Model APIs
Send prompts and application context through one interface for structured inference workflows.
- Input
- Prompt + context
- Output
- Text or JSON
AI infrastructure, shaped for developer workflows.
A developer-focused layer for model requests, application context, tool execution, evaluation, and runtime visibility.
Vector embeddings
Represent application content as vectors for similarity, retrieval, and downstream analysis.
- Input
- Text or records
- Output
- Vector arrays
Context retrieval
Assemble source material into model context with explicit query, retrieval, and ranking steps.
- Workflow
- Query → retrieve
- Context
- Ranked sources
Tool calling
Expose application functions as typed tools and return structured arguments for controlled execution.
- Input
- Function schema
- Output
- Tool arguments
Model evaluation
Compare prompts, models, and outputs against task-specific datasets and review criteria.
- Input
- Dataset + rubric
- Output
- Run comparison
Run observability
Follow requests through the stack with traces, errors, and runtime signals in a shared view.
- Signals
- Traces + metadata
- Output
- Request history
Start with the request contract.
Choose the capability that matches the job, then define its inputs, outputs, and review path.