If someone on your team spends part of every day copying details out of emails, PDFs and web forms into a spreadsheet or a CRM, that is the work this service removes. I build automations that collect each record where it arrives, check it, and file it in the right place, so people only see the items that need a decision.
What data entry automation fixes
Manual data entry rarely fails all at once. It fails a little every week: a postcode typed with a missing letter, an invoice keyed twice because two people saw the same email, a supplier’s date read the American way, a new customer who never reaches the CRM because the person who usually adds them was on leave. Each mistake is small, but they pile up in your reports, your chasing and your month-end.
When the collecting, checking and filing run automatically, records arrive in a consistent shape and errors are caught at the point of entry, not weeks later by your accountant. The time your team spent retyping goes back into work that needs a person.
Who it suits, and who it does not
It usually pays off for UK small and medium businesses where the same kinds of documents and form submissions arrive every day, for example:
- firms keying purchase invoices, receipts or delivery notes into accounting or job software;
- service businesses copying enquiry details from email into a CRM or booking system;
- teams that receive orders, timesheets or applications as attachments and rebuild them in a spreadsheet;
- operations run from a shared inbox where nobody is quite sure what has been entered and what has not.
It is not worth building if the volume is a handful of records a week, or if your process still changes every month. In that case I would tidy the process first and automate once it has settled, because an automation built on a moving process needs rebuilding almost as often as it runs.
What the work covers
Collecting records where they arrive
Records come from web forms, a shared mailbox, attachments, uploaded files and other software. I set up the intake so each source is watched and every new item is picked up once. Where your software can send data the moment something happens, a webhook does the job; where it cannot, a scheduled check of the mailbox or folder does.
Reading documents
Typed forms and system exports can be read field by field. Scanned PDFs, photographed receipts and invoices in a different layout from every supplier need a document-reading step, which may use optical character recognition or an AI model. The rule I apply here is simple: if a value cannot be read with confidence, the automation flags it for a person rather than guessing.
Checking the data
This is the part most homemade automations skip. I add field validation for the things that commonly go wrong in UK records: postcode format, VAT number format, dates in day-month order, phone numbers, and whether net plus VAT equals the gross figure on an invoice. Deduplication stops the same invoice or contact being filed twice when it arrives by two routes.
Filing it in the right place
Clean records are written to the system that should own them: your accounting package, a CRM, a job management tool, a database or a spreadsheet. Field mapping decides which value lands in which field, and I write that mapping down so you can see exactly what goes where. Files are renamed to one convention and stored in a predictable folder.
Exceptions and an audit trail
Anything that fails a check goes to a review queue with the reason attached, so a person can fix it in a minute rather than hunting for it. Every record keeps a log of where it came from and what happened to it, which helps when your accountant or a customer asks a question months later.
What I deliberately leave to people
Some steps should stay with a person, and I build them in as approvals rather than pretending otherwise. Paying a supplier, changing bank details, deleting records and sending anything to a customer on the strength of extracted data all keep a human in the loop. A changed sort code on an invoice is a common sign of invoice fraud, so I make sure it is flagged, never quietly accepted.
I also do not automate the reading of documents that carry health information, criminal records or similar sensitive material without a specific conversation about whether it should be done at all.
How I run a build
- Map the current process. I sit with whoever does the work now, on a call or with a screen recording, and list every source, every field and every judgement they make without thinking about it.
- Agree the rules. What counts as a valid record, what goes to review, and which system is the source of truth for each type of data.
- Build on a sample. I test against a set of your real past documents, including the awkward ones, before anything touches live data.
- Run in parallel. For a short period the automation runs alongside the manual process, and we compare results before switching over.
- Hand over. You get the documentation and a walkthrough, so the build does not depend on me to keep running.
The platform depends on what you already use and how much data passes through. Tools such as Zapier and Make suit many small businesses; higher volumes or direct connections through an API may call for something else. The platform is agreed with you before anything is built.
Data protection: invoices, statements and CVs are personal data
Most documents worth automating contain personal data: names on invoices, addresses on orders, employment history on CVs. Under UK GDPR you remain responsible for how it is handled, so I design each build to collect only the fields you need, which is data minimisation in practice. Before a build goes live I check that every third-party tool in the chain is covered by a data processing agreement, and where the data is stored.
Problems I see most often
- Dates read in month-day order by tools set to US defaults, so 3 April becomes 4 March.
- Automations that write straight into accounting software with no review step, so one bad read becomes a bad ledger entry.
- The same contact held in three systems, each slightly different, with no agreed source of truth.
- Builds tied to one employee’s personal login, which stop working the day that person leaves.
- No alert when an automation fails, so the backlog is discovered weeks later.
What you receive
- A written map of the process before and after, with the validation rules and field mapping.
- The working automation, set up in accounts your business owns.
- A review queue for exceptions and an alert when something fails.
- A short guide for your team on handling exceptions and making small changes.
Pricing depends on the number of sources, document types and destination systems. Each quote is written after a free first call, in GBP, and agreed in writing before work starts.
Next step
Send me a short description of the documents or forms you key in by hand, roughly how many arrive each week, and where they need to end up. I will tell you which parts I would automate first and which I would leave alone. You can get in touch through the contact page or book a free 30-minute call.