01
AI Services
AI systems built to survive production.
Select the right use case, design the retrieval and agent architecture, and establish evaluation, governance, observability, and unit economics before scale exposes the gaps.
AI Challenges
A working demo is not an operating system.
02
Prompt/model changes without regression tests
03
Unclear data provenance
04
Untraceable bad answers
05
Security and governance added late
06
Cost per request unknown
Capabilities
The controls production AI needs.
01
Use-case & value framing
02
Retrieval & data architecture
03
RAG & agent engineering
04
Evaluation & regression
05
Observability & incident analysis
06
Responsible AI & governance
Production Controls
Make quality, risk, and cost measurable.
01
Offline evaluation
02
Online quality signals
03
Traceability
04
Access and data boundaries
05
Fallback and human review
06
Model/platform portability
07
Cost per request and business outcome
Engagement
Start where the pilot stopped.
01
AI opportunity assessment
02
RAG/agent architecture review
03
AI production readiness review
04
Evaluation harness build
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Governance & control design
06
Production delivery partnership
Connected Practices
Production AI depends on the systems around it.
Cloud
Cloud Services
Design the platform, operations, governance, and FinOps that production AI has to live within.
Infrastructure
AI Infrastructure
Plan accelerated-compute capacity with engineering and commercial discipline.