Canvas Labs builds context graphs that unify an organization's scattered data, decisions, and people into a single queryable map. The company partners with clients to solve hard AI problems end-to-end, delivering production-ready systems rather than demos. Their approach has been applied to projects like a company-intelligence graph for Redseer that drafts due-diligence reports in about 30 minutes.
Funding
Funding not disclosed
Founders
Product
Problem
Organizational context is scattered across six disconnected tools—conversations, meeting notes, project management, CRM, document storage, and BI systems—each an island that doesn't share information with the others. This fragmentation means AI agents and employees cannot answer basic questions about project status, ownership, or decisions without manually stitching together data from multiple sources. The reasoning behind decisions, the "why," remains trapped in unstructured formats like Slack threads and meeting transcripts, invisible to the systems that could use it.
Solution
Canvas Labs builds context graphs: living maps of an organization's durable entities (people, projects, companies, products) and the relationships between them, kept current from the tools teams already use. The company partners with clients to solve hard AI problems from first principles through production, delivering working systems rather than proofs-of-concept. Their approach follows three principles: don't make models re-derive what structured data already states, freeze durable entities and hang changes off them as timelines, and use surrounding entities to resolve the same thing appearing under different names. Canvas Labs has delivered fifteen such systems in three years, from a global consulting firm to a consumer app serving a million people, with each build making the next one sharper.
Target Audience
Primary customers are enterprises with complex organizational structures—including consulting firms, market intelligence providers, and large corporations—that need AI systems capable of reasoning over their full context, as well as product teams building AI features that require organizational awareness.
Features
- Context graph architecture that unifies six source types (conversations, meeting notes, project management, CRM, document storage, BI) under a single taxonomy centered on a core entity like a project, matter, patient, or campaign
- Deterministic ingestion of structured data (Jira, CRM, BI tools) so models only handle genuine gaps rather than re-deriving known facts
- Entity resolution that tracks the same company or person across name changes over time (e.g., Grofers → Blinkit) using surrounding entities rather than general-purpose embeddings
- Timeline-based modeling that freezes durable entities and attaches changes as temporal layers, handling corporate restructuring and evolving org structures
- Production-tuned token and compute cost optimization so systems survive real-world usage, not just demos
- Build-and-transfer delivery model where systems are documented, hardened, and handed over to client teams rather than run as managed services