Improve Answer Quality
Identify incorrect, incomplete, unsupported, or off-task responses.
LLM & AI Application Testing Services
Release AI with Evidence,Not Assumptions.
Codoid tests the quality, reliability, and safety of applications powered by large language models (LLMs), including knowledge assistants and AI agents. Understand where your AI works, where it fails, and what needs to change before your next release.
Tell us what you are building, your release goals, and your biggest testing concern.
Trusted by 200+ Startup & Fortune 500 companies
The Need for AI Testing
Test the Answer. Verify the Action.
- An answer can sound right but lack evidence.
- An agent can say “Completed” without changing the application.
- A model update can improve one task and break another.
Codoid helps your team look beyond a convincing demo. We evaluate AI responses, check real outcomes, and turn failures into evidence your developers can act on.
Verify Completed Work
Check that agents take the right actions, respect permissions, and recover safely when something goes wrong.
Make Informed Release Decisions
Compare changes against a baseline and understand unresolved issues, coverage gaps, and remaining risks.
Built for Modern AI Applications
Built for AI Applications That Answer and Act
We test customer-facing chatbots, internal copilots, retrieval-augmented generation (RAG) systems that use your documents, and agents that call tools or work with other agents.
Customer-Facing Chatbots
Support and sales assistants your customers talk to.
AccuracyHelpfulnessSafe responses
Internal Copilots
AI assistants your teams use every day.
Cited sourcesCompany policyPermissions
RAG Systems
Assistants that answer using your documents.
RetrievalGroundingDocument access
AI Agents
Agents that call tools or work with other agents.
Tool callsApprovalsRecovery
Our Service Scope
What We Test
Choose a focused assessment or a broader testing program around your application, risks, and release goals.
01
AI Answers and Evaluation Datasets
Build and maintain test datasets, evaluate correctness, relevance, completeness, instruction following, and output formats. Includes LLM-as-a-Judge evaluators.
02
Retrieval Quality and Unsupported Answers
Test whether RAG retrieves relevant documents, supplies enough evidence, and investigate hallucinations and unsupported claims.
03
Prompt, Model, and Configuration Changes
Compare new prompts, models, and settings with a stable baseline. Check for regressions, reliability, response times, and cost.
04
Agent Workflows and Conversation Memory
Verify task completion, test multi-agent workflows, routing, handoffs, and what the application remembers across steps and conversations.
05
Tool Integrations, Approvals, and Recovery
Test MCP servers and connected tools, permissions, input validation, timeouts, and recovery from failures.
06
Security, Privacy, and Responsible AI
Assess prompt injection, data leaks, unsafe content, and policy violations. Validate PII handling and compliance requirements.
Our Approach
A Practical Approach to AI Testing
We tailor our approach to your application, risks, and release goals.
- 01
Understand Your AI Application
Learn your use cases, risks, and success criteria.
- 02
Build Test Datasets and Evaluators
Create representative test sets and scoring methods.
- 03
Run Evaluations and Tests
Assess answers, actions, retrieval, tools, and safety.
- 04
Share Clear Results
Provide actionable findings with examples and recommendations.
Technical Expertise
Fluent in the AI Evaluation Stack
Our engineers build evaluation suites with the frameworks AI teams use in-house, and know where each one falls short. Every automated score we report is checked against human judgment.
+ MoreAgentEvals, OpenTelemetry and your in-house tools
Calibrated LLM JudgesChecked against expert reviewers for bias and stable grading.
Statistics, Not Single RunsPass rates and confidence intervals across repeated trials.
Trace-Level Root CauseFailures followed through prompts, retrieval and tool calls.
Regression Gates in CISuites that run on every build against the last release.
Client Success
AI Testing Case Studies
Results from real AI engagements.
01 / 03
SpectraAgentic AI
ChallengeRepeated questionsand inconsistent resume scores Result90+ bugsidentified and tracked
Testing the AI Behind an Interview and Resume-Scoring Platform
A deep-tech startup built Spectra, an agentic AI interview platform. Its generated questions repeated within sessions and its resume scores were inconsistent. We tested the Admin, Tenant and Candidate portals end to end and built automation that checks the AI’s outputs, not just the user interface.
- Automation coverage
- 80%
- Test cases executed
- 165+
- Bugs identified and tracked
- 90+
- Portals tested end to end
- 3
What we delivered
- Keyword-based checks that catch repeated or illogical AI-generated questions
- API tests that measure resume-scoring consistency across varied candidate profiles
- Guardrail recommendations for question and scoring randomness
Tools used
SeleniumCucumberMavenJenkinsJira
Property InsightsReal Estate
Internal data98%prediction accuracy observed External data40%accuracy found in validation
Finding the Gap Between Lab Accuracy and Real-World Predictions
A property deal analysis platform shows AI-predicted prices and investment insights for every property. Internal results looked strong. We tested the model end to end, from the data pipeline to the page, and against external datasets to see how it held up in realistic conditions.
- Internal prediction accuracy observed
- 98%
- Accuracy on external data validation
- 40%
- Datasets reconciled: EPC, sales, postcode
- 3
What we delivered
- Actual-versus-predicted analysis in Pandas to find error patterns
- Consistency checks from data ingestion through to the values users see
- Recommendations on feature selection, retraining and model trustworthiness
Tools used
PythonPandasNumPyPostmanJiraDevTools
UK Rental PlatformReal Estate
Images tested2,500property photos Consistency94%classification consistency
Validating an Image-Classification Model Inside a Live Rental Platform
A UK rental platform uses a CNN model to grade property condition from photos, alongside rent prediction, agreements and payments. We validated the model’s accuracy and consistency, and the business workflows that depend on its output.
- Test cases validated
- 1,180+
- Images tested
- 2,500
- Classification consistency
- 94%
What we delivered
- Accuracy and robustness checks on a ResNet-based classifier, including poor-quality and ambiguous images
- End-to-end tests from image upload to rent prediction and agreement creation
- Performance and access-control testing under concurrent load
Tools used
PlaywrightPythonPandasPostmanJMeterBurp SuiteJenkinsJira
Example Scenarios
Common AI Testing Scenarios
Whatever you are building, we test it the way your users will use it.
Let’s Make Your AI Release-Ready
Get a clear view of what works, what fails, and what to fix, before your next release.
RAG Knowledge Assistant
Verify answers against your documents and reduce hallucinations.
Support Chatbot
Ensure correct, helpful, and safe responses to customer questions.
Internal Copilot
Check accuracy, cite sources, and follow company policies.
AI Agent with Tool Access
Verify actions, permissions, approvals, and recovery from failures.
Common Questions
LLM & AI Application Testing, Explained
Answers for teams testing chatbots, RAG systems and AI agents.
What types of AI applications do you test?
Customer-facing chatbots, internal copilots, retrieval-augmented generation (RAG) systems that use your documents, and AI agents that call tools or work with other agents.
Do you only test LLM responses?
No. We also test what the application does: retrieval quality, agent workflows and handoffs, tool and MCP integrations, permissions and approvals, memory across conversations, and recovery from failures.
Can you work with our existing test data and tools?
Yes. We can build on your existing test data and evaluation tools, and add the datasets, evaluators and frameworks needed to cover the gaps.
How do you evaluate AI responses?
We test against representative datasets with expected answers taken from your sources. Responses are scored for correctness, relevance, completeness, instruction following and output format, using LLM-as-a-Judge evaluators checked against human reviewers.
Do you test for security and privacy risks?
Yes. We assess prompt injection, data leaks, unsafe content and policy violations, and validate PII handling and your compliance requirements.
How long does an AI testing engagement take?
It depends on the scope. You can start with a focused assessment of one application or risk, or set up an ongoing testing programme that runs with every release. We agree the timeline when we plan the work together.
Talk to our experts
Discuss Your AI Testing Needs
Awards & recognitions
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