01
Support and email triage
Incoming messages read, categorised, routed and drafted against your own past replies, with anything uncertain escalated to a person instead of guessed at.
AI automation
We automate the repetitive work,then watch it.
Support triage, document handling and search across your own data, built as a monitored pipeline rather than a convincing demo.
The problem
Why do most AI projects never leave the demo?
Because a demo meets clean data and production does not, and nothing was built to notice when it starts failing.
How it usually goes
- An impressive demo that meets real data and stops being trusted
- No monitoring, so failures surface through a customer
- Model output acted on with no confidence threshold
- No way to tell whether it saved anything
- Prompts scattered across scripts nobody can audit
- Business data sent to a service that trains on it
How we do it
- Built as a monitored pipeline rather than a demo
- Alerts on failure, backlog growth and quality drops
- Anything uncertain goes to a human queue
- Cost and volume per run tracked, so the saving is a number
- Rules and prompts in one place, versioned and readable
- Business tier APIs that do not train on your data
How it works
What an automation actually looks like
The model is one step of five. Everything around it is what turns a convincing demo into something a business can rely on, and the loop back from Watch is why it stays reliable.
- 01
Connect
- Email, sheets and CRM
- WhatsApp and forms
- 02
Structure
- Clean and deduplicate
- One shared data model
- 03
Decide
- Model with guardrails
- Rules you can audit
- 04
Act
- Write back to systems
- Notify or escalate
- 05
Watch
- Live dashboard
- Alerts the moment it fails
Measure and tune, back to Decide
AI automation
What we automate most often
02
Document and invoice handling
Purchase orders, invoices, contracts and forms read into structured data and pushed into your accounting or operations system, with confidence thresholds you control.
03
Search across your own data
Ask a question in plain language and get the answer with the document it came from, across the systems your team already uses, with permissions respected on every result.
04
Reporting that writes itself
The weekly summary somebody currently assembles by hand, generated from the source systems and delivered where the team already reads things.
05
Data entry between systems
The copying and pasting between two tools that never got an integration, done reliably and logged so you can see every record it touched.
06
Lead qualification and follow up
Enquiries read, enriched, scored against your own criteria and drafted a reply, so the sales conversation starts sooner and nothing sits unanswered.
Automation nobody is watching breaks quietly, and you hear about it from a customer.
As standard
In every automation we build
- 01A dashboard built around your process, not a generic tool view
- 02Alerts on failure, backlog growth and quality drops
- 03Rules and guardrails you can read and audit
- 04A human review queue for anything uncertain
- 05Costs tracked per run, so value stays measurable
- 06A tuning cycle after launch, not a handover and goodbye
The timeline
What a typical automation looks like
The model is the quick part. Getting the input clean and the guardrails right is where the work is, and the tuning never really stops.
- Week 1
Find the task worth automating
We look at volume and time spent, and say so if it will not pay for itself.
- Week 2
Data and guardrails
What clean input looks like, and where the threshold for a human sits.
- Weeks 3 to 5
Build the pipeline
Ingest, decide, act and watch, with the dashboard built alongside rather than after.
- Week 6
Shadow run
It runs against real work without acting, so you compare before trusting it.
- Ongoing
Review and tune
Outputs reviewed on a cycle, thresholds tightened, dead steps retired.
- Week 1
Find the task worth automating
We look at volume and time spent, and say so if it will not pay for itself.
- Week 2
Data and guardrails
What clean input looks like, and where the threshold for a human sits.
- Weeks 3 to 5
Build the pipeline
Ingest, decide, act and watch, with the dashboard built alongside rather than after.
- Week 6
Shadow run
It runs against real work without acting, so you compare before trusting it.
- Ongoing
Review and tune
Outputs reviewed on a cycle, thresholds tightened, dead steps retired.
Questions
Questions about AI automation
Not on the setups we build. Business tier model APIs do not train on your data by contract, and we configure them that way deliberately. Where data genuinely cannot leave your infrastructure, we build on open models you host yourself, which costs more to run and we will say so plainly rather than steering you either way.
It will, which is why the design assumes it. Anything below a confidence threshold you set goes to a human queue instead of being acted on, every decision is logged with its input so you can see what happened, and the review cycle after launch exists to tighten the cases that keep coming back.
Because we instrument it. The dashboard shows volume handled, what needed a human, and what each run costs to operate, so the saving is a number rather than a claim. If a step stops earning its place we tell you and retire it.
No, and almost nobody is. Getting the input clean and structured is part of the work, and it is usually where most of the effort goes. The modelling is the easy part.
Tell us which task is eating the hours.
Describe the repetitive work and roughly how much of it there is. We will tell you honestly whether automation pays for itself, and what it would take.