AI automation for the work nobody should do by hand
Reading order emails and retyping them. Extracting figures from PDFs. Chasing approvals. Copying data between two systems. These tasks are predictable enough to automate and expensive enough to be worth automating.
- Typical timeline
- 5 to 9 weeks
- Usually built with
- n8n, Node.js, Claude / OpenAI
- You own
- Code, data and accounts, in your name
About AI & workflow automation
Automation works best on tasks that are frequent, rule-based and currently done by a person reading something and typing it somewhere else. Language models have made a category of these tasks newly automatable, because they can handle documents that are not in a fixed format. We build automations that do the routine work, flag the genuinely ambiguous cases for a human, and keep a record of every decision so you can audit what happened.
Tasks that are ready to automate
Usually a good fit for
- Businesses receiving orders, enquiries or invoices by email in inconsistent formats
- Teams re-entering the same data into two or more systems every day
- Operations that depend on someone remembering to send a follow-up or a reminder
- Finance teams processing large volumes of invoices, bills or expense claims
- Companies whose staff answer the same internal questions repeatedly
- Businesses where reports are assembled manually every week or month
Signs it is time
- Order and enquiry emails are read and retyped into a system by hand, all day
- Details are copied between systems, and a typo in one is discovered much later
- Invoices and delivery notes arrive as PDFs and scans in a dozen different layouts
- Follow-ups depend on human memory, so some quietly never happen
- Approvals sit in an inbox because the approver did not notice them
Automations we build
Document and email processing
Reading incoming emails, purchase orders, invoices and delivery notes, extracting structured fields and pushing them into your system with a confidence score.
Workflow automation
Multi-step processes that run without supervision: when this arrives, validate it, route it, notify these people, wait for approval, then update that system.
System-to-system synchronisation
Keeping two systems in agreement automatically, with conflict handling and a log of every synchronisation.
AI knowledge assistants
An assistant that answers questions from your own documents, policies and product information, with citations back to the source.
Customer-facing chatbots
Handling common questions on your website or WhatsApp, with clear handover to a human when the question is outside its scope.
Automated reporting and alerts
Reports assembled and distributed on a schedule, and alerts raised when a figure moves outside an expected range.
What's built into every automation
- Structured extraction from unstructured emails, PDFs and scanned documents
- Confidence scoring with automatic routing of uncertain cases to a human reviewer
- Human-in-the-loop review screens so nothing important is fully unsupervised
- Validation against your master data before anything is written to a system
- Complete audit log of every automated decision and its inputs
- Retry handling, so failures raise an alert instead of failing silently
- Cost controls and usage monitoring on AI model calls
- Fallback to the existing manual process if a service is unavailable
- Configurable rules that your team can adjust without a developer
- Notifications through email, WhatsApp, Slack or Microsoft Teams
- 30-day bug-fix warranty after launch
Not included: third-party fees such as AI model usage (Claude or OpenAI), n8n hosting or licence, WhatsApp API message charges and hosting. These are billed to you directly at cost and estimated in the quote.
Tools we typically use
Final choice made during scoping.
- Automation
- n8nNode.js
- AI models
- Claude / OpenAI modelsPostgreSQL with pgvector
- Email & documents
- GmailMicrosoft 365 / Outlook
- Notifications
- WhatsApp Business APISlack / Microsoft Teams
How an automation project runs
Typical timeline: 5 to 9 weeks, depending on scope and how quickly access and content arrive.
- 013-5 days
Find the task worth automating
We look for tasks that are high-frequency, rule-based and currently manual. Automating a rare task is rarely worth the effort, and we will say so.
- 022-3 days
Measure the baseline
How many times a day, how long each takes, how often errors occur. Without this you cannot tell afterwards whether the automation was worth it.
- 031 week
Design the flow and the exceptions
The happy path is easy. We spend the time on what happens when a document is unreadable, a field is missing or a service is down.
- 042-4 weeks
Build and test against real samples
Built and tested against a large sample of your actual documents, including the messy ones, until accuracy is measured rather than assumed.
- 051-2 weeks
Shadow run
The automation runs alongside the manual process without acting, so you can compare its output to what your team does and tune it before it takes over.
- 06Ongoing
Go live with monitoring
Switched on with dashboards showing volume, accuracy and exceptions, plus alerts when something needs attention.
Is someone on your team retyping emails or PDFs?
Tell us about it. You get a written scope and a fixed price, with no obligation.
How our fixed quotes and payments work →What automation gives back
What tends to happen, not a guarantee.
- 1
Hours returned to your team
Work that consumed a large part of someone's day happens continuously in the background instead.
- 2
Consistency
An automation applies the same rules at 6pm on a Friday as it does on a Monday morning. People do not.
- 3
Faster response to customers
Orders and enquiries processed as they arrive rather than when someone gets to that part of the inbox.
- 4
Fewer transcription errors
Removing the retyping step removes an entire class of mistakes that are expensive to find later.
- 5
Capacity that scales
Volume can double without proportionally increasing headcount on routine processing.
- 6
An auditable record
Every automated decision is logged with its inputs, so you can always explain what happened and why.
Is AI accurate enough to trust with real business data?
For well-defined extraction tasks on documents, accuracy is usually high, but the honest answer is that it is never perfect. That is why we build confidence scoring and human review into every automation. Clear, high-confidence cases go straight through. Ambiguous ones go to a person. You decide where the threshold sits, and we measure real accuracy on your documents during a shadow run before anything goes live.
What happens when the automation gets something wrong?
Every automated decision is logged with the input it saw and the output it produced, so mistakes are traceable and correctable. Validation rules catch impossible values before they reach your system. If the failure rate rises above a threshold you get an alert, and there is always a manual fallback path.
Will this replace people on my team?
Usually it changes what they spend time on rather than removing the role. The person who spent four hours a day retyping orders spends that time on exceptions, customer relationships and the work that needs judgement. We think that is the honest framing, and it is also how automations get adopted rather than quietly sabotaged.
What does running an AI automation cost each month?
There is the build, quoted as a fixed fee, and then a running cost for AI model usage, which depends on volume and document size. It is billed to you directly by the model provider and is usually a small fraction of the staff time it saves. We estimate it in the quote, put usage monitoring in place, and set spending limits so it cannot surprise you.
Do we need to change our existing systems?
Usually not. Automation typically sits between your existing systems rather than replacing them. If a system has no API, we work with what it does offer: file exports, database access, or in some cases email. We assess the integration path before quoting.
Can our team adjust the rules later?
Where it makes sense, yes. Business rules like approval thresholds, routing conditions and notification recipients are exposed in a configuration screen. Changes to the underlying extraction logic need a developer, and we are clear about which is which.
Ready-made automations you can start from
Email & Document Automation
Read incoming emails and documents automatically, extract what matters and file it where it belongs.
AI Knowledge Assistant
An assistant that answers from your own documents and cites where each answer came from.
Approval Workflow System
Route requests to the right approver automatically, with delegation, escalation and an audit trail.
Lead & Follow-up System
Capture every enquiry, assign it, and make sure follow-ups happen on time.
Talk to us about AI & workflow automation
Tell us what the work looks like today and what is not working. We will come back with what we think it involves, a realistic timeline and a fixed price.
The first conversation is free, with no obligation.