AI reliability audits · RAG & agent systems

We don't build AI.
We fix it.

Your assistant demos beautifully — and hallucinates in production. We audit it with a rigorous eval harness: a numeric reliability score, evidence for every failure, a prioritized fix plan. In 10 days, fixed price.

⟲ DRAG TO ORBIT · 360°
0%

of enterprise GenAI pilots show no measurable P&L impact.

MIT NANDA, 2025
0%

of companies abandoned most of their AI initiatives in 2025.

S&P Global
10 days

from access to a scored, evidence-backed audit report — fixed scope, fixed price.

Attova audit
The method

The money is spent. The pain is real.
The fix is measurable.

01

Audit 10 days · fixed price

We run your system against a golden question set and score retrieval quality, grounding and hallucination behavior — context precision & recall, MRR, faithfulness. You get numbers, ranked failure classes, and root causes. Not vibes.

deliverable: reliability score + evidence + fix plan
02

Remediate failure-class driven

Chunking strategy, hybrid retrieval, rerankers, grounding guardrails, eval loops — we fix exactly the failure classes the audit exposed, and prove the delta with a before/after run of the same harness.

deliverable: measured score improvement
03

Monitor monthly · continuous

Models update. Indexes drift. Prompts rot. Continuous regression evals catch silent degradation before your users do — reliability as a standing guarantee, not a one-time snapshot.

deliverable: regression alerts + monthly reliability report
What we measure

Every score is explainable. Every failure is evidence.

Model-agnostic instrumentation across the full pipeline — retrieval, generation, and operations.

Retrieval
Context Precision
are retrieved chunks relevant?
Retrieval
Context Recall
did the right chunks arrive?
Retrieval
MRR / nDCG
is the right chunk ranked first?
Generation
Faithfulness
is the answer grounded in context?
Generation
Hallucination Rate
claims not supported by sources
Generation
Answer Relevance
does it answer the question?
Operations
Latency p50 / p95
user-felt speed under load
Operations
Cost per Query
token waste → savings map
Sample audit

Real harness. Real output.

Below: an actual run of the Attova harness against a demo RAG system — the same instrument we point at yours.

attova — reliability audit

    

Two failure classes surfaced in seconds: vocabulary-mismatch recall collapse and ranking misses — the exact failures that make an assistant "confidently wrong" in production.

Open the full sample audit report → Run the open-source harness yourself ↗

Why Attova

Evaluation discipline from environments where AI failure is not an option.

Founded by a PhD defense-systems architect. We bring the rigor of high-stakes evaluation to commercial AI systems — and we sell outcomes, not tooling.

Numbers, not vibes

A single reliability score your board understands, decomposed into metrics your engineers can act on.

Evidence under every score

Each metric ships with the failing queries, retrieved contexts and root causes behind it. Fully reproducible.

Your stack, not ours

Model-agnostic and vendor-neutral. We audit the system you already built — no rip-and-replace, no lock-in.

Request an audit

Tell us what you built.
We'll show you where it breaks.

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Free 30-minute mini-diagnostic first. No spam, no list, one reply.

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We'll get back to you within one business day with a proposed slot for your free 30-minute mini-diagnostic.

What the audit hands you

  • A single 0–100 reliability score, decomposed per metric
  • Every failing query, with retrieved context as evidence
  • Root-cause map: chunking / retrieval / reranking / prompting
  • Prioritized, costed fix plan your team can execute
  • Before/after re-run proving the delta, if we remediate
0
reliability score