The busywork, automated end to end.
Invoices, documents, tickets, and data entry handled by AI workflows that run reliably in the background and escalate to a person only when it matters.
What you walk away with.
- 01
Live automation
An automated workflow running in production from day one
- 02
Human review
Human review queues for the cases that need judgment
- 03
Audit trail
An audit trail for every automated action
- 04
Measured savings
Measured before/after numbers on time saved
Every business has work that follows a pattern: read the document, extract the fields, update the system, notify someone.
It eats hours and produces errors, and it's exactly what AI is now good enough to own. We build automations that process the routine 90% straight through and route the exceptions to a human, so your team spends its time on judgment calls instead of copy-paste.
Sound familiar?
The sentences we hear on first calls.
Your team re-keys the same data between documents and systems every week
Heard yourself in one of these?
Talk to us→Four steps. No mystery.
- Step 01
Map the workflow
We trace one high-volume process end to end and measure what it costs you today.
- Step 02
Automate the routine
AI handles extraction, classification, and routing; rules handle what rules do best.
- Step 03
Keep humans in the loop
Exceptions and low-confidence cases go to a review queue, never silently through.
- Step 04
Measure & expand
We prove the numbers on the first workflow, then roll the pattern out to the next.
The full scope, one accountable team.
Document & invoice processing
AI extraction from PDFs and scans, validated and pushed into your systems
Email & ticket triage
classify, prioritise, and route incoming requests automatically
Data entry & sync
keep CRMs, finance tools, and databases in step without manual re-keying
Approval workflows
structured sign-off chains with reminders, escalation, and full history
Report generation
recurring reports assembled and delivered without anyone compiling them
Human-in-the-loop review
confidence thresholds and queues so people only see what needs them
We've already built this.
AI Invoice Parsing & Open Banking Payment Platform
Next.js app that extracts payment details from uploaded invoices using AI, then initiates payment via Open Banking through Barclays, with zero manual data entry.
- Zero-keying invoice payment: AI extracts sort code, account number, amount, and reference from uploaded PDFs
- Open Banking payment initiation via Barclays, removing the need for manual bank transfers
- Multi-currency support with GBP conversion and original amount tracking
Questions, answered.
The things people ask us before starting a project like this. If yours isn't here, ask us directly, we reply like humans.
Ask us anything→What's the difference between this and the RPA tools we've tried before?
Rule-based RPA breaks the moment a document layout changes. AI extraction handles variation the rules-based tools can't, while rules still handle the parts they're genuinely better at, like validation and routing. The combination is what survives contact with messy real-world documents.
What happens to the cases the AI isn't confident about?
They go to a human review queue with the extracted data pre-filled, not a blank form. Confidence thresholds decide what's automatic and what needs a person, so exceptions never process silently and wrong.
How much of our process can actually be automated?
In our experience it's rarely 100%, and treating it as all-or-nothing is why so many automation projects stall. We aim for the routine work to go straight through, with the judgment-call cases routed to a person, and prove the split with real numbers on your first workflow.
Is there an audit trail if something goes wrong?
Yes, every automated action is logged: what was extracted, what confidence score it got, who reviewed it if anyone did, and what happened next. That trail is what lets you trust the system enough to expand it.
We tried rule-based automation and it broke constantly. Will this be different?
Usually yes, because the failure mode is different: rules break silently on anything they weren't written for, while AI extraction generalises better and low-confidence cases get flagged instead of processed wrong. We still keep rules where they're the right tool, like validation and routing.
We've automated invoice-to-payment flows, claims triage, and procurement chains for real businesses. The pattern that works is never 'automate everything': it's automating the routine flawlessly and being honest about where a human still belongs.
Ready to craft tomorrow, today?
Where innovation meets execution, and ideas evolve into impactful solutions. Book a free consultation, no strings attached.