Custom Generative AI Services

Production-Grade GenAI for Regulated Microsoft Estates

i3Solutions delivers custom generative AI services for IT leaders at regulated, Microsoft-centric organizations who need GenAI that survives a security review, not just a demo. A business unit pilots a chatbot on SharePoint content, the demo impresses, and then the security review asks three questions the pilot cannot answer: where did that answer come from, did the model only see data this user is cleared for, and who signs off when it is wrong. Most generative AI projects stall at exactly that point. i3 builds the version that clears the review: grounded in your governed sources, scoped by your access model, and auditable from the first prompt.

 

Where Generative AI Earns Its Place

The teams that get value from generative AI are not the ones chasing the broadest model. They are the ones who narrowed the job: answer from approved sources, respect the permissions of the person asking, and log what was used so the output can be defended later. A model that produces new generative AI text, code, or structured data is only the engine. The retrieval, the access scoping, and the logging are what make it shippable in a regulated estate. Grounding it well also depends on a governed data layer, which is why teams pair it with Microsoft Fabric for the analytics and source data the model retrieves from.

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How do we build production-grade generative AI for a regulated enterprise?

Most regulated AI pilots stall at the security review, not the technology evaluation. Production-grade generative AI for a regulated enterprise is a system you can defend in an audit: ground every answer in approved content with retrieval, scope access to each user’s existing entitlements, log every prompt, source, and response, and keep a person on the approval before any action commits. i3Solutions delivers these builds with US-based senior Microsoft engineers, so the controls are designed for the audit and security review from the first sprint.

What “production-grade generative AI for a regulated enterprise” requires

Control area What it must do What it looks like in a Microsoft estate
Data governance Constrain the model to approved, current sources; no open-web generation Retrieval over a curated Azure AI Search index; content scoped by classification and recency
Grounding and RAG controls Make every answer traceable to a source document Citations returned with each response; answers refuse when no grounded source exists
Evaluation and guardrails Catch unsafe, off-policy, or hallucinated output before a user sees it Azure AI Content Safety filters, response evals against a fixed test set, blocked-topic and DLP rules on outputs
Audit Reconstruct who asked what, what was retrieved, and what the model returned Prompt, retrieved source IDs, model response, and requesting identity logged to Azure Monitor
Access control Return only what the requesting user is already entitled to see Retrieval filtered by Entra ID group membership; no privilege escalation through the assistant
Human oversight Keep accountability with a person for any consequential action Human approval step on write or send actions; phased rollout scoped to one site before scaling

Generative AI is easy to demo and hard to make defensible. In a regulated enterprise, the question is not whether a model can produce an answer but whether you can prove where the answer came from, that it used only data the user is allowed to see, and that a person remained accountable for it.

Production-grade generative AI is grounded in your governed sources, scoped by your access model, and traceable from prompt to source, rather than open-ended generation no one can account for.

Look for a partner who builds for the security review, not the demo. i3 builds production-grade generative AI for regulated Microsoft estates with senior, U.S.-based engineers, grounded in your data and governed from the first prompt.

Grounded Retrieval, Not Open-Ended Generation

The failure mode that ends most pilots is a confident answer with no source. We ground responses in your approved content using retrieval over Azure AI Search and Azure OpenAI, so every answer traces back to a document the user could have opened themselves. When the source does not cover the question, the system says so instead of inventing one.

Permissions That Match Your Tenant

A generative AI system is a data-access path, and reviewers treat it like one. We scope retrieval to what the requesting user is entitled to in Entra ID, so the assistant cannot surface a contract, a salary, or a CMMC-controlled document the person could not reach in SharePoint or Dataverse directly. Least-privilege retrieval is designed in, not bolted on after a leak.

Audit Logging You Can Hand to a Reviewer

An auditable generative AI system can show, after the fact, which sources fed an answer and which user asked. We log each prompt, the retrieved source documents and their IDs, the model response, and the requesting identity to Azure Monitor and Log Analytics, so a compliance or security team can reconstruct any interaction and export the trail for an assessor. Auditability comes from grounding and logging built in at design time, not a report generated afterward.

Custom Copilot and ChatGPT-Style Assistants

A custom ChatGPT-style assistant trained on your proprietary content answers in your context: your policies, your part numbers, your prior decisions. We build these on Azure OpenAI with your data kept inside your tenant, not sent to a public model. Where Microsoft Copilot already governs the data and meets the need, we configure that first.

Data-Loss Prevention on Outputs

Grounding controls what the model reads; DLP controls what it returns. We apply Microsoft Purview sensitivity labels and data-loss-prevention policies to generated output, and we test the leakage cases, prompt injection, over-broad retrieval, and confused-deputy access, before go-live rather than discovering them in production.

When the source content carries different sensitivity labels across departments, the access scoping and Purview policies have to be deliberate, or the system surfaces data the review never approved.

When to Build Custom Versus Configure Copilot

Start with what Microsoft Copilot already governs if it meets the requirement, because it inherits your tenant security and is easier to defend. Build custom in three cases: the source data lives outside what Copilot can index (a line-of-business database, a Dataverse table, an external API), the workflow needs a grounding or approval step the product does not expose, or retrieval has to honor record-level permissions Copilot cannot enforce. If none of those apply, configure Copilot and stop there.

Grounded Customer-Facing Assistants

A customer-facing assistant that answers from your published policies and product data, with retrieval scoped to what a given account is allowed to see, is defensible in a way an open chatbot is not. We build the same grounding and logging discipline into external assistants so a support answer can be traced to the document behind it.

Document and Workflow Automation

Drafting a first-pass response, summarizing a long case thread, or extracting fields from a PDF intake form into Dataverse are the tasks where grounded generation removes the most manual keying. We target one repetitive step, keep a person on the approval, and baseline the handling time before and after so the change is measured against the cycle it replaces rather than promised as blanket efficiency.

Senior, U.S.-Based Delivery

The hard part of generative AI in a regulated estate is the governed data and the accountability around the model, not the model itself. i3 builds with senior, U.S.-based engineers who design for grounding, access control, and audit, and as a Microsoft Partner since 1997 we hand back a system your security and compliance teams can defend.

Adoption With Guardrails Before You Scale

Rolling generative AI past a single pilot is a governance exercise, not a culture slogan, and we map those controls to recognized guidance such as the NIST AI Risk Management Framework. A common pattern: the first deployment is a knowledge assistant scoped to one SharePoint site, and the second tries to span three departments with different sensitivity labels.

That second step is where the access scoping and Purview DLP work done on the pilot has to be re-applied deliberately, because the wider the retrieval reaches, the more cross-label exposure a single misconfigured permission can create.

Organizations formalizing that shift work with i3’s senior Microsoft engineers to define approved use cases, set the human-review points, and carry the pilot’s grounding, permission scoping, and audit logging into each later deployment so those controls are reused rather than rebuilt as the footprint grows. Teams that want that handled inside their wider Microsoft estate can fold it into a broader Microsoft consulting engagement with our senior Microsoft team, or pair it with a Microsoft Fabric data layer when the assistant needs governed analytics to retrieve from.

 

Scope a Build Against Your Tenant

The fastest way to know whether you need a custom build or a well-configured Copilot is to put your actual tenant, data sources, and review requirements on the table.

A scoping conversation maps those constraints against a short checklist (where the data lives, which identities must be honored, which sensitivity labels apply, and what the assessor will ask for) and tells you, before you commit budget, whether configuring Copilot clears the bar or a custom build is warranted. You leave it with a written build-or-configure recommendation and the reasoning behind it, not a sales pitch.

Contact i3solutions to scope a governed GenAI build with our senior Microsoft engineers.


About the Author

By , Senior Consultant, i3solutions

Matt is a senior consultant at i3solutions who builds data and AI systems for regulated, Microsoft-centric enterprises. On generative AI work he focuses on grounding answers in approved sources, scoping retrieval to each user’s permissions in Entra ID, and logging prompts and sources to Azure Monitor, so a system can survive the security review instead of stalling at it.

Frequently Asked Questions

How do we build production-grade generative AI for a regulated enterprise?

Start with one scoped use case, not a platform. A knowledge assistant for a single approved SharePoint site is a defensible first deployment; a tenant-wide rollout is not. Ground the assistant in that approved source through retrieval, restrict what it can read to the user’s existing Entra ID permissions, and log every prompt, retrieved source, and response to an audit trail a reviewer can read. Add Azure AI Content Safety and data-loss-prevention checks on outputs, and keep a person on the approval for any action that writes or sends. Prove the controls on that one use case, then widen scope. The test is not whether the model can answer; it is whether you can prove where the answer came from.

What separates a production-grade regulated deployment from a pilot that stalls at the security review?

Pilots stall when grounding, permissions, and audit are added after the fact. A production-grade build treats them as the architecture. Retrieval is scoped to approved sources, access inherits the tenant’s existing Entra ID entitlements rather than a new permission model, and every interaction is logged in a form a reviewer can reconstruct. The security review then confirms controls that already exist instead of blocking a system that was built for the demo.