Home·Use cases

Five problems you'll recognise. And what production looks like.

Most of these aren't new ideas. What's new is being able to put them into production inside your own Azure tenant, on your own data, with the governance your security team needs, without a year of engineering work.

Use case 02 · Internal IT & service desk
For: IT Director · Head of Service · Head of Operations

"The service desk is buried under questions that already have answers."

Eighty per cent of the tickets coming in are repeat questions: password reset, VPN setup, expenses policy, holiday rules, how to share a file with a client. The answers exist in the knowledge base. Nobody reads the knowledge base.

The service desk is stretched. The options are to hire more agents, write more articles nobody reads, or buy a chatbot that won't know your actual policies, will hallucinate, and will be quietly switched off within a quarter.

What's been tried: two vendor chatbots, both rejected because they couldn't ground in the live policy documents; a Copilot Studio pilot, blocked because it leaked across teams; a knowledge-base refresh that nobody used.
Answer

A first-line answers assistant, grounded in your current policies.

A self-service assistant deployed across the workforce, grounded in your IT and HR knowledge bases, returning policy answers with citations. When the answer isn't safe to give as self-service, it triages to the right team and pre-populates a ticket with the right context.

The assistant updates when the policy updates. No chatbot retraining cycle. If the expenses policy changes on Monday, the assistant answers Monday's questions with Monday's policy, not last quarter's.

What changes
  • ·Repeat tickets fall by 40 to 60% in the categories the assistant covers
  • ·Service desk time redirects to complex tickets where a human is genuinely needed
  • ·Audit trail per query: who asked what, what was returned, what the user did next
  • ·No vendor lock-in to a chatbot platform you might want to retire
Use case 03 · Finance & compliance
For: Director of Finance · Head of Compliance · CFO

"Compliance review takes weeks. The auditor wants days."

Every contract, every supplier agreement, every change to a standard policy has to be reviewed for compliance against your sector's rulebook. The compliance team is small. The volume is high. Review cycle stretches from days to weeks, and the rest of the business waits.

The standard AI offers don't help: they don't know your specific regulatory framework or your firm's interpretive history; a vendor product wants to take the documents off-tenant for processing.

What's been tried: a vendor "AI compliance review" tool (failed risk assessment because it processed documents in their cloud); ChatGPT Enterprise (made it worse, because answers sounded confident but weren't grounded in your own ruleset); a contractor reviewing the backlog at £900 a day.
Answer

A first-pass compliance review assistant, grounded in your rulebook.

A compliance-review assistant scoped to your finance and compliance teams, grounded in your firm's interpretive guidance and the relevant regulatory documents, that produces a first-pass review of a submitted contract or policy: which clauses match precedent, which deviate, which need human attention, with citations to the source rules.

Nothing leaves your tenant. The compliance team retains the sign-off; the assistant compresses the time to "ready for human review" from days to hours.

What changes
  • ·First-pass review time drops from days to hours per document
  • ·Compliance team focuses on the deviations, not the routine matches
  • ·Backlog clears without hiring or contractor spend
  • ·Every review is auditable and consistent with the firm's interpretive history
Use case 04 · HR & people operations
For: Head of HR · Director of People · HR Operations

"HR answers the same questions every week."

Maternity policy. Sickness reporting. Probation extensions. Performance management procedure. The same fifty questions arrive in HR's inbox every single week, always taking a real person fifteen minutes to find, draft, and reply to. HR loses two days a week to questions that are already answered in writing.

Generic AI tools don't help: they don't know your actual handbook or the version that applies after last quarter's revision. They confidently hallucinate policy that doesn't exist, which is worse than no answer.

What's been tried: a vendor HR chatbot, switched off after answering one query with policy from a different organisation entirely; an internal SharePoint search project; a "just read the handbook" all-staff email that nobody read.
Answer

An HR self-service assistant, grounded in your handbook.

A people-operations assistant scoped to all employees, grounded in your current employee handbook, policy documents, and process guides, that answers policy questions with citations to the live document. When the question is sensitive it routes to a human contact rather than answering.

Versioning matters here. When you update the handbook, the assistant answers tomorrow's questions with tomorrow's policy. No retraining, no two-version-of-the-truth confusion.

What changes
  • ·HR inbox volume on routine queries drops sharply, often by half
  • ·Employees get answers in seconds, at the moment they need them
  • ·Sensitive matters still reach a human, by design
  • ·Every answer is auditable and consistent with the live handbook
Use case 05 · Sales enablement
For: Sales Director · Head of Bid · Commercial Operations

"Every proposal is written from scratch by someone too senior to be doing it."

The proposal arrives, the response is due in ten days, and the bid team starts hunting. Past wins. Pricing rules. Standard scope language. All of it lives in fifteen different places, half of it lives in someone's email.

A senior salesperson ends up writing the first draft over three days, drawing on a decade of context they hold in their head. The result is good, but the cost is enormous and unsustainable. Win rate is acceptable; volume is the bottleneck.

What's been tried: a proposal generation vendor that wanted the win library in their cloud (rejected); ChatGPT Enterprise drafts that read generically and don't know the firm (rewritten end-to-end every time); a "central bid library" SharePoint initiative that everyone agreed was a good idea and nobody updates.
Answer

A proposal-authoring assistant grounded in your win library and pricing rules.

A sales-enablement assistant scoped to the commercial team, grounded in your past proposals, win/loss notes, scope-of-work libraries, pricing rules, and technical specifications. Given a fresh RFP, it produces a structured first draft in your firm's voice.

The senior salesperson edits rather than authors. Their judgement still shapes the proposal, but the legwork compresses from three days to an afternoon. The win library updates from the proposals that come out of the assistant, so each cycle improves the next.

What changes
  • ·Proposal turnaround drops from days to hours per response
  • ·Senior commercial time redirects to strategy and customer dialogue, not drafting
  • ·Win library stays current because it's part of the working loop
  • ·Customer data and your commercial IP never leave your tenant

The same conditions, every time. That's what the platform delivers.

01

Grounded in your own material. Not generic answers: answers based on your documents, your policy, your matter archive, your rules.

02

Inside your tenant. Your data never leaves your Azure subscription. No vendor processing in their cloud.

03

Scoped to the right people. Identity from your Entra ID. Team A's assistants don't leak into Team B's view.

04

Auditable to the request level. Every query, every answer, every reaction, recorded in Azure Log Analytics inside your tenant.

05

Operable by your existing team. No specialist AI engineering hire. Your build team uses the authoring environment; the platform handles the rest.

06

Reversible. Kill switch per assistant. Version rollback. The same loop that ships fast is the loop that rolls back fast.

Recognised one of these? It's a short conversation.

Tell us which use case you've been trying to solve, what you've tried, and where it stalled. If we can help, we'll be specific about how; if we can't, we'll be honest about that too.

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