Service 06

Your AI shipped. Now keep it honest.

Accuracy decays, prompts regress, costs creep and nobody notices until a customer does. We instrument what you already run, then tune it against evidence.

ai-operations · cockpit7d window

QUALITY

94.2%

eval pass rate, 240-case set

LATENCY

1.28s

p95, down from 3.4s

COST / 1K RUNS

$11.40

-38% after routing + cache

CONFIDENCE

0.91

mean, threshold at 0.75

ESCALATIONS

3.6%

routed to a named human

What we fix

Six ways live AI quietly degrades.

ACCURACYModel driftProvider updates and shifting input distributions move behaviour under you. Without a case set you find out from a complaint.
CHANGEPrompt regressionA one-line edit fixes today and breaks four other paths. Prompts need version control and a test gate like any code.
SPENDRising costEverything on the biggest model, nothing cached, retries unbounded. Usually the fastest win in the whole engagement.
TRUSTUnreliable outputsFree-text where a schema belongs, no validation, no citation. Users stop believing the feature and route around it.
VISIBILITYObservability gapsNo trace of what context was retrieved or which tool ran, so a bad answer cannot be explained or reproduced.
RISKPII & compliance riskSensitive fields reaching a third party, retention nobody configured, logs no auditor would accept.

Start with two weeks, not a retainer.

  1. 01

    Two-week audit

    We build an eval set from your real cases, trace a week of traffic, and price every step of the pipeline. Fixed fee, no commitment beyond it.

    deliverable: scored baseline
  2. 02

    Prioritised fixes

    A ranked backlog by impact against effort, then four to six weeks executing the top of it — each change measured against the baseline before it ships.

    deliverable: measured deltas
  3. 03

    Ongoing optimisation

    A light monthly cycle: evals on every change, cost review, drift alerts, and a written report of what moved. Cancel any month.

    deliverable: monthly report

Six levers, pulled in order of evidence.

Nothing changes without a before-and-after number against your own case set.

Quality evaluationsA graded case set built from your real traffic, run on every prompt, model or retrieval change.240-case baseline
Model routingCheap model for the easy majority, escalation to a stronger one only where the score justifies it.-38% cost
Prompt & context tuningTighter instructions, structured output, better retrieval — measured, not vibed.+6.4pt accuracy
CachingSemantic and exact-match caching for repeated questions, with sane invalidation.p95 3.4s → 1.3s
ObservabilityTraces per run: input, context, decision, tool calls, cost, outcome — searchable by case.100% traced
Compliance controlsRedaction before inference, retention policy, access boundaries, exportable audit trail.auditor-ready logs

AI operations questions

A scored baseline on a case set built from your own traffic, a traced cost breakdown per pipeline step, a ranked fix list with effort estimates, and a short written recommendation. It stands alone — plenty of clients implement it themselves.

Find out what your AI actually costs and scores.