Enterprise AI deals fail on evidence gaps.
UniToolx finds them before your buyer does. We pressure-test the claims, documentation and technical evidence behind your AI product so security, privacy, procurement and governance review does not discover avoidable blockers late in the deal.
We review your product from the buyer's side of the table.
Enterprise buyers increasingly ask AI-specific questions that standard SaaS collateral does not answer cleanly: training use, retention, subprocessors, model provenance, model changes, permissions, logging, incident handling and AI-layer testing.
- Map public and private claims to the evidence that actually supports them.
- Identify contradictions, scope ambiguity and missing documentation.
- Simulate the questions a serious buyer is likely to ask next.
- Prioritize fixes by deal friction: blocker, escalation, clarification or hygiene.
- Produce a buyer-ready evidence pack structure your team can maintain.
- Claim
- “Customer data is never used for model training.”
- Checked
- Privacy policy · DPA · subprocessors · model-provider terms · product documentation
- Observation
- The public claim is broader than the documented scope. One downstream processing path is not covered by the cited language.
- Buyer question
- “Does this commitment cover every model/provider path, feature tier and optional telemetry setting?”
- Recommended fix
- Narrow the public claim or extend the evidence/contractual language to the uncovered path.
Not a checklist. A traceable evidence map.
Every material finding should be understandable by engineering, sales and a buyer reviewing the same issue.
Executive blocker map
A concise view of findings that can trigger procurement escalation, follow-up or contractual friction.
Claim-to-evidence matrix
The exact claim, supporting source, test performed, result, ambiguity and recommended remediation.
Buyer-ready remediation
Concrete changes to documentation, product wording, evidence collection or internal ownership.
A commercial stress test, not compliance theater.
We do not award a badge, certify compliance or replace your legal/security advisors. We identify what a buyer can verify, where the evidence breaks, and what needs to change before the question arrives in a live deal.
Read the assessment boundaryCollect
Product claims, docs, policies, contracts, architecture evidence and optional test access.
Challenge
We test scope, consistency, provenance, controls and evidence quality.
Trace
Each material claim is connected to evidence and a buyer-facing answer path.
Fix
You receive prioritized actions and the structure for a maintainable evidence pack.
What we look at
Data & training use
Prompts, files, outputs, embeddings, logs, retention, deletion and training/fine-tuning commitments.
Model & supply chain
Underlying model providers, subprocessors, versioning, dependencies and change communication.
Security evidence
AI-layer testing claims, prompt-injection coverage, isolation evidence, access controls and incident paths.
Governance & ownership
Who owns AI risk, changes, exceptions, review cadence and customer-facing representations.
Contract & policy consistency
Whether product pages, privacy terms, DPA language and operational reality describe the same system.
Buyer response readiness
Whether a sales/security team can answer follow-up questions with evidence instead of improvisation.
Know what your buyer will challenge before the questionnaire lands.
Start with a public-evidence scan or send us the documents you already use in enterprise deals.