AI is only as good as the data layer and governance beneath it.
We teach Claude and Copilot to give the right answers. Securely. Quickly. With minimal tokens.
Six areas that hold AI in your company together
We start with scenarios, data and rules, because that is what AI stands or falls on. Each area solves a different piece of the puzzle: from data and rules through agents and automation to costs and choosing the right scenarios.
AI-ready DWH in Fabric
Data that AI understands
Documented data, one version of the truth, and permissions that apply to AI too. We review and prepare identity, row-level security and metadata, and build a semantic model with descriptions, synonyms and a test set of questions.
Agents for CEO, finance, sales, production and more
One agent, one domain
Specialized agents on top of your data, trained on the terms and rules of a specific department. Leadership asks once and gets an answer with a link to the source.
AI automation
Processes that watch themselves
Daily company status summaries, alerts on deviations from plan, sorting of documents and complaints. AI as a step in a process or a data pipeline, with the result right in the table.
AI security and governance
AI that sees only what it should
The user’s identity travels from the question down to the data row. Query auditing, agent monitoring and clear rules on who can create an agent. A framework for leadership and tenant configuration.
AI cost optimization
Value calculated up front
Capacity, tokens and licenses calculated before deployment. A budget, overspend alerts and quarterly reviews keep AI paying for itself.
Business analysis
The right scenario before the first agent
Before anything gets built, we verify where AI will actually help your company. We go through use cases, assess solution fit (Copilot, Data Agent, Foundry, or no AI for now) and pick the scenarios where the value outweighs the cost. The result is a ranked list of scenarios, each with an owner, a metric and an estimated benefit.
Four AI scenarios that make business sense
AI in a company takes four forms. They differ in who uses it and whether it helps a person with their work or handles a step in a process on its own. Each needs different tools, different licenses and a different rollout, so we start by working out which quadrant your scenario belongs to.
Answers without waiting for a report
- Ad hoc reporting on all the data the user has access to.
- Consistent KPI definitions, even when you ask AI.
- Presentations and documents built from real numbers.
- AI that understands finance, sales, production, HR and IT.
Faster data platform builds
Models, queries and pipelines with AI at hand, within rules that keep production safe.
- DAX, SQL, PySpark and model descriptions with Copilot
- agentic development via Fabric CLI, API and MCP
- code review, no production data in prompts
Processes that run themselves
- Daily status summary for the company or a department.
- Alerts on exceeded limits and deviations from plan.
- Mailbox and task triage, meeting prep.
- Agents prepare the outputs, you focus on the work.
AI as a step in the data pipeline
Batch classification, extraction and prediction across thousands of rows. The result is an ordinary column in a table.
- sorting texts, contracts and complaints with AI functions
- predictions from your own ML model straight into the Lakehouse
- MCP server: agents read data with the user’s identity
How AI works on business data
It doesn’t matter who asks, what they ask or which tool they use. Every query travels in a governed, secure and correct way through a managed, protected zone to your company data. And to exactly the data it should reach.
One definition of truth
Revenue, margin and plan are calculated in the semantic model the same way for reports and for AI. The assistant doesn’t do its own math.
Why the numbers moved
Emails, meeting notes and SharePoint documents add the reason, the decision and the name of the person who knows.
AI sees only what you see
Access runs through your identity and your tenant, with no shared service accounts. Every query is logged.
Why Copilot and Claude on their own get you nowhere
An AI project stands on three pillars and needs all of them at once. Meaningful scenarios define which questions, areas and data you want to ask about. AI-ready company data gives AI material it can be trusted with. Model training and agents prepare AI for your users’ questions and commands. When one pillar is missing, the project doesn’t fail right away. It fails later, and at a higher cost.
I know which questions, areas and data I want to ask about.
Documented data, one version of the truth, and permissions that apply to AI too.
AI prepared and optimized for user questions and commands. Instructions, skills, tools, MCP, artifacts and agents for specific processes.
When one pillar is missing, the project fails later and costs more.
Usable, expensive and inconsistent
Scenarios and data are in place, but AI has no instructions, skills, tools or agent for the process. It answers generically, doesn’t know company terms, and every query burns too many tokens. Each user writes their own prompts, so the same question gets a different answer every time.
AI that will lie to you
The scenario is clear and the agent is built, but the data isn’t documented, there are several versions of the truth and permissions don’t fit. The demo works, production brings wrong numbers, and one wrong answer costs trust in the whole project. The worse case: the agent bypasses permissions and shows what it shouldn’t.
An expensive toy
The platform is ready and the agents are built, but nobody defined what AI should improve or how to measure it. The pilot never ends, capacity and licenses keep costing money, and nobody can quantify the value. It’s the first thing cut in the next budget.
What AI you can trust looks like
When AI, scenarios and data meet the right way, they create three qualities that tell you AI can be trusted. AI connected to data is secure and fast. Data under scenarios is governed and controlled. Scenarios for AI make financial sense too.
Secure and fast AI + data
AI reads only the data the person asking is entitled to. Identity travels from the question down to the data row, RLS and sensitivity labels apply, and every query is logged. The query takes the shortest path to prepared data, so the answer arrives quickly and with minimal tokens.
Governed and controlled scenarios + data
Every scenario has a data owner and a process owner, rules on what may be automated, and a place in the agent registry. When the data changes, you know which scenarios are affected.
Meaningful and cost-effective AI + scenarios
We deploy AI where the value outweighs the cost. Every pilot has a metric, a budget cap and, at the end, three numbers: value, cost to complete and cost to run. Sometimes the right answer is no.
Technologies we work with
Not sure where to start?
In one day we go through three of your processes, and you leave with a proposal for one PoC: goal, metric and budget cap.
- 1day
- 3of your processes
- 1PoC proposal