Your vendor’s safety report covers their model. Not your system.
You added prompts, RAG, business rules, escalation paths, and human review. Those changes created a different system. The vendor’s testing does not cover it. Ours does.
Your vendor tested their model. But your team built a system — with prompts, data, business rules, controls, and human review paths that changed the behavior. AiValuations tests the system your people actually use.
The vendor's safety testing covered their model. Your team's modifications created a different system. That's the one customers experience, regulators examine, and counsel may need to defend.
AI systems are powerful, not inherently dangerous. The teams deploying them deserve better than certification theater and single-score safety reports.
When you show people what their system actually does — clearly, honestly, with the evidence to back it up — they make better decisions. AiValuations exists to produce that evidence. Not to sell compliance. Not to replace legal judgment. To give the people accountable for AI systems a record they can trust, inspect, and act on.
Most organizations can explain what their AI system is supposed to do. Fewer can show what it actually says, decides, routes, flags, or escalates in their own deployment context.
You added prompts, RAG, business rules, escalation paths, and human review. Those changes created a different system. The vendor’s testing does not cover it. Ours does.
A response can pass safety checks and still be incomplete, inaccurate, poorly escalated, or unfit for the workflow it serves. We score the dimensions separately so you can see what is really happening.
Your governance file describes what the system is supposed to do. Our evidence shows what it actually does — in your environment, with your data, under realistic pressure.
The process is built to be practical for enterprise teams: scoped, documented, reproducible, and clear about what the evidence supports.
We learn your system: what it does, who uses it, and how decisions flow.
We build test cases that reflect the pressure points your system actually faces.
We exercise the system and preserve every output, decision, and relevant condition.
We score what happened across multiple dimensions — not just safety.
We test whether guardrails, escalation paths, and review layers actually work.
You get evidence, findings, limitations, and documentation your team can use.
Our reviews produce a structured evidence package that legal, risk, data, and governance teams can inspect, challenge, and reuse.
of outputs contained operational defects that safety-only scoring missed entirely — including template placeholders, half-filled boilerplate, and broken links.
Insurance workflow evaluations. Decision-system mocks. Control-layer patch tests. Legal and governance analysis with clear claim boundaries. Every review is documented, scored, and available for inspection.
AiValuations supports two review tracks. The track defines the tests; the engagement tier defines the depth. A language/workflow system or a decision system can each be reviewed as a Targeted, Standard, or Deep engagement.
For chatbots, copilots, assistants, RAG workflows, and customer-facing or internal language systems.
For underwriting, credit scoring, pricing, claims triage, hiring, routing, and other systems that approve, deny, rank, price, or escalate.
We do not sell generic AI scores. Choose the review track that matches your system, then scope the engagement depth based on complexity, data access, controls, legal/governance context, and the evidence your team needs.
Enterprise engagements are scoped per system. Most teams start with a Targeted Review, typically beginning around $25K depending on scope, data access, review track, and reporting needs. Larger reviews, documentation packs, and monitoring retainers are scoped after we understand the system.
Typically starts at $25KOne deployed AI system or workflow. A focused evidence review across the most important risks, outputs, or decisions.
A fuller deployed-system review for legal, risk, governance, AI oversight, and internal audit teams that need a broader evidence record.
An expanded review for high-risk, complex, or board-level systems facing messy data, sensitive users, regulatory scrutiny, or risk committee attention.
For teams that know a workflow is risky and need to test whether a verifier, output gate, reviewer workflow, or policy prompt actually reduces the observed failure.
A natural add-on to an evidence review. We turn the deployed-system map and findings into practical governance materials your teams can adapt and maintain.
For systems that keep changing. Retesting can be triggered by model updates, prompt changes, RAG corpus updates, routing changes, workflow changes, or new regulatory expectations.
Start with a scoping call. We map the deployment, identify whether it belongs in the language/workflow or decision-system track, and recommend the right engagement level. Governance Documentation Packs and monitoring retainers are scoped as follow-ons when the evidence record and operating model are clear.
Our portfolio combines legal analysis, engineering evidence, and practical deployer guidance. Some pieces explain the problem; others demonstrate how the evaluation method works in practice.
Silvia’s AI Law. Decoded piece on why governance files cannot stop at documented purpose — deployers need evidence of what the system actually does in context.
Awakened Intelligence article on deployed-system evidence, Article 25, and why the modified AI system matters more than the vendor model alone.
Insurance workflow evaluation piece on operational-quality defects, template leakage, verifier controls, and why one safety score is not enough.
Upcoming demonstration reviews for systems that approve, deny, price, route, rank, or escalate — including proxy risk, counterfactual flips, drift, explainability, and oversight.
AiValuations brings together Awakened Intelligence — the technical evaluation and evidence team behind the deployed-system reviews, judge stacks, decision-system tests, and evidence packages — with independent AI regulatory counsel.
John brings 25 years of experience managing complex builds with real deadlines, real budgets, and many moving parts. He applies that discipline to AI evaluation: clear scope, careful sequencing, evidence preservation, and review pipelines that show what deployed AI systems actually do.
His operating principle is simple: the best evidence is the kind that does not need a sales pitch.
Silvia is a practicing AI regulatory lawyer with six years at Amazon, experience leading an AI project featured in the Wall Street Journal, and current work inside an insurance company implementing AI governance.
She brings the legal, governance, and buyer-context lens: what evidence matters, what counsel needs, and where technical findings must stop before becoming legal conclusions.
AiValuations produces technical evidence, system maps, test results, and governance documentation structure. Legal interpretation, regulatory advice, and privilege strategy belong to counsel. Attorney-directed engagement structures are available where appropriate.
A review can show observed behavior, control performance, defects, gaps, and risk indicators. It does not certify compliance, prove fairness, or replace legal review.
Observed outputs, decisions, defect rates, differential outcomes, counterfactual sensitivity, drift signals, control-layer behavior, and evidence limitations.
Certified compliant, production-ready, legally safe, discrimination proven, fairness guaranteed, or regulator-proof.
Raw records, prompts, outputs, metrics, model/version details, source assumptions, scoring artifacts, and explicit claim boundaries.
Regulated teams need to know how evidence is protected before a scoping call becomes a review. AiValuations is designed around isolated workspaces, human-gated transfer, and documented retention from the start.
Each engagement gets its own workspace and evidence room. We do not share client data across clients, projects, demonstrations, or internal training packs.
We do not accept client data until NDA/DPA coverage is in place. Named subprocessors and API providers are documented in the DPA, including retention windows.
No automated pipeline pulls client data. Every transfer is deliberate, reviewed, documented, and limited to what the scoped review requires.
We work with the minimum data needed to answer the review question. Where synthetic, sampled, masked, or redacted data is enough, we prefer that first.
Retention terms are defined before review. Deletion is documented, with attestation that accounts for provider retention windows and evidence-preservation obligations.
Client evidence is used for the client engagement only. Public examples, portfolio work, and synthetic demos stay separate unless a client explicitly approves otherwise in writing.
Tell us what you have deployed or plan to deploy. We will help map the system, identify the right review track, and recommend the smallest evidence review that answers the real question.
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