Visible health
Latency, error rates, cost per request, and quality scores in one place.
AI · AI Operations
AI operations that keep quality from drifting.
Prompts, models, costs, and evals need ownership after launch. We set up the loops that keep AI features reliable in production.
Outcomes
What success looks like.
Safer releases
Prompt/model changes go through checks - not silent edits in prod.
Lower surprise bills
Budgets, alerts, and routing before costs spike overnight.
Capabilities
How we deliver in this area.
Eval harnesses
Golden sets and regression tests for prompts and models.
Observability
Tracing, logging, and user-feedback capture.
Versioning
Prompts, tools, and model configs under change control.
Incident playbooks
What to do when outputs go wrong in public.
Cost governance
Caching, batching, and model fallbacks.
Human review queues
Escalate edge cases before they hit customers.
How we run it
A clear path from brief to release.
Seniors stay close to the work. Status stays honest. The process bends to your stage.
01
Baseline production
Instrument what you already shipped.
02
Define quality bars
Pass/fail for the cases that matter most.
03
Automate checks
CI and scheduled evals on every meaningful change.
04
Operate weekly
Review drift, cost, and user complaints on a cadence.
Questions
Straight answers before you commit.
Is AI Ops only for large teams?
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Is AI Ops only for large teams?
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No. Even a small product needs basic evals and cost alerts. We size the ops layer to your stage.
Can you take over an AI feature we already shipped?
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Can you take over an AI feature we already shipped?
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Yes - we start with a health check, then stabilize and improve.