Built for Scale
Distributed-system patterns, resilient error handling, microservices stability, and database scalability are enforced by engineers.
Enterprise AI Engineering
AI Engineering With Enterprise Rigor
AI-generated code is only valuable when it can live inside a production engineering organization.
Codified Project Context
Engineering standards become persistent context so AI workflows align with the customer's stack, conventions, and constraints.
Production requirements
The surrounding system matters.
System qualities
- Scalability
- Reliability
- Fault tolerance
- Security
- Observability
- Auditability
- Data governance
Engineering context
- Persistent project context
- MCP integration
- Existing engineering standards
- Existing CI/CD
Toolchain alignment
- Jira
- Linear
- GitHub
- Repository conventions
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Build an engineering workflow around AI.
Bring us the roadmap, repository, or production constraint. We'll show you where AI can safely create leverage.