A fixed-scope, fixed-timeline diagnostic of one shipped AI product against the AUX framework. You walk away with a Trust Scorecard, a named gap list, and a 90-day prioritized fix roadmap — in five business days.
A 5-business-day diagnostic of one shipped AI product against the AUX framework. Fixed scope, fixed timeline, fixed price.
Most AI products ship without ever being evaluated against how they build — or break — user trust. The Agent Experience Audit closes that gap. We examine your product across the full AUX trust architecture, score it against the 10 AUX heuristics, identify named failure modes with evidence, and hand you a prioritized roadmap your team can act on immediately.
One product. One audit. A "product" means one user-facing AI surface — a copilot, an assistant, an agentic workflow. Multi-product or platform-level audits are a separate engagement. This constraint is what makes delivery in five days possible.
Every audit covers the same five dimensions in the same sequence. This is what makes the methodology consistent and the scorecard comparable across products.
If the packet isn't complete by Day −3, kickoff slips a week. No exceptions — this is what stops the engagement running into 12-day chaos.
agent-spec.schema.yaml template (we provide the blank)⟶ We send the blank agent-spec template and a packet checklist on booking confirmation.
The structure is fixed by design. Every audit follows the same five-day cycle — which is what makes it deliverable in five days and comparable across clients.
tg.family.name classification, a severity rating, a stage-collapse description, and an evidence link.Every audit produces the same three primary deliverables. No slide decks, no generic recommendations. Each document is specific to your product — built from your transcripts, your agent spec, and your users' observed experience.
Your product's maturity across the four trust architecture stages (T01–T04) and all ten AUX heuristics (H01–H10), scored 0–3 with a visual heatmap and an overall grade.
Every trust failure mode detected, classified with its tg.* taxonomy code, severity rating, and linked to the specific transcript evidence that surfaced it.
A prioritized three-tier action plan: must-fix before next release, fix this quarter, and architectural debt. Each item links to a named gap and a recommended fix pattern from the AUX library.
The Trust Scorecard looks like this. Names and scores are illustrative — your product's results will vary.
Both tiers cover the full audit methodology. The difference is scope of stakeholders, product complexity, and readout format.
The default tier. Right for most B2B or B2C AI products with a single product team and English-language transcripts.
Same five-day cycle, extended for products with multiple locales, regulated environments, or stakeholders who need a board-level readout.
This audit asks whether humans can trust your agent. Its mirror is whether machines can understand your brand — because in 2026 an AI answer engine is often the first thing that decides whether you're in the consideration set at all.
The Entity Legibility add-on scores how cleanly AI systems can parse, attribute, and cite you: semantic-triple coverage (subject → predicate → object), Schema.org and DefinedTerm markup, an answer-first AI Info Page, machine-readable discovery files (llms.txt, capabilities.json), and off-page entity consistency. The same discipline as the audit — making a system legible — pointed at the brand instead of the agent.
It folds into this engagement or the AI-readiness audit as a discrete module. Ask about the Entity Legibility add-on →
A 30-minute scoping call to confirm the product is in scope, identify your contact, and agree a kickoff date. If the audit isn't the right engagement for where you are, we'll tell you that on the call.
Tell us which product you want audited and what's driving the timing. We'll confirm fit and come back with a kickoff date within one business day.