01
Scoping and use-case selection
Workshops with business teams to identify high-value use cases, estimate expected gains and running costs, and rule out those that will not survive production.
AI Consulting · FinOps
Generative AI consulting: useful agents, a bill under control
We help companies move from experimentation to AI assistants in production: connected to their data, secure, and with costs measured from day one.
Generative AI is no longer a demo. Models are available, tooling has matured, and most leadership teams have launched a first pilot. The hard part lies elsewhere: picking the use cases that truly pay off, connecting AI to reliable data, and preventing a successful rollout from turning into an unpredictable bill.
Bitwin supports this stage with an architect’s approach. We start from your data, often already in Microsoft Fabric, Power BI or a data warehouse. We design agents that query it through MCP servers, then deploy them on Azure AI Foundry or the platform that suits you. At every step, we estimate what a question, a user and a month of operation will cost.
Our conviction: an AI project is judged in production, not in a demo. It must give accurate answers, respect existing access rights and stay profitable when usage takes off.
Our AI services, from scoping to production
Four stages, delivered together or separately depending on your maturity.
02
Architecture and data
Choice of building blocks (Azure AI Foundry, Copilot Studio, Azure AI Search, MCP servers), data modelling in Microsoft Fabric and Power BI, integration with your information system.
03
Prototype on your data
A working agent on a limited scope, tested by real users, with measured answer quality and actual cost per request.
04
Industrialisation and governance
Security, access rights, logging, monitoring, GDPR and EU AI Act compliance, team training: everything that turns a prototype into a lasting service.
AI FinOps: controlling the cost of every answer
An AI agent consumes resources with every question: model tokens, search, compute capacity for data. Without steering, the bill follows adoption. Drawing on our cloud FinOps experience at Oracle, we apply the same discipline to AI.
Understand what you pay for
- → Tokens consumed by models, input and output
- → Microsoft Fabric and Power BI capacity used by queries
- → Search index, application hosting, storage and monitoring
Pick the right model for each task
- → A small model to classify or route a request, a large one only when it adds value
- → Pay-as-you-go or provisioned throughput, depending on volume and regularity
- → Deferred batch processing when an immediate answer is not needed
Reduce consumption at the source
- → MCP tools that return aggregated results rather than whole tables
- → Short instructions and context, caching of repeated parts
- → Quotas per user or per application
Measure and steer over time
- → A tracked unit cost: per question, per user, per use case
- → Resource tagging, Azure budgets and alerts
- → Sizing, pausing and reserving Fabric capacities based on actual usage
A proven architecture: Web App, MCP, Power BI, Fabric and AI Foundry
For Alliance Marine, we deployed a Web App connected to an Azure AI Foundry agent and to Power BI. An MCP server makes it possible to query data hosted in Microsoft Fabric in natural language.
This design has become our reference architecture for giving teams conversational access to their data, without duplicating that data and without bypassing existing security rules.
Why entrust your AI project to Bitwin
Data and AI, from the same architect
AI is only as good as its data. We design the data platform (Microsoft Fabric, Power BI) and the AI layer together, not separately.
FinOps in our DNA
Years of driving cloud adoption and cost optimisation at Oracle: we know how to grow usage without losing control of the bill.
Technology independence
No exclusive vendor agreements. We recommend Copilot, a custom agent or another platform based on your needs, not on a sales target.
Frequently asked questions about AI consulting
How much does an enterprise generative AI project cost?
There are two budgets: the project (scoping, development, deployment) and operation (models, data capacity, hosting). The second is often underestimated. We estimate it before starting, based on the expected number of users and questions, then measure it during the prototype.
What is FinOps applied to AI?
It is the discipline of making AI service costs visible, allocating them and optimising them: choosing the model per task, reducing tokens consumed, quotas, tracking a cost per question or per user, and sizing the data capacities involved.
Copilot, Copilot Studio or a custom agent: which to choose?
Microsoft 365 Copilot covers office productivity. Copilot Studio lets you build agents quickly with low-code. A custom agent in Azure AI Foundry is the right fit when AI must be embedded in a business application, query complex data or meet strict cost and security constraints. We help you choose case by case.
What is an MCP server?
The Model Context Protocol (MCP) is an open standard that lets an AI agent use tools: query a database, run a query on a Power BI model, read a document. The MCP server exposes these tools in a controlled way and can be reused by several agents and platforms.
Is our data used to train the models?
For Azure OpenAI models offered in Azure AI Foundry, Microsoft states that prompts and completions are not used to train the models. We review with you the terms of the chosen model, the hosting region and the data retention period.
Ready to move from experimentation to production?
Let’s talk about your use cases, your data and the running budget you are aiming for.
Book a call