The AI readiness and evaluation audit without the vendor demo.
Inventory live AI use cases, build a representative evaluation set, quantify failure modes, and deliver a prioritized remediation and deployment roadmap.
Nothing is finalized until a human approves it.
Where the time and money actually go.
Companies deploy pilots without stable acceptance criteria, provenance, access controls, cost telemetry, or regression tests; vendor demos do not reveal production risk.
AI product teams, engineering, data, security, legal and operations
Trigger to act: An AI feature is entering production, incidents or hallucinations occurred, procurement requests governance evidence, or leadership cannot compare model/vendor choices.
The result you can model before you sign.
Illustrative only: compare the audit fee with the expected cost of one failed launch, support incident, or quarter of low-adoption development. No generic ROI number is defensible without the client’s incident and engineering data.
The outcome, plainly: Inventory live AI use cases, build a representative evaluation set, quantify failure modes, and deliver a prioritized remediation and deployment roadmap.
Inputs in. A cited, review-ready result out. Your expert decides.
A AI evaluation, governance and readiness audit. Every material fact is grounded in an allowed source and returned with its identifier, no invented data.
The client’s existing production models plus at least one challenger model for comparative evaluation; deterministic graders and human adjudication; model-as-judge is never the only acceptance signal. Braintrust, LangSmith or custom pytest-based eval harness; OpenTelemetry-compatible traces.
- Representative inputs/outputs
- Policies
- Failure reports
- User feedback
- Model settings
- Data flows
AI-Native, not autonomous. Judgment stays with your people.
The machine does the work; the human’s role narrows to the one thing that matters, judgment. That constraint is what makes it safe to deploy.
Business, legal, security and domain owners approve risk tolerance and go-live criteria; the audit does not certify regulatory compliance.
A scorecard, not a demo. We baseline what breaks in production.
Every deployment ships with an evaluation suite. These are the numbers we baseline before launch and monitor after.
The category is crowded. Most of it isn’t built for your workflow.
Fixed-scope, tuned to your systems and rules, grounded in your data, with the human gate and audit trail built in from day one. A price you own, not a subscription you rent.
It plugs into the stack you already run.
No rip-and-replace. Access is scoped to the minimum data necessary, isolated per tenant, and fully logged.
Transparent by design. The build price buys the workflow and the proof.
A fixed implementation fee plus a monthly bill that scales with volume and governance. No hidden seats.
- ✓ One process / scope
- ✓ Live workflow on your data
- ✓ Baseline evaluation suite
- ✓ Measured vs. current process
- ✓ Full scope & integration
- ✓ Human-review UI & audit trail
- ✓ Write-back to your systems
- ✓ Production evals & monitoring
- ✓ Multi-facility rollout
- ✓ Advanced security & compliance
- ✓ Custom control & escalation
- ✓ Dedicated evaluation program
Scheduled regression runs over 250 to 5,000 cases per month; no always-on production agent assumed.
Planning assumptions, not vendor quotations. Your AI and other platform licenses are separate and owned by you. Figures confirmed during scoping.
“His vast knowledge of technologies and a natural problem-solving mindset consistently lead us through complex challenges with clarity and confidence.”
Questions serious buyers ask.
Does the AI act on its own?
No. Business, legal, security and domain owners approve risk tolerance and go-live criteria; the audit does not certify regulatory compliance. The system drafts and recommends; a human approves every consequential action. Explicitly excluded: no compliance certification, penetration test, or model-safety guarantee; no conclusion from toy benchmarks alone; no use of production data outside approved scope.
How do you stop it inventing facts?
Every material claim is grounded in an allowed source record and returned with its source identifier. The system separates observed facts, model inference, and missing information, and routes to a human whenever confidence is low, evidence conflicts, or an adverse outcome is possible.
What does it cost to run each month?
A usage bill of roughly $150 to $1,100 per month (scheduled regression runs over 250 to 5,000 cases per month; no always-on production agent assumed), plus a $1,400 per month managed retainer for evaluation, monitoring and maintenance. Your existing platform licenses are separate and already yours. Exact figures are confirmed during scoping.
Do we need a ChatGPT or Claude subscription?
No consumer ChatGPT or Claude subscription is required for the production workflow. The client needs an approved API/cloud billing account. Workspace seats are optional for internal prototyping and administrator access.
How is this different from Model provider services?
Tools like Model provider services, Credo AI, and Arthur are broad platforms you adapt to. This is a fixed-scope implementation tuned to your systems and rules, grounded in your data, with the human gate and audit trail built in, and a transparent price instead of a seat subscription.
How long until it is live, and how do we prove it works?
This is a launch now. We baseline “Task success” first, then measure against that baseline. You see the scorecard before expanding scope: the evaluation suite ships with the system, not as an afterthought.
Bring your real numbers. Leave with a fixed-scope plan.
A 30-minute engineering-led working session, no slideware. You leave with a sized opportunity estimate, a fixed-scope pilot plan, and the integration & human-review path mapped.
VP of Growth at ViitorCloud · senior delivery owner confirmed before paid work