TypeSafe AI
TypeSafe AI provides a new class of structured AI models called System One Models that produce type-safe, probabilistic decisions instead of text strings. Its first model, Jev, delivers calibrated confidence scores with sub-100ms response times at roughly 238x lower input cost than leading LLMs, enabling direct embedding into enterprise software as reliable, function-like decision modules.
- Artificial Intelligence
- Enterprise Software
- Developer Tools
- Software Only
Funding
Raised to date
$910MRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
across 2 rounds
$870M · Series A
Announced Oct 2026
Disclosed cumulative: $910M
Founders
Founder details are not available yet.
Product
Problem
Enterprises face high friction when trying to embed advanced AI capabilities into legacy software stacks. Existing large language models are treated as black-box services, leading to integration complexity, unpredictable latency, and limited control over decision logic. This hampers reliable automation and scaling of AI-driven business processes.
Solution
TypeSafe AI delivers a new class of Structured Artificial Intelligence (TAI) models designed for direct embedding within traditional codebases. The platform exposes type-safe APIs and composable intelligence modules that can be invoked as native functions, enabling deterministic decision making without extensive orchestration. Models are packaged with versioned contracts, runtime monitoring, and automated governance to ensure consistent behavior across production environments. A cloud-hosted inference layer provides low-latency, high-throughput serving while maintaining enterprise-grade security and auditability. Developers can integrate these modules using familiar SDKs, allowing existing applications to gain AI-driven capabilities without rewrites or heavyweight middleware. The solution also includes a dashboard for model performance analytics, drift detection, and policy-based access controls, supporting scalable deployment across large organizations.
Target Audience
Primary customers are engineering and data science teams at large enterprises that need to augment existing software systems with reliable, scalable AI decision logic—such as fintech platforms, supply-chain management solutions, and automated compliance tools.
Features
- Structured AI model format with explicit type contracts for seamless function-level integration into Java, Python, and Go codebases
- Composable intelligence primitives (e.g., routing, scoring, anomaly detection) that can be chained to build complex decision pipelines
- Versioned model artifacts with built-in rollback, canary deployment, and automated compliance checks (GDPR, SOC 2)
- Low-latency inference service backed by auto-scaling GPU/CPU clusters, delivering sub-100 ms response times at enterprise scale
- End-to-end telemetry: real-time metrics, drift monitoring, and automated alerting via a unified observability dashboard
- Role-based access control and end-to-end encryption for data in transit and at rest, meeting strict security standards
- SDKs and CLI tools for CI/CD integration, enabling continuous delivery of updated AI components alongside application code
- Policy engine for deterministic fallback logic, ensuring safe operation when model confidence falls below configurable thresholds