High-velocity AI engineering, built for production.
We design and build production AI systems that execute real work inside your company. Agents, workflows, integrations, and the infrastructure required to run them reliably.
From agents to domain AI systems.
Pick the modules you need. We build and integrate them into your environment so teams can ship safely and scale.
We integrate at the workflow layer, not just the model layer.
Agentic workflow systems
Agents that execute multi-step work with verification and human handoff where needed.
- Deterministic routing for high-risk actions
- State boundaries and memory control
- Tool execution with result verification
Document intelligence
Turn unstructured inputs into structured outputs that your systems can use.
- OCR, layout parsing, field extraction
- Schema normalization and QA review loops
- Human verification for edge cases
LLM orchestration
LangChain and LangGraph orchestration with strict tool calling and retrieval control.
- RAG with scoped retrieval and source controls
- Structured tool calls and schema validation
- Agent policies and approval checkpoints
Domain models and SLM routing
Workload-fit model strategy for precision and cost control, including local inference.
- Small models for routine extraction and classification
- Escalation to larger models only when needed
- Options for private or on-prem deployments
Evals and reliability
Engineering to prevent silent failures under drift, load, and edge cases.
- Golden sets and regression gates
- Fallback behavior and human escalation
- Continuous measurement and monitoring
Governance and control
Guardrails that let AI operate in sensitive environments with traceability.
- Least privilege tool access
- Audit logs for decisions and actions
- Policy gating for sensitive operations
Plug-in delivery. Short cycles. Shippable output.
We work like an internal team. You bring the workflow and constraints. We build production-ready increments with clear acceptance checks.
- System access and SMEs
- Security constraints and policies
- Acceptance checks for workflows
- Forward-deployed engineers
- AI systems architecture
- Optional ML and data specialists
- Working code you can deploy
- Guardrails, evals, and logs
- Clear runbooks for operation
Build AI you can run in production.
If your roadmap includes agents, documents, and real system actions, start with a pilot workflow that is governed, measurable, and cost-controlled.