graph and Vector Search

Cognee offers a developer-oriented framework merging graph databases and vector stores. Its SDK lets engineers create semantic search or reasoning applications.

Strengths: open SDK, semantic graph design, and strong developer flexibility.

Limitations: no ready-to-use ontology editor or governance layer; relies on developer setup for schema and access rules.

Fit: promising for data science teams building in-house reasoning tools; less turnkey for compliance or audit teams.

Writer: Enterprise Generative Suite with Limited Document Reasoning

Writer markets itself as an enterprise generative AI suite focused on brand voice, content governance, and policy control. It integrates with corporate sources but targets content generation and quality assurance rather than document reasoning.

Strengths: enterprise-grade safety, policy control, and governance across writing workflows.

Limitations: no semantic ontology layer, limited document parsing, no reasoning traceability.

Fit: ideal for communication and operations teams; not designed for regulated document validation or auditability.

Lettria: Graph-Based Document Intelligence for Regulated Workflows

Lettria combines language models, vector embeddings, and a knowledge graph in its GraphRAG architecture.

Its Knowledge Studio provides no-code tools for ontology management, dictionary curation, and document enrichment.

Strengths:

• GraphRAG improves retrieval accuracy by linking entities and relations.
• Ontology and taxonomy editors let compliance or legal teams define domain semantics.
• Advanced parsing supports multi-column, tabular, and scanned documents.
• Traceable outputs show where each fact or insight comes from when provenance is enabled.
• Designed for financial, legal, and insurance workflows.

Limitations: fewer native integrations than Glean, but expanding through APIs.

Fit: ideal for organisations where provenance, consistency, and auditability are mandatory.

Key takeaway

Most document AI tools prioritise convenience or productivity over governance. Glean, Cognee, and Writer each bring distinct strengths -search, flexibility, or content safety- but none provide full compliance-grade explainability.

Lettria fills that gap. Its combination of knowledge graphs, ontologies, and explainable AI directly addresses the bottlenecks of regulated document workflows: trust, control, and traceability.

Conclusion

Regulated industries can’t rely on opaque LLMs or black-box summarisation tools.

They need systems that reason over data in a structured, explainable way.

If your business deals with legal documents, insurance contracts, or financial compliance records, the question isn’t “how fast can AI summarise this?” but “can I trust and verify the result?”

See how Lettria handles your own documents.

Request a demo and test it on real contracts, financial reports, or compliance frameworks.