Production AI systems, not demos.

Yes. We design for the boundary you need: private retrieval infrastructure, controlled integrations, least privilege access, and self-hosted models where data may not reach a third party.

We combine grounded retrieval, constrained tool permissions, output validation, adversarial testing and monitoring. No single technique is treated as a complete guardrail.

Through an evaluation set built before the feature ships and run on every change. Without one you are relying on whoever tried it most recently, which is why teams end up unable to say whether last month's prompt edit helped.

Inference cost follows usage, so the variable is how much context each request carries and how often retrieval runs. We model that during design, because the cheapest system to build is often not the cheapest to operate.

A design in which the model answers from documents retrieved for each question, rather than from what it learned in training. It keeps answers grounded in your current content, lets them cite their sources, and means the model never has to be trained on your data.

It depends on the data, the volume and the quality required. Commercial APIs are usually the strongest and the quickest to start with; open-weight models on your own infrastructure give more control over data and cost at steady volume. We test the candidates on your evaluation set before choosing.

An open protocol for connecting AI assistants to tools and data sources through a standard interface. We use it where it fits, because one set of scoped, logged tools can then serve several assistants, and the permissions of each tool are explicit.

Each tool an agent can call is scoped to the narrowest permission it needs, actions that change data or spend money require confirmation, and every call is logged. Instructions that arrive inside retrieved content are treated as data, never as commands, which is the core of prompt injection defense.