AI Testing

Quality assurance for AI systems, from teams who understand which AI tools actually do which jobs well.

The challenge

AI is being embedded into financial services products at a pace that has significantly outrun the quality assurance practices designed to work alongside it. Credit decisioning models, fraud detection systems, customer-facing LLM-powered interfaces, document processing pipelines, risk scoring engines — all of these are now in production at FinTech and financial services companies, and most of them have been tested using approaches that were designed for deterministic software rather than probabilistic systems.

The fundamental problem is that traditional testing assumes that the same input always produces the same output. That assumption does not hold for AI systems. A large language model producing a customer communication might produce a subtly different response to the same prompt on two consecutive calls. A recommendation engine might produce outputs that are individually defensible but collectively biased across a specific demographic. A fraud model might perform well on the data distribution it was trained on and degrade quietly as transaction patterns evolve in production. None of these failure modes are visible to standard functional testing.

There is a second problem that sits alongside the technical one. Most organisations implementing AI tools are doing so under significant time pressure and with limited visibility of how different AI technologies actually behave in practice. The decision of which LLM to use, which AI platform to build on, which evaluation framework to apply — these are frequently made based on vendor marketing and peer recommendations rather than on an informed assessment of what the specific use case actually requires.

RAPD's AI testing capability exists to solve both problems. We test AI systems using techniques designed for probabilistic, non-deterministic behaviour — and we provide the advisory input to help organisations select and configure AI tools that are genuinely suited to their specific use case rather than simply available or currently fashionable.

This is the right conversation if...

You are deploying an LLM-powered feature — a customer service interface, a document summarisation tool, an internal knowledge assistant — and you need to know it behaves accurately and consistently before it goes live.

You have a machine learning model in production and you are not confident that it is performing as expected as the data distribution around it evolves.

You are evaluating AI tools and platforms for a specific use case and you want independent advice on which option is genuinely suited to what you are trying to achieve.

You are in a regulated environment and you need to demonstrate to internal governance or external regulators that your AI systems have been tested appropriately, including for bias, fairness and accuracy.

What this covers

AI Model Testing and Validation

Testing AI systems requires a different discipline from testing conventional software. RAPD applies techniques designed specifically for the characteristics of machine learning models and probabilistic systems.

Output consistency and accuracy testing: Evaluating model outputs against defined accuracy benchmarks and assessing consistency across repeated inputs under realistic conditions.
Hallucination and factual drift testing: For LLM-based systems, systematic testing of factual accuracy, source reliability and the conditions under which the model produces incorrect or fabricated outputs.
Bias and fairness testing: Evaluating model outputs across demographic groups, input distributions and edge case populations to identify differential treatment that may be unintended or impermissible under regulatory frameworks.
Model regression testing: Ensuring that model updates, retraining cycles and version changes do not degrade performance on previously validated use cases.
Data pipeline and training data quality: Assessing the quality, completeness and representativeness of training data as a direct input to model behaviour.

AI Tool Selection and Implementation Advisory

RAPD brings genuine working knowledge of the AI tooling landscape — which models perform well for which categories of task, which platforms are suited to which deployment contexts and which evaluation frameworks are appropriate for which use cases. This is not general awareness built from reading vendor documentation. It is working knowledge of how different AI tools behave in practice.

Use case analysis: Understanding what the AI system is actually being asked to do before any tool recommendation is made.
LLM evaluation and selection: Independent assessment of which large language models are best suited to specific tasks, covering cost, performance, latency, accuracy and compliance considerations.
AI platform and infrastructure review: Evaluating the suitability of AI development and deployment platforms for the organisation's specific technical environment and governance requirements.
Implementation quality review: Assessing how well an AI tool has been integrated into the surrounding system, covering prompting strategies, context management, fallback behaviour and error handling.
Evaluation framework design: Building the ongoing evaluation infrastructure so AI system quality can be monitored continuously rather than assessed once at launch.

AI Governance and Compliance Testing

For financial services organisations, the regulatory dimension of AI is not optional. RAPD provides the testing and documentation that internal governance and external regulators need.

Regulatory alignment testing: Testing AI systems against the requirements of applicable UK and international regulatory frameworks for algorithmic decision-making.
Explainability and auditability assessment: Evaluating whether AI-driven decisions can be documented, explained and justified to the standard that governance and regulators require.
Ethical AI review: Assessment covering fairness, transparency and accountability dimensions relevant to the specific use case and its context.
Governance documentation: Producing the evidence package that internal audit, compliance functions and external regulators need to satisfy their oversight requirements.

How we work together

Define

We establish what the AI system is supposed to do, what good looks like, and what the regulatory and governance requirements are before designing any test approach. This requires understanding your specific use case and its context, not applying a generic framework.

Design

We build an evaluation approach specific to your system — the metrics, techniques, data requirements and tooling that are appropriate for what you are testing.

Evaluate

Systematic testing across model outputs, data quality, system integration and governance requirements. Findings documented in a format that serves both technical and governance audiences.

Advise and support

Where tool selection, implementation improvement or ongoing monitoring is required, RAPD provides advisory input and stays involved through the decisions that follow from the findings.

Flexible delivery, your way

RAPD operates full AI testing and advisory capability across both the London and Hyderabad teams. Specialist AI testing skills exist in both locations. The structure of the team is determined by the engagement requirements and client preferences. Some clients want their AI governance and advisory work led from the UK. Others want the cost-effectiveness of the India team for the technical evaluation work. Many use both. The decision is always yours.

Why RAPD

We know which AI tools actually work for which jobs

The AI tooling market is full of confident claims that do not survive contact with specific use cases. RAPD has working knowledge of which LLMs perform well for different categories of task, which evaluation frameworks are appropriate for different model types and which implementation approaches produce reliable results versus those that look good in a demo.

We test AI the way AI needs to be tested

Probabilistic systems fail in ways that deterministic testing does not find. RAPD applies techniques designed for non-deterministic behaviour — statistical sampling, adversarial prompting, distribution shift testing, longitudinal performance monitoring — rather than adapting functional test scripts to a problem they were not designed for.

We understand the regulatory context

For financial services AI, the regulatory dimension is not optional. RAPD's experience in FinTech and regulated environments means we understand what evidence internal and external stakeholders need and how to produce it in a form that satisfies governance requirements.

Questions we get asked

Do you test AI systems built on any platform or model?

Yes. RAPD does not have platform or vendor constraints. We test AI systems regardless of which underlying model, cloud provider or AI development platform has been used.

Can RAPD help us decide which AI tool to use before we have built anything?

Yes, and this is often the most valuable point to engage. Selecting the wrong tool early creates problems that are expensive to fix. RAPD can provide an independent evaluation of which options are genuinely suited to your use case.

How is AI testing different from what our existing QA team does?

Your existing QA team applies techniques designed for deterministic software. AI systems behave differently — same input, potentially different output. The testing techniques, metrics and evaluation frameworks required are substantially different. This is a specialism, not an extension of conventional testing.

We have an LLM-powered feature going live soon. What does AI testing look like at short notice?

A focused pre-launch AI evaluation can be structured around what matters most for your specific use case — accuracy, consistency, hallucination risk or governance evidence. We design the scope around your timeline rather than applying a fixed engagement model.

If you are deploying AI into a regulated environment and you are not certain how it has been tested, this is the conversation to start.

Talk to RAPD about AI testing, model evaluation or AI tool selection for your specific use case.

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