LLM Application & Chatbot Testing
Evaluate whether your LLM application stays accurate, useful, consistent and secure across real conversations, changing prompts, connected knowledge and model updates.
The Challenge
LLM applications and chatbots now sit in front of customers, employees and internal workflows across sectors such as customer support, internal knowledge assistants and recruitment. They answer questions, complete tasks, call tools and draw on connected knowledge — but their behaviour shifts as prompts, models and data change.
Because these systems are non-deterministic, the same input can produce different answers over time. Failures are often silent: a confident but unsupported answer, a task left half-finished, or a policy applied inconsistently across users and phrasings.
We evaluate your LLM application against representative and adversarial scenarios so you can see where it fails before your users do — and keep it reliable as it evolves. Recruitment is one example sector we work in; see our recruitment industry page for that context.
What Can Go Wrong
Common failure modes we evaluate in LLM applications and chatbots
Unsupported or Hallucinated Answers
The system produces confident answers that are not supported by its sources or grounding data.
Incomplete Task Completion
Multi-step tasks are started but not finished, or reported as done when they are not.
Policy Inconsistency
Rules, disclaimers or refusals are applied inconsistently across users, sessions or phrasings.
Quality Loss After Changes
A prompt change or model update quietly breaks behaviour that previously worked.
Data Leakage
The application exposes confidential, internal or personal information it should not reveal.
Prompt Injection
Crafted input overrides instructions, bypasses guardrails or triggers unintended tool actions.
Inconsistent Treatment
Equivalent requests receive materially different answers depending on wording or user profile.
Misused Connected Knowledge
The system ignores, misreads or misattributes information from connected knowledge sources.
Our Testing Methodology
A systematic approach to evaluating LLM application behaviour
Map the Application
We document your application, prompts, models and integrations, and the tasks it is expected to handle.
Build Scenarios
We construct representative and adversarial scenarios that reflect real usage and likely abuse.
Evaluate Behaviour
We evaluate output, conversation and workflow behaviour against expected outcomes and policies.
Convert to Reusable Test Coverage
We turn confirmed failures into reusable tests that verify the problem does not return after every change.
What You Get
Findings Report
Documented failure modes with reproducible evidence, severity and business impact.
Evaluation Dataset
A representative and adversarial scenario set tailored to your application.
Reusable Test Coverage
Reusable tests so prompt and model changes can be checked before release.
Evaluate Your LLM Application
See where your chatbot or LLM application fails — and keep it reliable as it changes.
Discuss a Partner Pilot