Flagship · Agentic AI
AI Agents & Copilots
Custom AI agents that automate real workflows across Microsoft 365, Dynamics 365 and Salesforce, built on Copilot Studio, Azure AI and Agentforce.
The short version
What this actually involves
An AI agent is not a chatbot with a nicer interface. It is software that takes a goal, works out the steps, calls your systems to get them done, and hands back to a person when it should. The difference matters, because the second kind changes how work gets done and the first mostly moves it around.
The hard part is almost never the model. It is grounding the agent in your own data, trimming what it can see to what the person asking is allowed to see, deciding where a human signs off, and proving it behaves before it touches a customer. That engineering is what separates the agents that reach production from the large majority that stall in pilot.
We build on Microsoft Copilot Studio, Azure AI Foundry and Salesforce Agentforce, and we pick between them on your constraints rather than our habits. Sometimes the honest answer is that a scripted automation would serve you better, and we will say so.
Capabilities
What’s included
- Copilot Studio & Microsoft 365 agents
- Salesforce Agentforce agents
- Azure AI Foundry / Azure OpenAI copilots
- Retrieval over your SharePoint & Dynamics data
Deliverables
What you get
- A working agent in your own tenancy, not a demo environment
- Grounding over your SharePoint, Dataverse or Salesforce data, permission-trimmed
- Tool and action definitions wired to your existing services
- An evaluation set so you can tell an improvement from a regression
- Guardrails, escalation rules and full conversation logging
- Handover documentation and the source, which you own
How we work
From first call to something you can use
Week 1
Use case and grounding audit
We pick the workflow with the highest volume and the clearest source of truth, then audit whether the content behind it is good enough to ground an agent. This is where most projects are quietly decided.
Week 2–3
Build the narrow version
One job, done properly. Topics, actions, retrieval and guardrails, running against your real data with a human in the loop on anything that writes.
Week 4
Evaluate and tune
We assemble real inputs with expected outputs and measure. Prompts and retrieval get tuned against evidence rather than impressions.
Week 5–6
Pilot with real users
A limited group uses it while we read every transcript. The first fortnight of real conversations teaches more than a month of planning.
Ongoing
Widen the remit
Once the narrow version is boring and reliable, we add the next job. Boring is the goal.
Explore AI Agents & Copilots
The work, broken down
Copilot Studio Agents
Custom agents and copilots across Microsoft 365, grounded in your Graph and Dataverse data.
Learn more →Salesforce Agentforce
Autonomous service and sales agents built on Salesforce Agentforce and the Einstein platform.
Learn more →Azure OpenAI Solutions
Enterprise copilots on Azure AI Foundry and Azure OpenAI, with your guardrails and governance.
Learn more →RAG & Knowledge Assistants
Retrieval-augmented assistants over your SharePoint, Dynamics and document stores, permission-aware.
Learn more →Typical stack
- Copilot Studio
- Azure AI Foundry
- Azure OpenAI
- Semantic Kernel
- Agentforce
- Azure AI Search
- Dataverse
- Microsoft Graph
- .NET / C#
Questions
What clients ask before starting
How long before we see something working?
A narrow agent against real data usually runs in two to three weeks. A full production rollout with evaluation, pilot and governance is more like six to eight. We deliberately ship something you can click early, because that is what surfaces the requirements nobody wrote down.
Will it invent answers?
It will if you let it answer from the model rather than from your content. We ground agents in your own data, make them cite sources, and give them an explicit path for saying they do not know. That, plus review-and-sign on anything that writes, is what keeps it defensible.
Can it see data a user should not?
No. Retrieval is permission-trimmed at query time, so the agent can only surface what that person could already open. This is the first thing we design and the first thing we test.
Copilot Studio or a custom build on Azure OpenAI?
Copilot Studio when the pattern is common, your data sits in Microsoft 365 and you want business users involved in maintaining it. Azure OpenAI when you need control over retrieval, orchestration or the user experience. We are happy to recommend the cheaper option.
What does it cost to run?
Token cost is usually smaller than people fear and easy to model once we know the volume and the retrieval pattern. We instrument cost per feature from day one so the number is never a surprise.
Talk it through with an engineer
Thirty minutes, no obligation, and an honest answer about whether this is the right service for what you are trying to do.