Automate the work nobody should be doing by hand
We don't start with a tool. We start with your processes, work out what they actually cost you in hours, and automate the ones with a real return — with governance in place before anything goes live.
The automation audit
Four stages, fixed scope, and a written report you keep whether or not we build anything for you.
1. Map how work actually moves
We sit with the people doing the work — not just management — and document the real process, including the spreadsheet workarounds nobody mentions in a meeting.
2. Quantify the cost of each step
Every manual step gets an hours-per-month figure and an error-rate estimate. This is where 'that only takes five minutes' turns into 90 hours a year.
3. Rank by return, not by hype
Opportunities are scored on hours saved per dollar spent, risk, and how quickly they can ship. Some of the best wins turn out to be a Power Automate flow, not AI at all.
4. Govern before you deploy
Data access, retention, acceptable-use policy and staff training are set up before anything goes live — so AI reduces risk rather than creating a new one.
Capability, not a demo
Everything below runs in production for real businesses, on platforms with enterprise support behind them.
Microsoft 365 Copilot
Rollout with the licensing, permissions clean-up and data governance done first — because Copilot surfaces exactly the files your permissions let it.
Power Automate & Power Apps
Approvals, onboarding, reporting and system-to-system integration built on tooling you likely already own.
Document & email AI
Quote generation, invoice and PO extraction, contract summarisation and inbox triage — with a human checkpoint where it matters.
Custom AI assistants
Assistants grounded in your own policies, product data and documentation, so answers come from your business rather than the open internet.
AI governance & policy
Acceptable-use policy, data classification, and the controls to keep client data out of tools it shouldn't reach.
Measurement
Baseline the hours before, measure after. If an automation isn't paying for itself we'll tell you and switch it off.
AI that your risk register can live with
Most failed AI projects don't fail technically. They fail because nobody agreed what the tool was allowed to see, who checks the output, or what happens when it's wrong.
- Permissions audited before rollout — AI inherits whatever access your users already have
- Data classification so confidential material is excluded from AI-accessible locations
- Enterprise services with commercial data protection, not consumer tools on personal accounts
- Written acceptable-use policy your staff actually read, plus short practical training
- Human review checkpoints on anything customer-facing or financial
- Logging and retention settings configured deliberately, and documented for auditors
The things everyone asks before switching
If your question isn't here, ask it directly — we'd rather answer it now than have you guess.
Isn't AI just hype for a business like ours?
Some of it is. That's exactly why we start with an audit rather than a product. In a lot of businesses the highest-return automation is a deterministic workflow — an approval chain or a data sync — with no AI involved. We'll tell you when that's the case rather than selling you a model you don't need.
Will our data be used to train someone else's AI model?
Not in the enterprise services we deploy. Microsoft 365 Copilot and Azure OpenAI operate under commercial data protection terms, meaning your prompts and data aren't used to train the underlying foundation models. We configure tenancy, data residency and retention explicitly, and document the configuration for your records.
What does an automation audit cost and what do we get?
It's a fixed-scope engagement, priced up front based on how many processes are in scope. You receive a written process map, an hours-and-cost estimate for each manual step, and a ranked list of automation opportunities with expected payback. The report is yours regardless of whether we build anything.
How long before we see a result?
The audit typically runs over a few weeks depending on scope. First automations usually ship shortly after, because we deliberately sequence a fast, visible win first — it builds staff confidence, which is the real constraint on adoption.
Our staff are worried AI means job cuts. How do you handle that?
Honestly, and early. In practice we're almost always automating the parts of a role people dislike — re-keying data, chasing approvals, formatting documents. We involve the team in the mapping stage so they help decide what gets automated, and we run short training so they can use the tools rather than be replaced by them.
Find out what your manual processes are really costing
Book a conversation about an automation audit. We'll scope it properly, tell you what's realistic, and give you a ranked list of opportunities you can act on with or without us.
- Free 30-minute review
- Written findings you keep
- No obligation, no sales pressure