From AI software quality assurance and AI engineering consulting to AI agent evaluation and development, we work with your team to bring AI into every step from requirements and code to testing and release. Models and solutions can run inside your company.
Works with the coding agents your team already uses
AI writes code faster every month, yet quality still rests on whoever looks last. We bring AI testing, code review and defect analysis into your delivery process, so every delivery comes with test results, evidence and a clear verdict.
You bought AI coding tools, but they do not stick on real projects. We start with one pilot, map how requirements, code and testing work today, design how AI takes part, train the team, and expand once it has proven itself.
Before your own AI agent, RAG or LLM feature ships, you need to know whether it is right, stable and stays within its permissions. We build evaluation sets and scoring criteria with you, so launch decisions rest on evidence and a model or prompt change can be re-checked quickly.
You want to hand part of engineering or the business to an AI agent but lack the engineering experience. We pick the use case and design with you, build the agent, connect it to internal systems and knowledge, and accept it with the same evaluation method before handing it over.
Private deployment
The product, the models and the solutions can all run inside your company. How is set by your network, permissions and compliance needs; book an evaluation for your setup.
Timo supports corporate networks and private deployment; private deployment of the CodexQA platform can be evaluated. It connects to your repositories, CI, test environments and knowledge base, with a full audit boundary kept.
Deploy models inside your company or connect the internal models you already run, including models from Chinese providers. The AI roles work with those models directly.
Solutions such as quality assurance and agent evaluation are deployed inside your company together with their process, skills and evaluation sets, for your team to run and maintain.
The CodexQA platform will also be offered as SaaS, opening module by module; see the roadmap.
We would rather give you a result you can reproduce than a testimonial. This is codexqa-defect-analyzer on the open inventory-service example; the example and its answer key are public.
feature/reservation-v2→mainrefund.js:18 · §11Spec deviation§11refund.js:18Lowrefund.js:56 · §12Swallowed error§12refund.js:56Lowinventory.js:49 · §4Async race§4inventory.js:49Midpricing.js:32 · §8Off by one§8pricing.js:32Lowdb.js:186 · §14Resource leak§14db.js:186Midreservation.js:93 · §5Cross-file invariant§5reservation.js:93Highreservation.js:162 · §6Cross-method state§6reservation.js:162Mid$ node examples/inventory-service/verify.mjsPASS: 7 seeded defects reproduce, 4 decoys behave correctly.Security and governance
Where code and data live, who sees what and how it is audited are settled during assessment.
CodexQA Skills run inside the developer’s own agent, write reports to local disk and need no account. How source context is handled depends on the agent and model in use.
Use the models you choose or host internally, including models from Chinese providers.
The skill set is Apache-2.0 / MIT: instructions and scripts can be read line by line. The local engine six skills use is distributed separately and closed source.
Network, permissions and existing systems are assessed before deployment, with a full audit boundary kept.
Start with your team and a real project. Agree on scope, deliverables and acceptance criteria, then roll AI software engineering out step by step.
Understand
What you want to improve, what you have tried and what constraints you face.
Scope
Choose a service and agree on deliverables, timing and acceptance criteria.
Deliver
Quality assurance, consulting, evaluation, development or deployment around a real project, reviewed together.
AI software quality assurance, AI engineering consulting (including team training), AI agent evaluation and AI agent development, plus private deployment of the product, the models and the solutions. Take one or combine them.
Yes. Timo and the solutions we deliver can run inside your network; private deployment of the CodexQA platform can be evaluated. We look at network, permissions and existing systems first, then agree on deployment, integration scope and acceptance criteria.
Yes. We can deploy models inside your company or connect the internal models you already run, including models from Chinese providers. Model choice and resources are settled during assessment.
Evaluation delivers evaluation sets, scoring criteria, a report and launch-gate recommendations. Development delivers a working agent, its integration with your systems and the evaluation results it was accepted on. Scope is agreed during assessment.
CodexQA Skills run inside the developer’s agent on their machine, without passing through CodexQA servers and with no account. With private deployment and internal models, code and data can stay entirely inside your network.
Skills install with the skills command into Cursor, Claude Code, Codex, GitHub Copilot, Windsurf and other agents that load skills.
Each service is quoted by scope, with deliverables fixed during assessment. Introductory training and advice for individuals is free.
With one real project: understand where you are, agree on scope, then deliver around real work and review the results together.
Tell us about your team and what is getting in the way. Let’s find a starting point.
Or email us：dev@clawyi.comOther ways to contact us