Microsoft data and AI
We build the Microsoft data foundation AI actually needs.
StratIQ works entirely in Azure, Fabric and Foundry. Software reads your systems and does the volume work, senior people own every decision that reaches production, and you see something working on your own data in the first week.
Where clients start
Start something new, or move what already works.
Engagements begin one of two ways. They end in the same place, which is a governed Microsoft platform you own, with capability being added to it every month.
Build new
A question the business cannot answer yet
You have a use case worth proving and nothing underneath it. We establish the Fabric and Foundry foundation around that one question, prove it on your data, then widen it across teams, domains and agents.
Modernize
A system that works and cannot continue
Reporting, logic and definitions locked inside Cognos, Tableau, a custom application or a spreadsheet estate. We recover what still matters and move it onto a platform you own, without waiting for the whole estate to be perfect.
Sequencing
Start with the data you have.
There is a familiar order to these proposals. Clean the data first, then remediate, then migrate, and then, eventually, do something useful with it.
That is three projects standing in a queue and the one the business actually asked for is last. Most organisations never reach it. The budget runs out, the sponsor moves on, or the next planning cycle reshuffles the list.
You do not need a finished data estate before you can see value. A working proof of value can be built on the data as it stands today, while the platform, the governance and the modernization path take shape around it.
The use case validates the foundation and the foundation gives the use case somewhere to go. Run them together and the sequencing problem stops existing.
The engagement
From one question to a platform that compounds.
Four stages, and they overlap. Platform work starts while the vision is still being sharpened, migration starts as soon as there is somewhere to land, and the managed service never really ends. Each stage is worth having on its own.
VisionIQ
Week one
Agree the business question, map the data and platform it touches, prioritise the backlog, and build a working proof of value on your own data.
Platform
Runs alongside
The governed Microsoft foundation. Fabric workspaces and OneLake structure, Foundry projects, semantic models, identity, security, deployment automation and cost controls, all inside your own tenant.
Migration
Only if you have something to move
Recovering and moving the data, reports, pipelines, procedures and business logic that still matter. Plenty of engagements never need this stage at all.
ManagedIQ
Continuous
Operating, governing and widening the platform as it gets used. New sources, new use cases, new agents, worked against a backlog you can see.
How the work gets done
Software does the volume. People own the decisions.
This is the part most firms leave vague, and the vagueness is exactly what makes people suspicious. Here is the actual division of labour.
Repeatable
Deterministic where it repeats
Scripts and templates stand up the parts of a Fabric and Foundry platform that should look the same every time. Governance, deployment, identity and environment promotion are not places for improvisation.
Variable
Agent-assisted where the volume is
Reading an estate, recovering business logic, generating pipelines and semantic models, writing tests and documentation. Work that used to mean months of someone reading now runs in days, repeatably.
Judgement
Human-owned where the risk is
Architecture, scale, security, cost, and anything that reaches production. A senior practitioner reviews the output, reworks it, and is accountable for it.
Yours to keep
The context stays with you
The prompts, models, procedures, documentation and decisions end up in your environment. The next use case starts from what the platform already knows rather than from nothing.
Evidence
See the method in action.
These are technical demonstrations built on synthetic data for fictional companies. The method, the analysis and the outputs are real. The client is not. We do not publish real client work, because it is not ours to publish.
Utilities
A regional utility leaves Cognos
Fourteen years of outage, load and asset reporting locked inside a Cognos estate nobody had upgraded since 2019. Read, mapped and rebuilt at parity.
Cognos · Oracle · Spreadsheet estate
Manufacturing
A manufacturer retires a quality application
A fifteen-year-old Java application that did tracking and trending for a regulated quality function. Reverse-engineered from source, data and outputs, then rebuilt as reports.
Custom application · SQL Server · Spreadsheet estate
The obvious questions
The questions technical leaders ask next.
If this is fast, what are you skipping?
Nothing that matters, and we will show you the receipts. The speed comes from changing how the work is done rather than from removing architecture, governance, testing or review. Repeatable platform work is scripted, high-volume engineering is agent-assisted, and a senior practitioner is accountable for anything that reaches production.
Does the platform live in our tenant, and what do we own?
Your tenant, and you own all of it. The Fabric workspaces, Foundry projects, pipelines, semantic models, procedures, prompts and documentation are deployed into your Microsoft environment. If you part ways with us you are not left holding an empty box.
Can you start before our data is clean?
Yes, and it is usually the better order. We start with the data and access available today. A working proof of value shows which definitions, sources and controls actually matter, and the platform and modernization backlog improve around a use case that has already been validated instead of around a guess.
Does AI end up inside our system?
Only if you ask for it. AI accelerates how we deliver, and none of that tooling ships to you by default. Grounded copilots and agents in your own estate are something we build deliberately, as their own decision with their own governance, not something that arrives as a side effect of the platform work.
How do you validate what the agents produce?
The same way you would validate a person's work, with more of it. Generated artifacts are tested against real outputs and intended behaviour, reviewed by a senior engineer for scale, performance and security, and recorded as verified, inferred or unconfirmed. The analysis harness is deterministic and re-runnable, so the same inputs give the same answers every time.
What if we are not on Fabric yet?
That is often the easier starting point. We stand up a new capacity built correctly from day one rather than inheriting someone else's decisions. If your organisation is committed to a different platform, the analysis work is the same either way, because the architecture matters more than the vendor.
What does this cost?
Less than you have been quoted, usually by a wide margin, but we are not going to put a number on a page. Engagements are fixed fee with a monthly managed service after. We will scope it honestly once we have seen what you have, and we will tell you if it is bigger than we thought.
Have you done this before?
The people here have spent their careers building Microsoft data and analytics practices, and have taken a great many organisations from one generation of technology to the next. What we do not have is a long list of clients who have been through this particular method, because the tooling that makes it possible is about a year old. Anyone claiming otherwise is stretching. What we have instead is a viewpoint, a plan, and work we can show you.
Start with one high-value problem.
A question the business keeps asking and cannot answer. A system that has been on next year's list for three years running. Either one is a place to begin.
We will tell you what we think before you spend anything.