Evaluation Harness
Golden datasets, rubric scoring, regression checks, and release gates before a prompt or model change ships.
Services/AI Operations
AI operations that ship.
We build the operating layer around AI systems: evals, monitoring, prompt control, governance, cost visibility, and handoff paths. Not another demo. The machinery that keeps production AI useful after launch.
What this service is
The missing layer between model output and business trust.
Teams do not fail at AI because the model cannot answer. They fail because nobody can prove the answer is still good, nobody knows what changed, and nobody owns the system after the launch meeting. AI Operations gives the system a release process, a dashboard, a runbook, and a business owner.
Field Note · No.01
Production AI is not a prompt. It is a control system wrapped around a model.
Operations BriefUNEXPECTED404
What is in scope
Six controls every production AI surface needs.
The exact tools change by stack. The control plane does not. These are the pieces that make AI measurable, explainable, and safe enough to keep improving.
Observability
Trace every prompt, tool call, model response, failure, cost spike, and latency change from one operating view.
Governance
Access boundaries, PII handling, model/version control, approval flows, and documentation a risk team can read.
Cost Controls
Budget thresholds, model routing, caching strategy, and alerts before experiments become recurring spend.
Prompt Operations
Versioned prompts, change logs, review workflows, rollback paths, and experiments with measurable outcomes.
Human Handoff
Confidence thresholds, escalation queues, and review loops that make automation safer instead of more opaque.
Bad path vs. operating path
Where AI projects usually fall apart.
Per 100 AI ideas. The left number is the typical path: demo-heavy, weak evals, vague ownership. The right number is the operating path: scoped, evaluated, governed, and reviewed.
AI initiative survival · per 100 ideas
Stage
DemoOperated
LiftIdea
100100
Prototype
6088
+28
Evaluated
2472
+48
Governed
1261
+49
Shipped
648
+42
Improving
239
+37
AI Ops turns launch into a managed system.
The goal is not more experiments. It is fewer unknowns: what changed, what quality moved, what cost shifted, and who owns the next decision.
Production survival
19x
operated vs. ad hoc
Primary control
Evals
before launch
How the engagement runs
From unmanaged AI use to operating rhythm.
01
Phase 01
Audit
Inventory AI touchpoints, model usage, costs, data exposure, and places where teams already rely on manual review.
02
Phase 02
Instrument
Add evaluation, tracing, prompt versioning, cost reporting, and clear release controls around the highest-value surface.
03
Phase 03
Harden
Pressure test failure modes, add governance, write runbooks, and define exactly when humans take over.
04
Phase 04
Operate
Review quality weekly, tune the system, expand scope deliberately, and keep finance/risk/product looking at the same facts.
Let's talk · 10-minute intro call
Put controls around the AI work already happening.
Bring the current tools, prompts, experiments, and risks. We will map what needs evals, monitoring, governance, or a hard stop.
4 wk
to stand up the first operating layer around one AI surface
1
shared view for quality, cost, latency, failures, and ownership
0
untracked prompt changes moving quietly into production
100%
of prompt changes go through evals before they hit production