typedef

What typedef does

Typedef builds AI agents for data engineering teams, automating tasks like impact analysis, root cause debugging, model refactoring, report reconciliation, and semantic validation. The core innovation is the Data Context Layer, a continuously-updated understanding of the entire data stack, providing system-level context for reliable agent operation. This layer is a semantic ontology compiled from code, covering definitions, pipelines, dashboards, and systems across the data platform, ensuring it's always current and traceable. Typedef aims to provide a trust layer for AI on enterprise data, enabling teams to hand more of the platform to AI without sacrificing correctness.

Key capabilities

• Data Context Layer: A live, connected map of every definition, pipeline, dashboard, and system across your data platform, always current, always traceable.
• Cross-system lineage: Spans systems of record, streaming infrastructure, and offline analytics.
• Semantic resolution: Metrics and definitions are first-class nodes; identifies divergence when logic conflicts.
• Impact tracing: Traces any code change through models, pipelines, and dashboards to show the full blast radius.
• Enterprise-Grade Security: SOC 2 Type II + GDPR compliance, VPC Deployment, Secure Encryption (SSO, RBAC, audit trails), No Data Egress.
• Automated root cause and recovery: Traces upstream changes causing pipeline failures and generates fix plans.
• Metric reconciliation: Traces computation paths to identify where logic splits between systems.
• Agent tracing: Traces agent answers through semantic definitions, models, and sources to identify mismatches.
• Semantic search: Helps find the right data by searching semantic catalog and ranking results by relevance, freshness, and usage.

Use cases

• Impact analysis before merging code (blast radius analysis)
• Root cause debugging for pipeline failures
• Metric reconciliation across different systems
• Safe change plans with risk analysis and verification steps
• Certified metrics comparison across models
• Trustworthy agentic analytics with explainable answers
• Automated fix plans for broken pipelines
• Tracing agent answers to identify root causes of surprising results
• Resolving ambiguous agent answers by routing to correct semantic views
• Finding the right data for BI analysts

AI approach

Typedef builds AI agents for data engineering teams, automating tasks like impact analysis, root cause debugging, model refactoring, report reconciliation, and semantic validation. Their core innovation is a "data context layer" – a continuously updated understanding of the entire data stack, which provides system-level context for agents to operate reliably. This context layer is compiled from code and used to check AI answers and changes, improving task success rates. They do not claim to build the foundational AI models themselves, but rather provide the context for existing AI agents to perform better and more reliably.

Tech named: AI agents, data context layer, knowledge graph, semantic ontology, lineage context, AST (Abstract Syntax Tree)

What it says sets it apart

• Data Context Layer provides comprehensive, continuously-updated understanding of the entire data stack.
• Achieves 82% task success rate for AI agents on real-world data engineering tasks with context, compared to 46% without.
• Compiles the graph deterministically from code's AST, ensuring it cannot drift from implementation.
• Runs entirely inside the customer's environment with no data egress.
• Needs only read-only access to metadata and runtime signals, never stores raw data.
• Connects and unifies context across various data tools that typically only see one layer.

Funding rounds we track

From the AI funding tracker — rounds as reported by the linked publications.