AI & Intelligent Systems
Run AI in production with tests, versions and a cost per call
Brihat Infotech builds the layer under a live AI system: the tests that block a bad change, the record of what runs where, the servers that scale, and a bill you can read.
What gets set up
- Prompt and model versioning
each change leaves a record and can be rolled back.
- Evaluation gates
a scored test runs before each release, and a change that lowers the score does not ship.
- Routing and caching
easy questions go to a cheaper model, and a cache answers repeated ones.
- Serving and scaling
from serverless endpoints to GPU machines, sized to your traffic.
- Drift monitoring
an alert when live inputs stop looking like the data the model learned from.
- Cost governance
spend by team and by use case, so a runaway job shows up in the report.
- Reproducible pipelines
any model can be rebuilt from its recorded data and code.
Who needs it
You need this layer when people depend on a pilot, when two teams call the same model and nobody owns the bill, or when prompts are edited by hand in production. A first prototype with a handful of users can wait.
What goes in the registry
The registry holds every prompt, model and settings file that a live system uses, each with a version number, an owner and the date it went live. When an answer looks wrong on Tuesday, you can find which version produced it and compare it with Monday's. Rolling back means pointing the system at the earlier version.
Routing rules sit in the same registry. A rule such as "send contract questions to the larger model, FAQ questions to the smaller one" can be changed and tested without touching application code.
An example gate
Suppose someone edits a prompt to make answers shorter. The change goes through the pipeline, which runs your scored test set. If answers to the refund questions get worse, the release stops and the author sees which cases failed. If scores hold, the change ships and the registry records who made it and when.
What your team gets
Dashboards for cost, latency and drift, runbooks for the common failures, and a walkthrough with the people who will be on call. The registry, pipelines and monitoring live in your own cloud account, so you can run them without us.
How a build runs
Set-up falls to an ML platform engineer and a site reliability engineer, on your cloud or in your data centre. The tools are Kubernetes with GPU nodes, vLLM for serving open-source models, a registry for prompts and models, and OpenTelemetry for traces. Your team gets runbooks and a walkthrough. How we engage covers the contract options, including a monthly run arrangement.
If the systems themselves are not built yet, start with custom AI and LLM development.
Tell us which model calls your teams make today, and who pays for them.
Tell us what you want to build.
The first call is 45 minutes with an architect, usually the founder. It is free, and we will sign an NDA first if you ask.