Synergos Audit validates AI systems for semantic consistency — testing whether your critical concepts hold their meaning across every context, every user, every edge case.
When an AI's understanding of a concept drifts between training, testing, and deployment — or shifts based on how a question is phrased — the result isn't just a wrong answer. It's legal liability, a PR crisis, or a customer loss.
Bot invented a bereavement refund policy that didn't exist. Court ruled Air Canada liable. One semantically inconsistent "policy" concept caused the entire failure.
Customer prompted the chatbot to agree to sell a car for $1. The bot complied — then went viral. The concept of "price" had no consistent semantic grounding.
AI system's ambiguous concept of "sepsis risk" led to thousands of missed diagnoses. A single semantic inconsistency compounded across patients and years.
Lawyer used ChatGPT to draft briefs. AI cited non-existent cases. The concept of "legal precedent" was semantically unstable — hallucinated citations felt real.
Total preventable value across documented AI semantic failures
$4 Billion+Each audit runs up to 8 specialized test blocks — selected based on your AI's architecture, use case, and risk profile. Every block probes a different failure mode that standard testing misses entirely.
Measures whether your AI means the same thing across differently-framed versions of identical questions.
Detects contradictions when the same policy question is posed from different angles or user types.
Measures hallucination rate and factual accuracy in high-stakes domains where wrong facts carry legal risk.
Tests whether your AI stays within its authorized scope or can be pressured into exceeding its mandate.
Validates that escalation decisions are applied consistently — not based on how a customer phrases their request.
Detects when a multi-turn AI contradicts commitments it made earlier in the same conversation.
Identifies implicit bias — equivalent requests receiving materially different treatment based on customer framing.
Detects hallucinations introduced by the generation layer that contradict or drift from retrieved documents.
One out-of-tune instrument undermines the whole ensemble. One semantically inconsistent AI response can shatter customer trust, create legal exposure, or go viral for the wrong reasons.
"Ensuring your AI's concepts vibrate at the same frequency — across every context, every user, every edge case."
Semantic consistency means your AI understands "refund policy," "risk," "price," or "precedent" the same way whether it's talking to a first-time user, a power user, an adversarial prompt, or an edge case your team never imagined.
Most AI failures aren't model failures. They're semantic failures — and they're entirely preventable.
Full semantic validation on the 15 highest-risk concepts in your specific AI system.
Your AI benchmarked against GPT-5 and Claude. See exactly where your model diverges.
Every finding scored by severity and dollar-value business exposure. Know what to fix first.
Professional report with evidence, findings, and a clear remediation roadmap for your team.
Live call reviewing findings with your team. Q&A, clarifications, next-step planning.
Follow-up questions, remediation guidance, and clarifications after report delivery.
We're building our case study library. In exchange for founding pricing, we ask for a testimonial and case study permission.
Same deliverables as the full $10K engagement. No shortcuts.
Written testimonial · Case study permission · LinkedIn recommendation · 2 referral introductions
We learn your system, use cases, and the concepts that matter most to your business.
You share API access or sample outputs. We identify the 15 highest-risk concepts to test.
Full testing, baseline comparison, risk scoring, and report writing.
20-page PDF delivered. 60-minute walkthrough call. Follow-up support begins.
No. We only need example input/output pairs and a brief description of what your AI is supposed to do — nothing that reveals your proprietary prompt engineering. We access your AI through its standard API or chat interface, exactly the way your users do.
Typically 10–14 business days. Day 1 is a short discovery call. Days 1–2 are intake and concept selection. Days 3–12 are the full semantic testing and analysis. Days 13–14 are report writing and your walkthrough call.
Complex enterprise deployments with more than 15 concepts may extend slightly — we'll set expectations clearly during intake.
A professional PDF report covering: an executive summary with business impact framing, full methodology, concept-by-concept risk scoring, annotated failure evidence, composite risk scores, and a prioritized remediation roadmap with engineering-actionable guidance.
Comprehensive audits also include a 60-minute findings walkthrough call and 2 weeks of follow-up support. See the example audit for a complete walkthrough of what a real report looks like.
Standard functional testing confirms an AI responds — it doesn't check whether it responds consistently. Semantic failures are invisible until a user finds the exact phrasing that breaks the pattern.
Air Canada's chatbot passed every internal test before it invented a bereavement discount policy on its own. The problem wasn't that it didn't answer — it was that it answered differently depending on how the question was framed.
We audit deployed behavior, not the underlying model. A GPT-4-powered support bot may be perfectly reliable or dangerously inconsistent — it depends entirely on your prompt design, context injection, and retrieval configuration.
The model is irrelevant; what matters is what your AI actually says to your users, in your context, when the questions get hard.
Through whatever interface your users actually use — typically an API endpoint or a staging environment. We never require production access. Most clients share a sandbox or test API key. If you'd prefer fully offline testing using recorded output samples, that's also an option.
The audit is a point-in-time risk snapshot of your current deployment. We recommend re-auditing after any significant change to your prompt, model version, or retrieval data — each of these can introduce new semantic inconsistencies.
For high-stakes deployments, we offer monitoring retainers that catch regressions before they reach users. Ask about this on your intro call.
Any AI system that handles natural language and operates within a domain of knowledge — customer support bots, clinical decision support tools, legal research assistants, HR policy Q&A systems, financial advisor chatbots, and more.
If your AI answers questions, gives guidance, or makes commitments based on a specific knowledge domain, we can audit it. If you're unsure whether your system qualifies, ask — we'll tell you honestly.
5 founding client spots at $2,500. Full audit. No risk.