Signals from real engagements.
Context, action, result. No fluff.
Representative outcomes from real engagements. Details vary, but the pattern is consistent: signal over noise, rigor over rush.
Autonomous inbox operations for teams that need speed with control.High-volume inboxes slowed response times and created inconsistent handling
Deployed niotap with knowledge grounding, confidence gates, and escalation rules
Faster responses with consistent outcomes and full audit trails
Flaky automation slowed releases
Rebuilt CI signal health
Predictable pipelines and faster releases
LLM failed in edge cases
Added evals and guardrails
Stable and safer rollout
Incidents during scale
Golden signals and runbooks
Faster diagnosis, calmer ops
Low trust search results
Hybrid retrieval and tuning
Higher relevance and trust
Stabilizing AI in Production
A team had an LLM system that worked in demos but behaved unpredictably in real usage. We introduced evaluation frameworks, retrieval tuning, and guardrails.
Measurable improvement in response quality and safer rollout.
Reducing Release Risk
A CI pipeline existed but lacked trust due to flaky automation. We redesigned test strategy around risk and signal health.
Faster releases with fewer late-stage surprises.
Improving Platform Reliability
A cloud platform struggled during scale events with poor observability. We implemented structured telemetry and operational patterns.
Faster incident resolution and calmer on-call rotations.