AI consulting & implementation
We build the AI that makes it to production.
PHE Consulting finds the workflow worth automating, proves the business case, and builds the system that runs it — securely, and in a way your team can own.
- Microsoft specialists
- A deliberate focus on one platform, so engagements move quickly from scoping to delivery.
- We build, not just advise
- Every engagement ends with something deployed and running, not a recommendations deck.
- Fixed-scope first step
- A priced, time-boxed assessment. You see the number and the plan before committing to a build.
Talk to a Consultant
Tell us what you want AI to do for the business. We'll tell you where it can move a real number — and where it can't. No technical prep needed.
Where our expertise runs deepest.
We maintain specialist capability across the Microsoft platform, from initial implementation through to ongoing development.
Azure
Compute, networking, PaaS
Azure AI Foundry
Models, agents, evaluation
Microsoft 365 Copilot
Declarative agents, connectors
Microsoft Fabric
Analytics & OneLake
Microsoft Entra
Identity & Conditional Access
Microsoft Defender
Cloud & workload security
Azure DevOps & GitHub
Pipelines and IaC
Power Platform
Low-code process automation
The gap
Using AI is not the same as getting value from it.
A few licences, some chat tools and an enthusiastic team aren't maturity. The question isn't who's using AI. It's where AI has actually changed how the work gets done.
- 1
AI access
Licences bought, tools switched on.
- 2
AI activity
Chat tools and assistants in daily use, informally.
Most teams stall here - 3
Chosen use cases
The workflows worth funding are identified and baselined.
- 4
Agentic workflows
Agents run inside real processes, with human approval.
- 5
Measured impact
Cost, speed, quality and risk move — and it's tracked.
Where the return shows up
The questions that matter
- Which workflow should change first?
- Who owns the number it's meant to move?
- What does that process cost us today?
- How do we avoid another science project?
Where AI pays
Five places the return actually shows up.
Every workflow worth automating moves at least one of these. If we can't name which one — and what it's worth — we'll tell you not to build it.
Revenue
Faster qualification and follow-up, fewer leads going cold, better conversion.
Cost & margin
Less manual review and rekeying, fewer repeats, lower cost per unit of work.
Speed
Shorter cycle times, faster approvals, reporting that doesn't wait on a person.
Quality
More consistent decisions, fewer errors escaping into downstream processes.
Risk & compliance
Exceptions caught earlier, with a clear audit trail of what happened and why.
Demo vs. production
The demo is the easy part.
Anyone can stand up an impressive agent in a fortnight. The reason so many never reach production isn't the model — it's everything underneath it.
A model, a chat window, a prototype flow and some sample data. The part you've already been shown.
We build the other 95%. It runs in your own cloud tenant, under your identities and your network boundaries — whether that estate already exists or we stand it up as part of the work.
The production system · 95%
Business context
So the system understands how your organisation actually works.
Source-of-truth data
So answers are grounded in real records, not plausible guesses.
Role-based access
So people only see and do what their role permits.
Rules & exceptions
So policy and the awkward edge cases are respected.
Approval paths
So anything consequential waits for a human.
Write-backs
So results land in the systems the work already runs on.
Logs & audit trails
So you can show what happened, and why, months later.
Monitoring & cost
So errors, latency, usage and token spend stay visible.
Adoption & ownership
So the workflow is used, and owned, after we leave.
Measured impact
So the change to the business is tracked, not assumed.
Services
AI on top. Azure underneath.
Two practices, one delivery team. The AI work is what you'll notice; the platform work is what makes it survive contact with production.
Artificial intelligence
AI readiness & roadmap
A short assessment of your data, security posture and candidate use cases, ending in a ranked roadmap with honest cost and effort estimates.
Custom AI agents
Agents that plan, call your systems and complete real tasks — built on Azure AI Foundry, Semantic Kernel and the Microsoft Agent Framework.
Copilot extensibility
Extend Microsoft 365 Copilot with declarative agents, Graph connectors and plugins so it answers from your systems, not just your documents.
Evaluation & governance
Test sets, groundedness scoring, content safety, prompt versioning and cost telemetry — the unglamorous work that keeps AI safe to run in production.
Microsoft Azure
Azure landing zones
Enterprise-scale foundations built to the Microsoft Cloud Adoption Framework — management groups, subscription design, hub-and-spoke networking and Azure Policy guardrails set up once, properly.
Migration & modernisation
Azure Migrate assessment through to cutover. We rehost what should simply move, and re-platform to App Service, AKS or Azure SQL only where the business case holds up.
Platform automation
Reusable Bicep and Terraform modules deployed through GitHub Actions or Azure Pipelines. Every environment reproducible, every change reviewed, no hand-built servers.
Security & identity
Microsoft Entra ID, Conditional Access, Defender for Cloud and Sentinel configured to a zero-trust model — with the reporting your auditors and insurers ask for.
FinOps & cost control
We find the spend that isn't earning its keep: idle resources, wrong SKUs, missed reservations and savings plans. Then we put tagging and showback in place so it stays fixed.
Data platform
Microsoft Fabric, OneLake, Azure Databricks and Azure SQL pipelines that consolidate scattered data into something your reporting — and your AI — can actually rely on.
How we work
From scattered AI to one workflow worth funding.
Start with a number worth changing, then build the system that changes it. You can stop after any stage and still be better off than when you started.
01 · Find the value
Opportunity map
You get
Ranked opportunities and a recommended first workflow.
02 · Build the context
Data & guardrails
You get
The data, rules, access model and a way to measure the result.
03 · Ship it
Agentic workflow
You get
A live workflow with human review, running against real systems.
04 · Measure & expand
Continuous AI ops
You get
Reporting on what changed, and a roadmap for what's next.
Fit
Is this right for your team?
We'd rather say no early than bill you for something that was never going to work.
✓A good fit when
- AI is a strategic priority, but the scope has yet to be defined
- AI tools are already in use, but their impact on the business remains unclear
- A manual process is constraining revenue, margin, speed, quality or risk
- You require a partner who delivers, rather than one who only advises
- An executive sponsor is accountable for the outcome
×Not a fit if
- You are seeking a training session or workshop alone
- You require a prototype with no defined owner
- There is no appetite to establish a baseline before proceeding
- Delivery is required on a platform outside our specialism
Questions
Before you get in touch.
Do you only advise, or do you build?
We build. Strategy work exists to decide what gets built and why — every engagement ends with something deployed, tested and in your source control.
What if we don't know the first use case yet?
That's the normal starting point. The opportunity map exists precisely to find it: we look at where work is slow, manual or error-prone, then rank candidates by value and feasibility.
What if we're not a Microsoft shop?
That's common, and it isn't a blocker. Microsoft and Azure are where our engineering is strongest, so it's what we'll recommend and what we build on — including setting up a tenant from scratch if you don't have one. If you're committed to another cloud, we'll tell you honestly whether we're still the right fit.
Do we need our data sorted before we start with AI?
Not entirely, but you can't skip it either. Most agent projects fail on data access and quality rather than models. Where the foundations aren't there, we build them as part of the work.
How do you handle security and IT sign-off?
Everything runs inside your own cloud tenant, under your identity provider, network boundaries and policies. We produce the architecture, data-flow and controls documentation your security team will ask for.
What does a first engagement cost?
The assessment is fixed-price and scoped to a couple of weeks, so you know the number before you commit. Build work is quoted per workflow once the baseline is agreed.
Talk to us
Start with a conversation, not a proposal.
Thirty minutes, no charge, no slide deck. Tell us what you want AI to do for the business and we'll help you find the workflow worth funding. If we're not the right firm for it, we'll say so.
What you'll get
- A read on where you sit on the maturity ladder
- A shortlist of workflows worth automating first
- A rough baseline and what the gap is costing you
- An honest build / don't-build call
Prefer email? contact@phe-consulting.com