Copilot
Copilot & Copilot Studio
Custom agents grounded in your own documents and data, deployed to the people who need them — inside Teams and Office rather than as another portal to log into.
- Grounding
- Custom agents
- Teams
AI & Process
AI that starts from your process, not from a demo.
We begin by mapping how the process runs today, identifying the steps where automation or a model changes the economics, and building only those — with a compliance layer added where the process is regulated.
Process firstWe map before we build
MicrosoftMicrosoft AI Cloud Partner Program member — Copilot, Power Platform, Azure AI
Any sectorPharma, professional services, accounting, small business
Yours to runBuilt so your team can operate it after we leave
The problem
The pilot that works, and the process that never changes
A common pattern: a promising proof of concept, followed by limited adoption. Usually not because the technology failed, but because it was applied to a step that was not the constraint, or to a process that had never been described precisely. A year later the licence is still in place and the work is still done as before.
Where projects stall
What we find when we are called in afterwards.
- No baselineNo measurement of how long the process took beforehand
- Wrong stepAutomation applied to the visible task, not the constraining one
- DataThe model needed data that lives in four systems and one inbox
- OwnershipBuilt by a vendor, with no internal capability to operate it
- ApprovalQA asked to sign off a finished system they never saw designed
How we approach it
The steps that determine whether the work reaches production.
- Map firstThe process as performed, including the workarounds in daily use
- QuantifyTime, error rate and cost per step, so value can be assessed afterwards
- Choose narrowlyTwo or three candidates, not a transformation programme
- Build smallSomething in production in weeks, then extended
- Govern earlyWhere the process is regulated, the intended use, GxP boundary and quality oversight model are defined before development.
Method
Five steps from process to production
Engagements typically run six to sixteen weeks depending on how many processes are in scope.
Step 01
Process discovery
Sessions with the people who do the work. We map what happens, not what the SOP says happens — the gap between the two is usually where the value is.
Step 02
Opportunity triage
Each step scored on volume, effort, error cost and data availability. Some become automation, some become a model, and some need only the form redesigned.
Step 03
Design & boundary
Target process, data flows, and — where relevant — the GxP boundary: what the system decides, what a person decides, and what evidence each produces.
Step 04
Build
On the Microsoft stack where it fits, on open tooling where it does not. Deployed into your tenant, under your control, with your team involved.
Step 05
Adopt & measure
Training, handover, and measurement against the baseline from step one. If the numbers do not move, we say so rather than declaring success.
The stack
Microsoft where it fits, open tooling where it does not
As a member of the Microsoft AI Cloud Partner Program, we can build within the Microsoft tenant many clients already operate. This may reduce technical onboarding effort, but supplier qualification and approval remain subject to the client's procedures. When the right answer is outside the Microsoft stack, we say so.
Power
Power Automate
The automation that removes the most hours: approvals, hand-offs, notifications, data moved between systems that were never designed to exchange it.
- Approvals
- Integration
- RPA
BI
Power BI & Fabric
Reporting built on a data model and a pipeline that hold up to the first question about where a number came from.
- Data model
- OEE
- Quality KPIs
Azure
Azure AI & ML
Where a model is warranted: document extraction, classification, retrieval over your own corpus, forecasting, anomaly detection on process data.
- RAG
- MLOps
- Foundry
D365
Dynamics 365
Process redesign inside the ERP rather than around it — Business Central configuration, batch and quality workflows, and the integrations either side.
- Business Central
- Workflows
- ERP
Open
Beyond the stack
Python, open-source models, self-hosted inference and container pipelines on AWS or on premise, where data residency, cost or capability makes the platform the wrong answer.
- Python
- Containers
- Self-hosted
Where it lands
Regulated, and everywhere else
The GxP version of this work carries the most constraints, and the method transfers to organisations with no regulator at all. Law firms, accountants, administrators and small businesses share the same underlying problem: heavy documents, manual hand-offs and expert time spent on work that repeats.
Pharma · Quality
Deviation triage and drafting support
Retrieval over historical deviations and CAPAs so an investigator starts from precedent rather than a blank form. The model proposes, a qualified person decides, and the boundary is written into the QA position before development.
Manufacturing
Batch record review, prioritised
Review by exception rather than page by page: anomalies surfaced from process and historian data so reviewers spend their time where an event occurred.
Supply chain
Supplier documentation, extracted
Certificates, specifications and quality agreements read automatically, key terms extracted into a structured register, and expiry tracked rather than discovered during an audit.
Professional services
Contract and document review for a law firm
No GxP anywhere in sight, and the same underlying problem. Retrieval over a private corpus, clause comparison against a house position, and a drafting assistant that cites its source.
Accounting & finance
Bookkeeping that stops being retyping
Invoices and receipts read and posted against the right account, bank reconciliation proposed rather than performed by hand, VAT and payroll documents classified and filed. The accountant reviews exceptions instead of keying in the routine, which is where the margin in a practice sits.
Administration
The back office of a small company
Quotes assembled from a price list, orders acknowledged, correspondence drafted, contracts and certificates tracked before they expire. For a firm of ten people this recovers a measurable share of a working week, without hiring and without changing the tools already in use.
The compliance layer
Governance proportionate to intended use and regulatory risk.
Governance should be proportionate to intended use and risk. An AI assistant used only to draft non-regulated marketing copy has a different control need from a system that supports a batch disposition decision. For regulated use cases, the intended use, human oversight, acceptance criteria and change strategy should be defined before development.
- 01Intended use and GxP boundary — what the system decides, and what a qualified person decides.
- 02Model qualification against acceptance criteria defined in advance, not after seeing the output.
- 03Change control designed for learning or externally updated systems, including controlled retraining, versioning and impact assessment.
- 04Monitoring, drift detection and the periodic evidence an inspector will ask to see.
Start with a process, not a tool.
Tell us one process that costs more than it should. We will tell you whether AI is the answer — and frequently the answer is that it is not, and something simpler is.
Typical engagement6–16 weeks
PlatformMicrosoft tenant, or open
SectorsRegulated and not