AI Testing Services

The problem

AI features fail differently than traditional software. A chatbot can be technically “up” while giving wrong, inconsistent, or unsafe answers. An agent can call the right tool with the wrong arguments. A RAG pipeline can retrieve the wrong document and still generate a fluent, convincing answer. These failure modes rarely show up as a crash — they show up as a bad answer that looks fine at a glance.

That is what AI testing services are for: structured QA on the product’s use of a model, not a bake-off of foundation models.

What’s at risk without dedicated AI testing

• Hallucinated or fabricated answers reaching real users
• Agents calling tools with malformed or unsafe arguments
• Chatbots losing context or contradicting themselves across a multi-turn conversation
• RAG systems answering confidently from the wrong source, or no source at all
• Silent regressions after a prompt tweak or model upgrade
• Inconsistent behavior across voice, chat, and API surfaces for the same feature

What we test

This page is the parent workstream. Specialist pages go deeper when you already know the failure mode:

• LLM evaluation
• AI agent testing
• RAG evaluation
• Chatbot testing
• AI red teaming
• Guardrail testing
• Hallucination testing

Practical activities on a typical engagement include conversational and voice AI testing, prompt and instruction-adherence checks, context retention, multi-turn paths, factual-accuracy validation, tool-call and agent-action validation, extracted-data comparison, intent classification, guardrail and safety checks, human-in-the-loop review, and healthcare AI workflow testing where process adherence matters.

Conversational and voice AI

Conversation flow, turn-taking, interruption handling, context retention, fallback and escalation, and tone consistency — across chat and voice. Detail lives on chatbot testing.

LLM-powered workflows

Prompt behavior under normal and adversarial inputs, context-window handling, output format, and quality across model or prompt versions. Detail lives on LLM evaluation.

AI agents and tool-calling

Whether the right tool is selected, whether arguments are valid and safe, and how the agent recovers when a tool fails. Detail lives on AI agent testing.

RAG and search-grounded AI

Retrieval accuracy, source grounding, citation correctness, and hallucination risk when context is missing. Detail lives on RAG evaluation.

Hallucination, guardrails, and adversarial use

Fabricated facts and invented sources — hallucination testing. Filters you already deployed — guardrail testing. Jailbreaks and abuse — AI red teaming.

Product surfaces, not just prompts

We still test the feature inside the web or mobile experience, and the API around it: errors, latency, and what happens when the model provider is slow or rate-limited.

Our approach

AI testing works as structured test design, targeted automated evaluation, and human review.

1. Scope the AI surfaces and define what “correct” looks like for your use case
2. Build scenario sets for normal use, edge cases, and adversarial inputs
3. Run evaluation — automated where it fits, human review where judgment is needed
4. Track hallucination risk, tool-call accuracy, and retrieval grounding as findings
5. Re-run regression after every prompt, model, or retrieval change
6. Deliver a QA report your team can act on

This work can sit inside QA outsourcing, a dedicated QA team, or QA as a service around a release.

Tools we have worked with

Tooling varies by project. Depending on the architecture, we have used LangSmith, Langfuse, Promptfoo, Ragas, DeepEval, OpenAI Evals, and Prompt Flow. That is not a partnership, certification, or affiliation claim.

Deliverables

• AI test scenario set covering your product’s real use cases
• Findings on hallucination risk, tool-call accuracy, and RAG grounding
• Regression suite re-run after prompt or model updates
• A written QA report, not a maturity score

Suitable for

Teams shipping a chatbot, voice assistant, AI agent, LLM-powered feature, or RAG product who need structured QA — especially teams changing prompts or models often.

FAQs

Do you test the model itself, or our product’s use of it?

We test how the AI feature behaves inside your product — prompts, retrieval, tool calls, and the user experience — not foundation-model benchmarking.

How do you detect hallucinations without ground truth for every answer?

We combine checks against known-correct sources, grounding checks for RAG, and human review where judgment is required. See hallucination testing.

What happens after we change a prompt or upgrade a model?

We re-run the regression set so you know whether behavior held or degraded.

Do you handle HIPAA or SOC 2 audits?

No. We test functional and quality behavior of AI features. Compliance evidence is a separate specialist process.

Is this the same as AI-powered test automation?

No. AI-powered test automation is using models to help write or run tests. This page is testing an AI product.