AI Production Audit for underperforming AI
Diagnose what's broken. Quantify the gap. Build a roadmap to ROI.
We help enterprises audit underperforming AI systems, quantify the performance gap (quality, cost, latency), and deliver a measurable roadmap to improve accuracy and ROI.
Offer ladder
Audit → Sprint → Retainer (baseline, ship, then prevent regressions).
Buyers do not need more slogans. They need concrete artifacts, clear scope, and an honest view of how delivery works.
Sample deliverable
Review the audit output format before you buy.
OpenAnonymized case studies
See the baseline, fixes, and measurable deltas.
OpenTransparent pricing
Understand scope, timelines, and where each offer fits.
OpenPrivacy and handling
NDA-friendly, redaction-ready, least-privilege workflows.
OpenRoot cause analysis
Root Cause Analysis: Accuracy, Hallucinations, Retrieval, Cost
This is production AI troubleshooting: we treat your system as a pipeline and diagnose the dominant failure modes—then quantify impact with an AI performance audit and cost assessment.
Underperforming AI system: inconsistent or wrong outputs → stakeholders lose trust
Hallucination measurement: ungrounded answers, missing citations, poor grounding
Retrieval evaluation (RAG): irrelevant context → confident wrong answers
AI project not meeting expectations: “POC worked” but production quality collapsed
Inference cost analysis + latency profiling: cost and p95 rise with unclear ROI
Coverage
What an AI Production Audit Covers
An AI system assessment across architecture, data/knowledge, evaluation, and cost/latency/risk—designed for production readiness, not academic reports.
System & Architecture Review
AI architecture review: model choice, prompting/system design, RAG pipeline, orchestration, tooling, failure modes.
Data & Knowledge Audit
Corpus coverage, freshness, chunking, embeddings, retrieval quality, labeling quality (ML).
Evaluation & Quality Measurement
AI evaluation framework + model evaluation framework: baseline creation, test suite, error taxonomy, failure analysis by cohorts and use cases.
Cost, Latency & Risk Profiling
Token usage, throughput, infra bottlenecks, cost per successful task, logging/PII risk checks (GDPR-aware for EU).
Measurement
Baseline Measurement & Evaluation Framework
We establish an AI evaluation framework that makes quality measurable—across classic model evaluation and modern LLM evaluation (including RAG evaluation / retrieval evaluation).
Offline evaluation suite
A baseline test set + error taxonomy (by use case/cohort) to track accuracy, task success, and failure modes.
LLM & hallucination measurement
Groundedness / ungrounded answer rate, citation coverage, refusal policy checks, and high-risk answer validation.
RAG evaluation + performance baselines
Retrieval precision/context relevance, latency profiling (p50/p95), and inference cost analysis (token + step attribution).
Timeline
Engagement Timeline (5–14 working days)
A production AI readiness assessment that delivers decision-ready outputs quickly—without forcing a tool migration.
Days 1–2
Access + interviews + baseline snapshot
Access setup, stakeholder interviews, baseline logging snapshot.
Days 3–7
Evaluation + error analysis + cost profiling
Evaluation build, failure analysis, and cost/latency profiling.
Days 8–14 (Deep)
Deeper analysis + validation + roadmap workshop
Deeper evaluation design, validate findings, propose quick wins, and run a roadmap workshop.
Deliverables
Audit Deliverables
You get a clear audit report + prioritized roadmap, plus the baseline metrics and evaluation suite to prevent “subjective quality.”
See what you keep after the audit and the minimum logging schema before an audit is worth paying for.
- Audit report (root causes + severity + risks)
- Baseline metrics (quality/cost/latency) + evaluation suite (initial)
- Prioritized roadmap (30/60/90-day plan)
- Quick wins list (fast fixes with estimated impact)
- Exec summary for leadership (ROI narrative + key decisions)
Metrics we typically establish
Pricing
AI Production Audit packages
Fast clarity with measurable baselines. Implementation work is scoped separately after findings.
Core
AI Production Audit — Core
Baseline + failure modes + decision-ready roadmap
- Baseline metrics: quality, cost, latency
- Failure taxonomy + root-cause hypotheses
- Prioritized quick wins + 30/60/90 roadmap
- Exec summary (ROI narrative + decisions)
Deep
AI Production Audit — Deep Production
Architecture-grade diagnosis + deeper cost/latency + risk
- Everything in Core
- Deep retrieval evaluation + RAG tuning hypotheses
- Cost/latency decomposition by pipeline step
- Safety/PII risk checks + rollout guardrails
| Capability | Core | Deep |
|---|---|---|
| Price | $3,800 | $9,800 |
| Baseline metrics (quality/cost/latency) | ● | ● |
| Failure taxonomy + root causes | ● | ● |
| Retrieval evaluation (RAG) | ⚠ Limited | ● |
| Cost/latency decomposition by step | ⚠ Limited | ● |
| Eval harness + regression gates (initial) | ⚠ Limited | ● |
| Safety/PII risk checks | — | ● |
| Workshop readout + follow-up | ● | ● |
Prices are for the audit engagement only (one-time). Recovery/implementation is scoped separately after findings.
FAQ
Common questions (high-intent)
Can you audit an AI system built by a vendor?
Yes. We run a second opinion AI audit using logs, outputs, and architecture evidence—without requiring source code in many cases (privacy constraints respected).
How do you measure hallucination rate?
We define a rubric for ungrounded answers, sample representative tasks, and run LLM evaluation + human review where needed—so hallucination measurement is repeatable over time.
Why does RAG fail in production?
Most failures are retrieval evaluation issues (coverage, chunking, embeddings, reranking) plus missing evaluation gates. We diagnose RAG not working by tracing retrieval → context → response and measuring each step.
Fit
Best fit if…
Best fit
- You have an AI system already built (vendor or in-house)
- You can share logs/data samples (with privacy constraints respected)
- You need a clear, measurable improvement plan quickly
Not a fit
- You only need a brand-new AI MVP from scratch at lowest cost
- You can’t access any system behavior, logs, or sample outputs
After the audit: next steps
Most teams proceed to an Optimization Sprint (4–6 weeks) to ship fixes with before/after benchmarks. For ongoing governance, our Reliability Retainer provides monitoring, regression gates, and incident triage.
Request
Request audit intake
Share the current stack, top pain, and timeline. We'll tell you if a Core Audit, Deep Audit, or a different path makes more sense.
What happens next
We keep the first step concrete.
We review the use case, stack, and constraints before recommending scope.
If the audit is not the right engagement, we say that directly.
If privacy is sensitive, redact examples first. High-level failures are enough for intake.
You can review sample deliverables and pricing before any call.
Prefer to review first?