AI Automation
Workflows that read documents, route requests, draft replies, and update systems, so nobody copies data between tabs. For teams spending hours a week on repeatable admin such as intake, triage, reporting, and reconciliation.
Who this is for
- Finance or operations staff re-keying invoices, orders, or forms into a system.
- A shared inbox where requests are read, categorised, and forwarded by hand.
- Weekly reports assembled from exports, spreadsheets, and screenshots.
- Reconciliation between two systems that never quite agree.
What is included. Tick what you need.
Artefacts, not adjectives. Each is something you can point to at the end. Tick the ones your project needs and send the list with your enquiry; we reply with a written scope.
Technologies we use for this
The relevant slice of our technology matrix. Nothing here that we cannot staff today.
- Python (Django, FastAPI, Flask)
- Node.js (Express, NestJS)
- PHP (Laravel)
- Go
- REST
- GraphQL
- gRPC
- WebSockets
- Celery
- Redis Queue
How we deliver it
The five steps every engagement goes through, in the form they take for this service.
Scope
A call with an engineer, then a written scope: what is in, what is out, and what it costs.
Architecture
Data model, API contract, and infrastructure plan, approved before code is written.
Build
The automation runs alongside the manual process first, with results compared weekly until it earns the right to take over.
Harden
Tests on the paths that matter, error tracking, a performance pass, and a security review.
Launch and hand over
Production deployment, monitoring, documentation, and every repository transferred to you.
From the blog
- Building AI agents that can safely modify customer dataRead access is forgiving of mistakes. Write access is not. The permission model, approval flow, and audit trail that make a write-capable agent safe to ship.
- What are AI agents, actually?Not a chatbot, and not magic: an AI agent is a model that can call tools and decide what to do next. What that definition includes, excludes, and implies.
- MCP servers: exposing your systems to models safelyThe Model Context Protocol is now how tools reach a model. How we design an MCP server for a client system: scope, safety, and what to leave out at first.
Questions we get asked
Which processes are worth automating?
Ones that are frequent, follow rules most of the time, and have a clear owner. We map the process first and tell you which steps pay back and which do not.
What happens with exceptions?
They go to a person with the extracted data and the reason it was flagged. The exception rate is a number we drive down over the first weeks.
Do we need to change our systems?
Rarely. Most automations connect through existing APIs or exports. Where a system has no API, we say so before quoting.
Who maintains it afterwards?
If the pipeline lives in n8n or Zapier, your team can adjust rules directly. Code-based pipelines come with documentation and a maintenance option.
How accurate is document extraction?
High on structured documents and measured on yours before we promise anything. Confidence thresholds route uncertain items to review rather than guessing.
Tell us what you are building.
We reply within one business day with how we would build it, what it would cost, and which engagement model fits.
- 01Tell us what you are building
A short form or an email. No deck required, and "not sure yet" is a fine answer.
- 02A call with an engineer
Within one business day. Technical questions get technical answers, from the person who would build it.
- 03A written scope and quote
Fixed price where the scope is defined. The document is yours whether or not you go ahead.