X-StudioServiceAI features people trust enough to use twice.
Most AI features fail for product reasons, not model reasons. They answer a question nobody asked, they are wrong just often enough to be abandoned, or the interface gives the user no way to tell. We build AI products and features that hold up in production: grounded in your data, evaluated before and after launch, and designed so people understand what they are getting.
The work spans engineering and design, because a retrieval pipeline and the interface that presents its answer are the same feature to the person using it.
Deliverables
What it covers
Search and answer systems over your own documents and data, with chunking, retrieval and citation designed for accuracy rather than demos.
Multi-step agents with tool use, guardrails and human checkpoints, built for tasks where a wrong action costs more than a wrong sentence.
Test sets, automated evaluation and production tracing, so quality is measured rather than assumed and regressions are caught before users find them.
Interfaces for uncertainty: confidence, sources, correction and control, so an AI feature feels like a tool and not a gamble.
How it runs
A discovery phase to identify where a model actually helps, what data exists to ground it, and how success will be measured.
A working pipeline on your data in the first weeks, evaluated on a test set you help write, so the decision to continue is based on results.
Tracing, evaluation and feedback loops go live with the feature, so it improves in production instead of decaying.
Related work
FAQ
The things people ask about this kind of engagement before the first call.
Anthropic, OpenAI and Google models through their APIs, and open-weight models where data has to stay in your environment. We choose per task and design the integration so the model can be swapped without rebuilding the product.
Yes. Retrieval runs over data you control, providers are selected for their data handling terms, and where needed the whole pipeline runs inside your own cloud. We document exactly where data flows before anything is built.
We build an evaluation set with you before launch and run it on every change, then trace real usage in production to catch the failures the test set did not predict. Quality is a number we can show you, not an impression.
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