AI that works inside the enterprise, not beside it.

A pilot proves that a model can answer a question. An enterprise solution adds identity, access control, integration with systems of record, evaluation, monitoring, cost management and a support model. Most of the engineering effort sits in that second part, which is why we design it from the start.

No. Retrieval-based architectures can index content where it lives, and models can be consumed through private endpoints on Azure, AWS or Google Cloud. What matters is a clear data boundary: what the model can read, what it can write and what is logged.

It depends on the task, the required accuracy, latency, cost and data residency. We usually design a model gateway so the choice can change over time without rewriting applications, and we evaluate candidates on your own test set before committing.

We classify data before it reaches a model, apply access-aware retrieval, minimize personal data in prompts, define retention for logs and document the legal basis for each use case. Governance is part of the architecture deliverable, not an afterthought.

When data sources and integration points are accessible, a first production release is usually a matter of weeks rather than months. The discovery phase defines the scope, the evaluation criteria and a realistic timeline for your environment.

Yes. That is where our integration background matters. We expose legacy capabilities through APIs, events or middleware so agents can act on them safely, without modifying the core system.