AI Automation for UK Businesses

Rules where you can, AI where you must

Classic workflow automation follows fixed rules: if the subject contains "invoice", move it to accounts. It is fast, cheap and predictable, and it breaks as soon as real-world input stops following the pattern. AI automation adds a model at the points where a person currently has to read and judge, and keeps rules everywhere else.

A typical inbound email workflow looks like this:

1. An email arrives in a shared mailbox, picked up through Microsoft Graph or the Gmail API.
2. Rules handle the obvious cases, such as known senders and automatic notifications.
3. A model classifies the rest (order query, complaint, new enquiry, supplier invoice), extracts the key fields and rates its confidence.
4. Code looks up the customer or order, creates or updates the CRM record and assigns the right person.
5. For routine queries, a draft reply waits in the agent's queue for approval.

Common AI automations we build

• Inbox triage and routing for sales, support and accounts mailboxes
• Lead enrichment: summarise a web form enquiry, categorise it by service and size, and create a scored lead in your CRM
• Order and returns handling: read return requests, match the order, check the policy and draft the response
• Call and meeting notes: turn a transcript into a summary, actions and CRM updates
• Data clean-up: standardise product descriptions, addresses or job titles across thousands of records with low-confidence rows flagged
• Document intake: covered in detail under AI document processing

Where a task needs several systems and the steps vary each time, an AI agent may fit better. We will say which during scoping.

Built to run unattended

Automation that silently fails is worse than no automation. Our workflows run as background jobs on a queue (Laravel Horizon, BullMQ or Celery on Redis, or a cloud queue), with:

• Retries with backoff when a model provider or API is slow or rate-limited
• Idempotency, so a retried job never creates a second CRM record or duplicate bill
• A dead-letter queue and alert for jobs that keep failing
• Confidence thresholds: high-confidence results run automatically, borderline ones go to review
• Full run history, so you can see what was received, what the model decided and what changed

No-code tools or owned code?

Power Automate, Zapier, Make and n8n all now offer AI steps, and for simple, low-volume flows they can be the right answer. We are happy to build alongside them. Owned code makes more sense when volumes are high, when logic gets complicated, when you need proper testing and version control, or when per-task pricing starts to add up. It also avoids spreading sensitive data across several third-party platforms. We will give you a straight recommendation either way.

Measuring whether it worked

Before we build, we record a baseline with you: how many items arrive each week, how long they take to handle, and how often they are routed or keyed wrongly. After go-live the dashboard tracks the same things, plus the AI-specific measures: the share of items processed without a person, the share sent to review, how often reviewers changed the AI result, and the model cost per item.

Those numbers tell you where to go next. A high edit rate on one category usually means the prompt needs clearer rules or better examples. A growing review queue might mean a supplier or customer has changed their format. A low edit rate over several months might justify raising the confidence threshold so more items run unattended. Every change is tested against the evaluation set before it goes live.

Data protection and human oversight

Automations often process personal data and sometimes make decisions about people, such as prioritising applications or flagging accounts. According to GOV.UK guidance on the Data (Use and Access) Act 2025, UK GDPR now permits most significant solely automated decisions only with safeguards, including the right to human intervention and to contest the decision. We build that route in: clear logs of what was decided and why, a review queue, and a way to override. For routine triage, a person stays one click away.

Every project is a fixed-price quote after a free chat.

What we deliver

• A process map showing which steps suit AI, which suit rules and which stay manual
• Workflow services with queues, retries and alerting
• AI steps for classification, extraction, summarisation or drafting with schema-validated output
• Integrations with Microsoft Graph, Gmail, CRMs, accounting and ecommerce APIs
• Confidence thresholds and a review queue for uncertain cases
• Run history, audit logs and a dashboard of volumes, accuracy and exceptions

How it works and what it costs

Every project gets a fixed-price quote after a free initial chat and a short scoping stage. You own the code and the data.

1. Free chat

   Tell us the problem in plain English: what you do now, what goes wrong and what "better" looks like. No charge, no obligation.

2. Scoping

   We map the processes, systems and data involved, agree what is in and out, and write it down so there are no surprises.

3. Fixed-price quote

   You get a fixed price for the agreed scope, or a phased plan for bigger builds, so you can start small and prove it works.

4. Build and test

   We build in short stages you can see and try, test against real data, then go live carefully with a rollback plan.

5. Hand over and look after

   You own the code and the data. We can host it, support it and keep improving it, or hand it to your own team.

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