AI agents & Copilot

An agent that answers from
your data, not from the internet

The difference between a chatbot and something useful is where the answer comes from. We build agents anchored to your documents, your systems and your rules — and that say «I don't know» when they don't.

Copilot StudioAzure OpenAIClaudeRAGMicrosoft 365 Copilot
What we do

Five concrete things,
not a list of good intentions

Pick a case that holds up

Not everything is solved with an agent. We look for the case with volume, a clear source of truth and tolerable consequences if it gets something wrong. Starting with invoicing or legal is how you lose trust in the first month.

Anchor it to your sources

RAG over your documents, your SharePoint, your database. The agent cites where each answer came from, so whoever asked can check instead of believe.

Make it act, not just talk

Check an order status, raise a ticket, book a slot, update a record. With the actions bounded and logged: what it can do is written down and auditable.

Limits and traceability

What it may answer, to whom, with what data, and what it does when it isn't sure. Every conversation is recorded, because the day it answers badly you will need to review it.

Where people already work

In Teams, in the portal, on WhatsApp or inside your own application. An agent on a separate tab you have to remember to open is not used by anyone by week three.

What we use

The full stack,
no window dressing

Everything below is in real projects. If something isn't on the list, we tell you before we start and not after.

Agent platforms

Microsoft Copilot StudioMicrosoft 365 CopilotCopilot en TeamsAzure AI FoundryAgentes autónomos

Models

Azure OpenAIGPT-4oClaude (Anthropic)Claude en AzureModelos de embeddingsModelos abiertos autoalojados

Knowledge and retrieval

RAGAzure AI SearchBases vectorialesFragmentación semánticaSharePoint como fuenteDataverse como fuenteCitas y trazabilidad

Integration and control

Model Context Protocol (MCP)Llamada a herramientasAcciones con Power AutomateConectores personalizadosMicrosoft PurviewRegistro de conversacionesEvaluación de respuestas
Frequently asked

What everyone ends up asking

That is the real risk, which is why it is anchored to your sources with RAG and required to cite. Even so no agent is infallible: that's why we pick cases where a mistake is caught and corrected, and why every conversation is logged. If someone promises you zero errors, be suspicious.

No. On Azure OpenAI and Copilot Studio the data you send is not used to train anyone's models and stays in your tenant. That is one of the reasons to build on that infrastructure and not on a free account of whatever tool.

If what you need is summarising email, drafting and searching your own documents, Microsoft 365 Copilot already does that and costs less than building. A custom agent earns its place when it has to query your systems, follow your rules or serve someone outside the company.

Two costs: model consumption, billed by usage and cappable, and periodic review of the answers, which is human work and doesn't go away. We give you both numbers before starting.

Yes, and that's where the value is — but also where the most care is needed: strict limits, escalation to a human when in doubt, and a clear notice that they are talking to an agent. We build it that way or we don't build it.

Shall we talk about your case?

Twenty minutes is enough to know whether there's a project. If there isn't, we'll say so.