Know What Breaks Before Your Users Do.
A rigorous diagnostic audit of your LLM pipelines, prompt architectures, and critical user journeys by an enterprise QA architect. Catch hallucinations, prompt regressions, and failure states before scaling.
Who Needs an AI QA & LLM Audit?
Founders and small teams shipping LLM-powered features who need to know what breaks before users find it
Startups with codebases built or accelerated by AI tools (Cursor, Claude, Copilot) needing structural verification
Teams experiencing unpredictable model outputs, prompt degradation, or edge-case crashes in production
What We Examine
We look beyond basic syntax into the non-deterministic failure modes unique to LLM applications and modern web architectures.
Critical User Flows & Edge Cases
End-to-end traversal of key user journeys under degraded and boundary conditions.
- Auth & session boundaries under token expiration or invalid permissions
- Asynchronous race conditions and timeout handling on long-running AI requests
- Form input boundary testing and defensive sanitization
- Cross-browser and mobile responsive breakages on core interactive states
LLM Output Quality & Security
Stress-testing prompt architecture, hallucination vulnerabilities, and error recoverability.
- Hallucination risk assessment across varied and ambiguous user queries
- Output schema compliance (JSON formatting, missing keys, type mismatches)
- Prompt injection vulnerabilities and defensive boundary guardrails
- Graceful fallbacks and clear error states when model APIs rate-limit or fail
Data Accuracy & Provenance Checks
Verifying data flow integrity between databases, business logic, and LLM context windows.
- Verification that mathematical & financial computations execute deterministically in code, not in prompts
- Source attribution and per-field data provenance tracing
- Database mutation and state isolation under concurrent requests
- Context window contamination and truncation checks
Regression-Test Recommendations & Fix List
Actionable blueprint to prevent recurring defects as your models and prompts evolve.
- Severity-ranked findings matrix (Critical / High / Medium / Low)
- Reproducible defect scenarios with step-by-step reproduction instructions
- Automated evaluation harness recommendations for CI/CD regression checks
- Prioritized architectural remediation roadmap
What You Receive
No vague generic summaries. You receive concrete engineering documentation with severity ratings and exact code & prompt remediation steps.
Written Findings Report: Comprehensive breakdown with severity-ranked issues, exact reproduction steps, and root cause analysis
Prioritized Remediation List: Concrete code and prompt recommendations for immediate fixes
Regression Test Suite Recommendations: Suggested test matrices and evaluation sets for your CI/CD pipeline
30-minute Findings Debrief: Walkthrough call with our founding QA architect to review results and answer questions
The 4-Step Audit Process
Intake & NDA
We execute a mutual NDA, collect repo or staging access, and map out your critical user flows and AI touchpoints.
Stress-Testing & Audit
We run adversarial prompt probes, edge-case evaluations, and code-level flow inspections across all 4 audit pillars.
Report Compilation
We synthesize test results into a severity-ranked findings report with reproducible steps and architectural fix guides.
Debrief & Roadmap
A 30-minute debrief call walking through critical vulnerabilities and recommended fixes so your team can remediate rapidly.
Frequently Asked Questions
What access do you need to perform the audit?
Typically read access to your Git repository and a staging or development environment where we can test live requests. If repo access is restricted, we can perform an external audit of your staging API endpoints and user interfaces under a mutual NDA.
How long does the audit take?
A standard AI QA & LLM Audit is completed within 3 to 5 business days from receiving environment access and approving the scoping brief.
Does this audit cover open-source models as well as proprietary APIs?
Yes. We audit pipelines built on OpenAI, Anthropic, Google Gemini, as well as open-weight models (Llama 3, Mistral) hosted privately or via inference providers.
How is this different from an automated security scanner?
Automated scanners look for generic dependency CVEs or basic lint rules. They cannot evaluate whether an LLM produces misleading responses, if a multi-step agent gets stuck in loops, or if user workflows fail under edge conditions. This audit is conducted by an enterprise QA architect analyzing application logic and prompt reliability.
Can you implement the fixes after the audit?
Yes. If your team needs hands-on engineering help implementing the remediation list, we can scope a remediation sprint under our fixed-price tiers or continuous retainer.
Who owns the findings and report?
You do, 100%. All reports, evaluation data, and test artifacts are strictly confidential and transferred exclusively to your team.
Secure Your AI Product Before Launch.
Send us a brief overview of your product and stack. We will reply within 24 hours with an NDA and scoping proposal.
Book an AI QA Audit →