A capable team. A clear way to work.
Your specialists know why the process works. Our engineers turn that knowledge into software your team can use and maintain. AI helps with investigation, implementation and testing, while experienced people make the decisions that affect the result.
Pavlo Konovalov leads Varex as CEO, with a background in software and AI engineering.
Expertise shows up in the decisions
AI/ML engineers choose how the system retrieves information, uses models and evaluates results.
Architecture and backend engineers connect data and tools, enforce permissions and handle failures without losing the state of the work.
Frontend engineers make it clear what the software has done, what is in progress and where a person needs to decide.
Your domain specialists define policy, identify exceptions and judge whether the result solves the problem.
How we use AI during development
Engineers define the task and acceptance criteria. AI helps investigate code, propose an implementation or draft tests. Engineers inspect the changes, run the checks and decide what to revise. Your team reviews a result against agreed criteria, rather than having to judge progress by the amount of code produced.
This development process is separate from how an agent operates inside your product.
How your agent runs and improves
The agent loop completes a task
The agent reads the request, retrieves permitted context, takes an allowed action and checks the result. It then continues, asks a question, hands over or stops. Tool permissions, retry limits and approval points bound the task. Before reporting success, it must verify the action's outcome.
The improvement loop changes the system
Reviewed failures become test cases. Engineers diagnose the cause, change the prompt, retrieval, tool logic or application code, then compare the candidate with the current version, including cases that already work. Your specialists help judge the results. The responsible team approves the release and monitors its effect.
Model training, including fine-tuning and reinforcement learning, is a separate process. Customer feedback does not automatically retrain the model.
Our public platform documentation covers tool calling, tracing and evaluation.
From first scope to release
Resolve the hardest unknown early. Agree the first result and test the dependency most likely to block it. Finding an access or integration problem at this stage is more useful than discovering it after the wider build is funded.
Review a working path. Test the complete task, including errors and handoffs. Your team can see what works, what remains uncertain and what a further investment would buy. Keep successful cases in the test set so improvements do not quietly undo them.
Release with control. Agree action limits, approval points, monitoring and responsible people, including how to pause or roll back. Approve release against the acceptance criteria, then use operating results to guide further work.
Make the economics visible
A suitable existing component removes work from both the build and the maintenance plan. AI assistance reduces routine implementation effort. We make clear where each is used, so you can see what you are paying to create and what you do not need to fund again.
Count the full cost: engineering, your team's preparation and review, model and tool usage, hosting and ongoing support. For automation, compare that with the work removed at the required quality. For a product build, assess the delivered capability and the effort to maintain it.
Plan the handover
The next team should not have to rediscover how the system works. We plan code, documentation and evaluation cases for the people maintaining it, with rights, licenses and support responsibilities agreed up front. Hosting and ongoing maintenance have an explicit scope, so the handover does not leave an invisible cost behind.