Services
Production AI, engineered like serious infrastructure
Six focused service areas covering the entire lifecycle of an AI system — from architecture to ongoing optimization.
AI Architecture
End-to-end system design with explicit state, deterministic orchestration, and observability baked in.
- Reference architectures
- Tech selection & trade-offs
- Roadmaps and de-risking plans
LLM Applications
OpenAI, Claude, Llama, and bespoke APIs woven into real product surfaces.
- Structured outputs
- Prompt engineering & evaluation
- Streaming, tool use, function calling
RAG Systems
Federated retrieval over fragmented enterprise knowledge — grounded, secure, and current.
- Hybrid retrieval + reranking
- Permission-aware indexes
- Continuous evaluation harness
AI Agents
Stateful, validated, cost-aware multi-agent systems that hold up under real workloads.
- LangGraph orchestration
- Critique & self-correction loops
- Replayable execution traces
Workflow Automation
Operational AI woven into CRMs, ERPs, ticketing, and back-office systems.
- Process discovery
- Human-in-the-loop design
- ROI tracking & rollouts
Cloud Deployment
AWS, Azure, GCP, Docker — productionized with cost, latency, and reliability budgets.
- Infra as code
- Observability stack
- Scaling & cost controls
Not sure where to start?
A 60-minute architecture call usually clarifies the path forward — what to build, what to defer, and what to retire.