Generate test cases, build automation scripts, and analyze failures with AI-powered testing. Codoid connects AI to your project knowledge and adds engineering oversight, so your team can focus on coverage, product risks, and release decisions.
Tell us what is slowing your team down. We’ll discuss where AI assistance could fit.
Trusted by 200+ Startup & Fortune 500 companies
Test design, scripting, and failure investigation can take up valuable time, leaving less room to focus on critical risks. We help teams leverage AI in software testing without having them give up control over the testing process.
Use requirements, past defects and project knowledge to identify edge cases, missing scenarios and relevant regression tests.
Use AI to draft scripts and organise failure evidence, so engineers can focus on review and resolution.
Define what AI may access, what it may do and when an engineer must approve the next step.
Choose the capabilities your team needs, from drafting tests to reviewing failures.
Tell us what takes up your team’s time and we’ll suggest the capability that fits.
Talk to a Testing ExpertTurn requirements, user stories, API specifications, logs and existing code into draft test scenarios and edge cases. We generate synthetic test data, API and UI tests, BDD scenarios in Given/When/Then format, and regression tests for engineers to review.
Move from approved test cases to automated scripts. We use AI to draft code, then engineers review the checks, test data and error handling and run the scripts in the agreed test environment.
Build AI agents that carry out defined testing tasks, such as drafting test cases, developing scripts and reviewing changes. We connect each agent to approved tools and define when it should act, ask for approval or stop.
Give AI the context behind your application. We connect it to approved Jira and Confluence content, test repositories, API specifications, source code, previous defects and production logs. This approach, called RAG, brings relevant project information into the AI’s task.
Use AI to suggest whether a failure points to an application defect, automation defect, environment issue, test-data problem, flaky test or infrastructure issue.
Analyse historical execution results to flag unstable tests and investigate likely causes, such as timing, shared data or environment changes.
Decide which AI recommendations can run automatically and which need an engineer’s approval. We define review points for new scripts, test changes, tool actions and access to sensitive information.
Set rules for how AI systems handle source code, customer information, production data, credentials and sensitive test data. We agree approved models, access permissions and retention requirements.
Use AI to review test cases, automated scripts and frameworks for missing coverage, duplicate tests, weak checks and maintenance issues. Engineers assess the findings before recommending changes.
We focus on a specific problem, agree the scope and show measurable results.
Tell us what is slowing your team down.
We define capabilities, integrations and approval rules.
We connect the tools, build the solution and test it with your team.
We review the results and expand to additional areas when it makes sense.
TelecomAn international telecom provider offers an app for managing accounts, paying bills and transferring money. Its in-house team tested the app manually but struggled to automate it, so it needed a partner that could work alongside them.
eLearningA US education provider was building an LMS that covers everything from student admission to assessment results. After an earlier automation attempt failed, it needed an offshore partner for regression testing. We have been its QA partner since 2015.
We combine AI capabilities with hands-on testing experience, practical implementation and ongoing support.
Focus on outcomes that improve your testing.
AI recommendations validated by engineers.
Works with your tools, frameworks and processes.
Help your team adopt, measure and grow.
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