AI Observability
See what your AI actually did, what it cost and where quality is slipping.
01 Approach
How we approach it
Tracing, evaluation and cost attribution for AI systems. Conventional monitoring says a request succeeded; it cannot say the answer was wrong. This is the tooling that catches quality regressions before customers report them.
02 Deliverables
What you get
- 01
Full traces of prompts, retrievals and tool calls
- 02
Automated evaluation against a regression set
- 03
Cost attributed by feature, tenant and model
- 04
Alerts on quality drift, not just errors
03 Related
More in Platform & Infrastructure
AI RAG Platform
Retrieval that grounds answers in your own content, with citations.
Explore serviceAI Knowledge Management
Scattered institutional knowledge made searchable and kept current.
Explore serviceAI Data & Analytics
Pipelines and analysis that make your data usable for AI in the first place.
Explore serviceTell us the process, we'll scope the AI
Bring one workflow that costs your team too much time. We will come back with what an AI system can take over, what it should not, and what it costs to build.