Systems review
We look at the work as it happens, with the people doing it, and agree what is worth fixing first.
AI INTEGRATIONS / USEFUL AI
Put AI where the work already happens.
The useful version of AI is usually not another chatbot. It reads the document, finds the answer, drafts the reply or routes the request inside the workflow your team already knows.
THE PROBLEM
What usually brings people here
- Staff spend too long searching for information that already exists.
- The same support and admin questions are answered again and again.
- Invoices, forms, reports and PDFs are read and routed by hand.
- AI experiments are disconnected from the systems that run the business.
WHAT WE CAN SHIP
A useful first version
- Private knowledge search and internal assistants
- Document extraction, classification and summaries
- Inbox and customer-support triage
- AI features inside portals and dashboards
- Human review, fallbacks and usage controls
IN PLAIN TERMS
What a useful AI integration looks like
A useful AI integration sits inside a workflow your team already uses. It reads the invoice, finds the answer in your own documents, drafts the reply or routes the request to the right person, and it shows its working so someone can check it.
The test is simple. Does it save time on a task that happens every day, and can a person review the output before it matters? If the answer is no, a rule or a better form is usually the cheaper fix.
CHOOSING A ROUTE
AI, a fixed rule, or a person?
Not every slow task needs AI. The cheapest fix is often a rule, and the most reliable is often a person with better information.
| Route | Best when | Watch out for |
|---|---|---|
| A fixed rule | The task follows the same steps every time and the input is predictable. | Rules break quietly when the input changes shape. |
| An AI step | Someone has to read, judge, summarise or draft something before acting. | Outputs need review, and accuracy depends on the quality of your source material. |
| A person, better supported | The decision needs judgement, context or accountability. | The same question gets answered from scratch every time unless the answers are captured. |
HOW IT USUALLY RUNS
Smallest useful version
A focused build lands in two to four weeks. You see it working before we plan the next step.
Stay close
The same developer handles the changes, the integrations and the questions after launch.
WHAT GOES WRONG
Where AI projects usually go wrong
- Starting with the model instead of the task. The useful question is which decision is slow, not which AI is newest.
- No review step. Anything customer-facing or financial needs a person able to check it before it counts.
- Messy source material. AI is only as good as the documents, records and answers it is given.
- No measurement. If you cannot say what the AI saved in time or errors, you cannot tell whether it is working.
BEST FIT
Teams with a clear repetitive task and enough real material to test the AI against. We start small and keep a person in control.
QUESTIONS WE GET ASKED
Straight answers before you commit.
Is our data used to train AI models?
No. We use business-grade AI services with training disabled, keep your data inside your own accounts where possible, and agree the data handling rules for each project before anything is built.
What happens when the AI gets something wrong?
Every integration includes a fallback. Low-risk tasks can proceed with logging, and anything customer-facing or financial goes to a person for review. The workflow is designed around the mistake rather than the assumption that it will not happen.
Do we need clean data first?
Better input makes better output, so we start by looking at the documents and records the AI will read. You do not need a perfect data project before starting, but obvious gaps are worth fixing first.
How do you measure whether it worked?
We agree a simple measure before building: hours saved per week, response time, or error rate. If the numbers do not move, we say so.
Can it work with our existing systems?
Yes, where those systems expose a safe way to connect. The AI step sits inside the workflow rather than replacing it, so your team keeps working in the tools they know.
KEEP READING
How to Automate Invoicing with AIWhat AI should and should not be allowed to touch in an invoicing process.Read more DesignIQ case studyAn AI pipeline that reads drawings and checks them against a standards base.Read more Melissa Berry Makeup case studyAn assistant that answers booking questions and sends follow-ups.Read more How to Automate Invoicing in ExcelA practical starting point with honest limits.Read more
NEXT STEP
Bring us the process, not a spec.
We will sit with the people doing the work, map the handoff, and tell you what is worth fixing first.
Book a systems review