The AI workflow—and the software that makes it work.
Our team connects agents, business systems, backend logic and interfaces into a complete working process. We can own the delivery or bring the expertise your engineers need.
That can mean automating a support process, building a product feature or connecting a system your team already uses. Varex brings AI/ML, backend, frontend and architecture expertise to the same piece of delivery.
What we build and improve
AI agents with a specific job
Give an agent the context and tools to complete a defined task. Decide which actions are allowed, what needs approval and when it should stop or hand over. Enforce those boundaries in application logic, not only in a prompt.
Use a fixed workflow when the steps are known and it is the more reliable choice. More autonomy is useful only if it improves the result.
Product features and interfaces
Build the frontend and backend work that makes a feature usable: API contracts, state, permissions, error handling and review screens. AI may be central to the feature or one component within it.
The interface should make it clear what the system has done, what is still in progress and where a person needs to decide.
Knowledge and system integrations
Connect documents, account data and business APIs. Keep retrieval within the user's access rights, and test whether the system finds the right material and uses it faithfully.
A support agent, for example, needs to distinguish a policy in the knowledge base from a fact about the customer's account. Both matter before it takes an action.
Improving an existing AI system
Use traces and reviewed failures to locate the problem. The fix may be a source document, a retrieval change, a tool contract or ordinary application code. Replacing the model is one option, not a diagnosis.
Compare the changed version with the current one, including cases that were already working. Review what still fails before deciding to release.
Work with what you already have
We can start from an existing codebase and use capabilities already in your tools. Buying the Varex platform is not a prerequisite for an engineering project.
We identify what is worth keeping before proposing what to build. Reusing a sound API, interface or workflow means less code to commission now and less to maintain after release. Responsibilities and dependencies are agreed before implementation.
A professional team, amplified by AI
AI helps with code investigation, routine implementation and test drafts. Our engineers choose the approach, review the changes and verify them against the requirements.
That leaves specialist attention for architecture, integration and difficult edge cases. We measure the benefit through the effort needed to deliver a reviewed result, including rework, rather than the amount of code generated.
Fit the engagement to the uncertainty
A small first milestone can answer the question that would otherwise hold up the whole project: can the required data be accessed, can the action be performed safely, or can the feature meet its quality bar? Resolve that uncertainty before funding the wider build.
For a clear deliverable, we can discuss fixed-price work. Where requirements need to evolve, time and materials may fit better.
Agree the output, acceptance criteria and what your team needs to contribute. The first result should help you make the next decision.
For software companies and integrators
Keep ownership of the client relationship and bring in specialist engineering where the project needs it. Reusable product capabilities can reduce repeated build work; a defined scope makes the remaining custom work easier to estimate. We agree responsibilities, client communication and commercials so you can plan delivery costs and protect your margin without guessing what sits outside the quote.
For AI support, you can also offer the Varex product under your own brand.