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RSV Global / Services / AI System Review

An independent review
of the system around the model.

An AI system review is not a generic code audit. It asks whether the workflow can be trusted in operation: what data is authoritative, what the model may act on, how quality is measured, how failure is detected, and where a human has to intervene.

Bring us a workflowSee the work
What we deliver

Ten things that decide whether it survives production.

01Architecture and data authority. State, retrieval, source-of-truth decisions, integration ownership and the boundary between generated and authoritative data.
02Evaluation and release gates. Test sets, scoring, regression thresholds, eligibility rules and the difference between an advisory check and a blocking one.
03Permissions and action boundaries. What the model may read, recommend, write, publish, send, or never do without explicit approval.
04Injection and tool exposure. Where untrusted content reaches the model, and what it can reach in turn.
05Observability and resilience. Logging, retries, fallbacks, rollback, provider concentration, maintenance ownership and incident recovery.
06Cost behaviour. What the system costs at current volume, and what it costs at the volume you are planning for.
How we approach it

Architecture first. AI compresses implementation afterwards.

The workflow, boundaries and acceptance criteria are written down before implementation. That lets AI accelerate delivery without quietly becoming the architect.

01

Read the system

Architecture, code, prompts, evaluation and the operating history, not just the description of it.

02

Find the failure modes

Rank findings by severity, each with a concrete scenario in which it actually breaks.

03

Split the remediation

What has to be fixed before scaling, and what can wait, stated plainly.

04

Hand it over

The findings are yours. Fix them with us, your team, or anyone else.

Where AI does not belong

Findings come with the scenario, not just the label.

A finding that says "insufficient evaluation coverage" is not actionable. A finding that says which input produces a wrong output, and what it costs when it reaches a customer, is. Severity without a failure scenario is decoration.

Proof

Systems we built and run ourselves.

Not client work. These are our own products, carrying our own risk, which is why the boundaries sit where they do.

Evidence boundaries in production

AI Economy Ledger

Missing evidence stays missing rather than being interpolated into a complete-looking total, with rights, review state and provenance kept as separate concerns.

Rules for AI near production

Agent governance standard

A versioned, harness-agnostic operating standard governing how AI coding agents contribute to a codebase and what they may do near production.

Other services

Not quite the right door?

Custom Software DevelopmentGTM AutomationAI and Workflow AutomationAI Agent DevelopmentSoftware Maintenance and Modernisation
A useful first conversation

Show us the workflow everyone has learned to tolerate.

We will help determine whether it needs a focused product, an autonomous operating system, a stronger data layer, a review of what already exists, or a simpler fix that involves no AI at all.

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