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Services / audit / automation
AI Reliability Audit
Find out if your AI feature actually works — in two weeks.You shipped an AI feature and now you have no idea if it works in production. Two weeks: build evals, measure hallucination rate, latency, cost-per-request, and ship a regression suite that fails CI when quality drops. You leave knowing exactly where the model is wrong, how often, and how much it costs you.
service graph
Surface ⇄ System
- price
- from $4,500
- timeline
- 2 weeks
- cadence
- one-time
price
from $4,500
timeline
2 weeks
cadence
one-time
scope
One-time / fixed scope
LLM evalsOpenAIAnthropicLangSmithBraintrustCI gates
00// matrix position
Where this fits in the services matrix.
Every service page now names the buyer state, the commercial shape, and the next route. That keeps the catalog navigable instead of feeling like disconnected offers.
01 · best fit
Build automation with a fixed scope and written handoff.
02 · commercial shape
from $4,500 · 2 weeks · One-time / fixed scope
03 · route logic
Use the diagnostic or book a call to confirm fit before scope is written.
04 · decide
Not sure this is the right service? Run the route finder and get the matching path.
00B// system flow
The offer is a route, not a loose task list.
This diagram gives every service page a concrete operating model: intake, system design, implementation, proof, and handoff.
service operating path
Surface ⇄ System
AI Reliability Audit flow
The diagram is intentionally simplified: it shows the buying logic and operating path, not a decorative fantasy architecture.
price
from $4,500
timeline
2 weeks
cadence
one-time
01// what you walk away with
The outcome, not just the output.
- 01Hallucination rate measured on a real dataset
- 02Latency + cost-per-request profile across providers
- 03Regression test suite that runs in CI
- 04Prioritized fix list ranked by impact and effort
- 05Dashboards your team can read every Monday
02// scope
Concrete artifacts you keep — and what we leave out.
Working code, written docs, dashboards your team owns. We also list what this engagement deliberately does not cover, so scope is honest before you click.
// deliverables
- Eval dataset (50–200 cases) sourced from your production logs or hand-curated golden set
- Eval runner wired into CI — fails PRs that drop quality below threshold
- Hallucination + accuracy + latency + cost dashboards
- Top 10 prompt / retrieval / model fixes with measured before/after impact
- Loom walkthrough + 60-minute review call
- 14 days of post-engagement Slack support
// not included
- Model fine-tuning (separate engagement)
- Building net-new AI features (use Internal AI Copilot or RAG Engineering)
- Annotation labor at scale (we set up the loop; ongoing labeling is on you)
03// methodology
How the engagement actually runs.
- 1Day 1–3
Eval design
Mine production logs (or build a golden set), define quality dimensions, agree on thresholds. We finalize the eval rubric before any code.
Eval rubricGolden dataset (50–200 cases)Threshold spec - 2Day 4–7
Measurement
Run evals across your current setup + 2–3 alternative model/prompt configurations. Profile latency, cost, accuracy, hallucination rate.
Eval results spreadsheetCost/latency profileProvider comparison - 3Day 8–11
CI integration + dashboards
Wire the eval runner into GitHub Actions / your CI. Stand up dashboards (Grafana / Braintrust / custom) so the team sees regressions as they happen.
CI workflowDashboardsRunbook for triaging eval failures - 4Day 12–14
Findings + handoff
Final report, Loom walkthrough, 60-minute review call, ranked fix list with effort estimates. 14 days of Slack support follow.
Findings reportLoom walkthroughRanked fix listSlack channel
// track record
Receipts, not promises.
- 2 weeks
- Typical delivery
- fixed scope
- 50–200
- Eval cases in scope
- production-grounded
- 3+
- Providers compared
- on cost vs quality
04// questions
Common questions.
01What if we have no eval dataset?
We build one with you. Two days of source-mining production logs and curating a golden set is the first phase of every audit.
02Which models do you cover?
OpenAI, Anthropic, Google, open-weights via vLLM/Together/Replicate. We test cost-vs-quality across at least 3 providers as part of the audit.
03Will you fix the issues you find?
The audit ends with a ranked fix list. You can hire us to ship the fixes (RAG Engineering, Prompt & Eval Library Setup, or AI Quality Retainer) or take it in-house — your call.
04How is this different from a regular code audit?
A code audit finds bugs in deterministic systems. This finds quality drift in non-deterministic systems. Different methodology, different deliverable.
// engage
Ready to start AI Reliability Audit?
A 30-minute call to confirm fit, scope, and timeline. No pressure, no slides.
automation system
From offer to operating system.
AI Reliability Audit is presented as a real engagement, not a generic service page: the surface, backend shape, delivery artifacts, and conversion path are all visible before the first call.
Scope AI Reliability Auditautomation system
Surface ⇄ System
- price
- from $4,500
- timeline
- 2 weeks
- tier
- B
price
from $4,500
timeline
2 weeks
tier
B
Living architecture
Scope ⇄ Ship
The page now exposes how the engagement moves from buyer pain to production artifact, then into measurement and next-step routing.
Scope AI Reliability Audit- 01Eval designMine production logs (or build a golden set), define quality dimensions, agree on thresholds. We finalize the eval rubric before any code.
- 02MeasurementRun evals across your current setup + 2–3 alternative model/prompt configurations. Profile latency, cost, accuracy, hallucination rate.
- 03CI integration + dashboardsWire the eval runner into GitHub Actions / your CI. Stand up dashboards (Grafana / Braintrust / custom) so the team sees regressions as they happen.
- 04Findings + handoffFinal report, Loom walkthrough, 60-minute review call, ranked fix list with effort estimates. 14 days of Slack support follow.
Conversion path
Surface ⇄ System
01
Diagnose
Confirm the real automation constraint, current surface, and business goal before writing code.
02
Design the system
Turn the offer into screens, data, workflows, ownership boundaries, and a measurable delivery plan.
03
Ship the artifact
Deliver AI Reliability Audit as working code, docs, dashboards, or launch assets your team can actually use.
04
Route the next move
Decide whether the work becomes a one-time delivery, a care plan, or a larger product build.
Proof assets
Real only
Asset slot
Service proof visual
Add a real screenshot, deliverable preview, or dashboard capture from a shipped engagement when approved.
Verified asset
Founder/operator photo
Real founder photo reinforcing principal-led delivery.
Asset slot
Client quote or logo
Add only permissioned testimonials or logos tied to this service category.