AI Evaluation & Testing

Know what your AI actually does before it goes live.

AI system audits, adversarial testing, and readiness assessments built on hands-on experience — not theoretical knowledge. If you're deploying AI into a business process, you need someone who has built AI systems and knows where they break.

AI evaluation and testing — HELP4BIS

Why This Matters

Most AI deployments are not tested properly before go-live.

Organisations invest in AI tools — chatbots, content generators, decision-support systems — and test whether they work technically. They rarely test whether they work correctly: whether the outputs are accurate, whether the system behaves well under adversarial input, whether it gives the right answer for the wrong reasons.

The background here is practical, not theoretical. AI systems have been designed, built, and run in production covering weather forecasting, financial analysis, business intelligence, and automated decision-making. That means knowing where these systems break, how to test for it, and what a good evaluation actually looks like.

  • Hands-on AI builder — not a theoretical reviewer
  • Domain expertise in technology, business process, financial analysis
  • Adversarial testing — the questions your users will eventually ask
  • Fixed-scope report — not an ongoing hourly engagement
Discuss Your AI System

Engagement Options

AI evaluation & testing engagements

Fixed scope. Defined deliverable. Price confirmed before work starts.

AI Readiness Assessment

Before you build or buy an AI system, assess whether your business data, processes, and team are actually ready for it. Most AI failures are not AI problems — they're data quality and process definition problems discovered too late.

$1,800 AUD ex GST

Delivers: Readiness report covering data quality, process definition, team capability, and realistic implementation risk. Go / no-go recommendation with conditions.

Enquire

AI System Audit

A structured evaluation of an existing AI integration — chatbot, content system, or automated decision tool. Tests for output accuracy, prompt boundary adherence, response quality, edge case handling, and user experience consistency.

$2,400 AUD ex GST

Delivers: Audit report with scored findings, example failure cases, prioritised remediation list, and configuration recommendations.

Enquire

Adversarial Testing Block

Systematic testing of your AI system using the inputs real users will eventually try — including the ones you didn't plan for. Identifies jailbreaks, scope violations, factual errors, and unexpected behaviour before your users find them.

$3,200 AUD ex GST

Delivers: Test log with inputs and outputs, scored vulnerability summary, specific prompt fixes, and system prompt hardening recommendations.

Enquire
Also Available

Domain Expert AI Evaluation

Available as a domain expert evaluator for AI training platforms — reviewing and rating AI-generated responses in areas of demonstrated expertise: software architecture, business process, financial analysis, data systems, and technology consulting.

$65–$87 / hour

Note: This work is conducted directly through AI training platforms. Get in touch if you're an AI company looking for domain expert reviewers — we can discuss suitability and availability.

Enquire

All client-facing prices shown are exclusive of GST. GST (10%) applies to Australian clients.

What the Evaluation Covers

What we actually test

AI audits that go beyond "does it respond" to "does it respond correctly and safely".

Output accuracy

Does the AI give correct answers? Does it hallucinate facts? Does it present inference as certainty? Accuracy testing across a structured set of domain-relevant inputs.

Scope boundary adherence

Does the AI stay within the role it was configured for? Tests whether the system prompt is doing its job and whether the AI can be redirected outside its defined scope.

Edge case handling

What happens with ambiguous inputs? Poorly formed questions? Out-of-scope requests? The edge cases are where most AI deployments fail in production.

Escalation behaviour

When the AI can't or shouldn't answer, does it handle the handoff correctly? Tests whether the system appropriately escalates to a human rather than guessing.

Response quality consistency

Does the same question get the same quality of answer across multiple runs? AI systems can be inconsistent — consistency testing reveals whether that's a problem for your use case.

Adversarial inputs

What happens when a user deliberately tries to break or redirect the system? Adversarial testing covers the inputs users will eventually try, whether you planned for them or not.

The Process

How an AI evaluation works

1

Brief the system

Tell us what AI system you've deployed, what it's supposed to do, and what concerns you have. Share access to the live system or a staging environment.

2

Scope confirmed

We confirm what will be tested, the format of the deliverable, and the fixed price. Signed off before work starts.

3

Evaluation conducted

Systematic testing against the agreed scope. All inputs and outputs logged. No guessing — documented evidence for every finding.

4

Report delivered

Findings report with scored issues, specific failure examples, and prioritised remediation recommendations. Actionable, not abstract.

Questions

Common questions

What AI systems can you evaluate?
Any AI integration accessible via a browser or API — chatbots, content generation tools, lead qualification systems, AI-assisted search, automated response systems. If it runs on Claude, OpenAI, Gemini, or a local model, we can evaluate it. We cannot reverse-engineer closed systems where there is no access to inputs and outputs.
Do you need the system prompt or source code?
For a full audit, access to the system prompt and configuration is helpful — it lets us understand intended vs actual behaviour. For adversarial testing, we can work black-box (access to the interface only, no prompt visibility), which mirrors the experience of a real user attempting to break the system.
Is the remediation work included?
Evaluation engagements deliver a report and recommendations — the fixes are not included in the evaluation scope. If remediation is needed, we can quote it separately or you can take the recommendations and apply them yourselves. Many issues (prompt hardening, scope restriction, escalation logic) are straightforward to fix once identified.
We built our AI system in-house. Will an external review cause problems?
The review is external and independent — that's the point. Internal teams are too close to what the system is supposed to do to test it effectively as a user would. The findings are delivered as an objective report, not a judgment on the development team.

Ready to test your AI system properly?

Tell us what you've built. We'll tell you what to test and what it'll cost.

Fixed price. Defined deliverable. Honest findings.

Discuss Your AI System View Engagements