01
Enterprise architecture leads
Accountable for whether the AI estate is coherent, and short of a Foundry specialist to check it against.
Fractional AI Architect / Microsoft
Foundry makes it easy to stand something up and hard to know whether it is ready. The architect works next to your engineer on the parts that decide that: grounding, identity, evaluation, quota and cost.
Microsoft AI Foundry
Architecture and design review
14 hrsPairing with your engineer
12 hrsEvaluation and release gates
8 hrsDocumentation and handover
6 hrsThe work is judgement rather than volume. Which model and deployment topology in Foundry, how to ground on SharePoint and Fabric without leaking permissions, how identity flows through Entra ID, what an evaluation pipeline has to prove before release, and where Copilot Studio is the right answer instead of a custom build. These are decisions that are cheap to make correctly at the start and expensive to unpick later, which is why they justify a senior person part time rather than a junior one full time.
Who it is for
Teams already committed to the Microsoft stack who have one capable engineer and no one to check their architecture.
Accountable for whether the AI estate is coherent, and short of a Foundry specialist to check it against.
Building on Azure already, and hitting decisions about grounding, permissions and evaluation for the first time.
Under pressure to ship AI on Microsoft while proving to risk that permissions and data boundaries hold.
Coverage
The parts of the Microsoft estate that decide whether an AI workload is production-grade, rather than the full surface of Azure.
Project and hub topology, model catalogue choices, deployment targets, and the evaluation tooling that ships with it.
Model and version selection, quota and throughput planning, content filtering, and the cost profile of each choice.
How analytical data reaches an AI workload, what is materialised for grounding, and where lineage has to hold.
Grounding on documents while honouring the permissions those documents already carry, which is where most Microsoft AI projects quietly fail.
Identity through to the model call, and the labelling and boundaries your compliance function is going to ask about.
Where the output actually lands, and the integration patterns that survive a tenant policy change.
Straight answer
The client requirement that prompted this offer asked for a clear point of view on capabilities and limitations. Here is ours.
Outcomes
Written down, reviewed, and specific enough that your engineer can build against it without guessing.
A real Foundry workload with grounding, identity and evaluation your risk function has seen.
Measured deliberately, because the point of the engagement is to become unnecessary.
Working with Indian enterprises
Most of our engagements run with banks, NBFCs, insurers and manufacturers headquartered in India. The architect works IST, joins your existing rituals, and is used to the approval chain an Indian enterprise actually has.
Architecture reviews, vendor selection and security sign-off go faster face to face. The architect travels to your offices across the metros and tier 2 cities as the engagement needs it.
Data residency, consent and purpose limitation under the DPDP Act 2023 shape the architecture from the first session, alongside RBI, IRDAI and SEBI expectations where they apply.
Design decisions, model choices and control gaps are written down as you go, in a form audit and risk can read without a translation layer.
Where a workload cannot leave the country, the architect can design against open-weight models running entirely on Indian infrastructure through Gyde Inference.
Delivered onsite in
Free download
A sample engagement plan for a 40 hour month: what the architect does in week one, what your engineer owns by week four, and the artefacts that exist at the end of it.
Questions
If your question is here in a form we have not covered, ask us directly and we will answer it plainly.
Microsoft AI Foundry is Azure's platform for building, evaluating and deploying AI applications, bringing together a model catalogue, deployment and hosting, evaluation tooling and agent capabilities in one place. It is where most enterprises already committed to Azure will build, because identity, data and procurement are already there.
Forty hours a month is the standard shape, which is roughly ten hours a week. That is enough for a weekly design review, real pairing time with your engineer, and the documentation that makes the work survive. Engagements can run heavier for the first month while the target architecture is set.
Yes, in the specific sense that they pair with your engineer and write reference implementations for the parts that set a pattern. They do not take delivery tickets. If you need someone to build the whole thing, our AI delivery POD is the right engagement instead.
Because most enterprises do not have 160 hours a month of genuine architecture work, and a senior Foundry specialist is hard to hire and harder to keep busy. Forty hours a month buys the judgement without the idle time, and it starts in days rather than the three to six months a hire takes.
A staffing agency sells you hours at a level you specify. This engagement sells you an architect who is accountable for whether the design is right, works to make your own engineer more capable, and is deliberately trying to reduce how much you need them. The measure of success is your team, not our utilisation.
Yes, and part of the value is an honest read on where Copilot Studio is sufficient and where a custom Foundry build is warranted. Teams often over-build one and under-use the other. Our Copilot Studio overview covers the platform itself in more depth.
Yes, and it is most of what we do. The architect works IST, travels to your offices across Bengaluru, Mumbai, Delhi NCR, Pune, Hyderabad and Chennai, and designs against DPDP Act 2023 obligations alongside RBI, IRDAI or SEBI expectations where they apply.
You keep the target architecture, the decision records, the reference implementations and an engineer who has been building against them for months. Engagements usually taper rather than stop, moving to a lighter monthly review once your team is running on its own.
Keep going
Start the conversation
Tell us the platform and the workload that is stuck, and we will propose a scope for the first 40 hour month.