Enterprise AI Chatbot Case Study
Enterprise conversational AI platform for virtual assistants.
Industry — AI & ML Products
AI Product Testing & Development
AI products fail quietly when answers drift or turn unsafe — we help AI product teams build features and measure their quality before every release.
Risks we eliminate
01
Inaccurate answers reaching users
Hallucinated answers erode trust fast. We measure accuracy against a test set built from your real use cases.
02
Silent breakage from prompt or model changes
A new prompt or model version can change behaviour overnight. We re-run evaluations on every change.
03
Unsafe or off-brand outputs
We test for harmful, biased and off-brand responses, and for attempts to bypass your guardrails.
LLM evaluation
We build an evaluation set from your product's real questions and expected answers, then score each model or prompt version against it. Your team sees a clear comparison before deciding what ships.
AI App Testing & Evaluation →What we test
- Answer accuracy against reference answers
- Retrieval quality in RAG pipelines
- Consistency across repeated runs
- Regression when prompts or models change
- Latency and cost per request
Tools
- Promptfoo
- Ragas
- DeepEval
- LangSmith
Hallucination & safety testing
We probe your AI features the way a curious or hostile user would. That covers made-up facts, prompt injection, data leakage and responses that break your brand or policy rules.
Security Testing Services →What we test
- Hallucinated facts and citations
- Prompt injection and jailbreak attempts
- Leakage of system prompts or private data
- Toxic, biased or off-brand responses
- Behaviour when the model should refuse
Tools
- Promptfoo
- Garak
- OWASP ZAP
AI feature builds
We design and build AI features — chatbots, assistants, search and agents — with evaluation tests written alongside the code. You get a working feature and the tests to keep it working.
AI Apps & Integration →What we test
- Chatbot and assistant conversation flows
- Tool and function calling
- Fallbacks when the model or API fails
- Integration with your product's data
- Usage limits and cost controls
Tools
- OpenAI API
- Anthropic API
- LangChain
- LlamaIndex
Standards we test against
- Testing that supports your EU AI Act readiness
- Testing that supports your GDPR readiness
- Testing that supports your OWASP Top 10 for LLM Applications readiness
Full-stack delivery for AI & ML Products
How we work with AI & ML Products teams
Timezone
Daily overlap with US and EU working hours
AI & ML Products FAQ
How do you measure LLM accuracy?+
We build a test set of real questions with expected answers, agreed with your team, and score each response with a mix of automated checks and human review. Results are reported per category so you can see exactly where the model is weak.
How do you catch regressions when prompts or models change?+
The evaluation set runs automatically whenever a prompt, model or retrieval setting changes. We compare scores with the previous version and flag any drop before the change goes live.
Do you test for prompt injection?+
Yes. We attempt direct and indirect prompt injection, jailbreaks and attempts to extract system prompts or private data, guided by the OWASP Top 10 for LLM Applications.
Can you build the AI feature and test it?+
Yes. Our AI developers build the feature and our QA engineers write the evaluation and safety tests alongside it, so testing is part of the build rather than an afterthought.