Technology
LLM Observability
LLM Observability implementation for production software delivery with clean architecture, maintainability, and predictable rollout.
Best For
Ideal use cases
Teams shipping AI features that must stay reliable over time
Products needing visibility into cost, latency, and quality
Systems requiring regression checks for prompt/model changes
What We Build
Projects we deliver
Tracing and structured logging for AI workflows
Eval suites and regression gates
Dashboards for quality, cost, latency, and failure modes
Ecosystem
Compatible tools & integrations
Seamless Integrations
Works with your existing stack
Use Cases
Recommended use cases
RAG assistants with retrieval diagnostics
Tool-calling agents with action auditing
Scaling AI features post-launch without surprise regressions
Delivery
How we deliver
We instrument end-to-end workflows so failures are diagnosable, not mysterious.
Evals are designed to run in CI with thresholds that match business goals.
Monitoring includes cost/latency controls for production predictability.
FAQ
Frequently asked questions
Basic tracing and a small eval set often pay off quickly, especially when AI affects customers or cost.
Yes. We can add tracing and evals without rewriting your product, then expand coverage over time.
Quality, latency, cost, retrieval relevance, tool-call correctness, and safety events—tailored to your workflow.
AI
Add AI on top of this stack
Two common AI services that pair well with this technology, plus a fixed-scope gig to start quickly.
Related
Explore related technologies
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