Intelligence,
engineered.

Start with one repeated workflow and a clear definition of a good outcome. Bring examples of the inputs, the current process and the exceptions. We can assess whether rules, retrieval, an agent or a conventional application is the most appropriate approach.

Yes. We first inspect the existing code, APIs, data and operational constraints. We preserve useful functionality and propose staged changes where a full replacement would add unnecessary risk.

A chatbot focuses on conversation. An agent can also select and invoke approved tools to complete a task. Agents need explicit permissions, validation, visibility and human review where actions have consequences.

Often, no. Many applications are better served by a capable model, well-designed retrieval and reliable integrations. Training or fine-tuning is considered when suitable data and measurable evaluation justify it.

We agree what data can be used, which services may process it and who can access the result. The design can include scoped credentials, permission-aware retrieval, data minimization, retention rules and audit logs. The final controls depend on your requirements and selected providers.

The schedule depends on scope, integrations, data readiness and acceptance requirements. After discovery, we propose milestones and identify external dependencies. We do not promise a fixed timeline before understanding the work.

Yes. A review step can show the exact proposed action, destination and supporting evidence before execution. This is particularly useful for communications, record updates and other consequential changes.

Ownership, licensing and handover are agreed in the project contract. A handover can include source code, deployment instructions and operating documentation. Support and ongoing improvement are scoped separately so responsibilities are clear.