Typical first build
2–4 weeks
One process, running in production, with your team trained on it.
AI workflows & automation
The work your team repeats every week, handed to software.
We start with the processes you'd never put in a job description — retyping invoices, chasing approvals, rebuilding the same report. Then we automate the ones worth automating.
Where we start
Your inbox
The highest-volume repetition in most SMBs is email-shaped.
What you own
All of it
Your accounts, your data, documented handover. No lock-in.
What we build
01
Workflow automation
Invoicing, approvals, recurring reports, notifications, data moving between systems that were never meant to talk.
Typical build: 2–4 weeks
02
Assistants & chatbots
After-hours customer triage, and internal assistants that answer from your own documents rather than guessing.
Customer-facing · internal
03
Data & reporting
Numbers pulled from your systems into one dashboard, with the monthly report assembled before you ask for it.
Dashboards · scheduled reports
04
CRM & sales automation
Lead capture, follow-up sequences, pipeline hygiene, and alerts when a real opportunity goes quiet.
Follow-ups · scoring · pipeline
05
Document intelligence
Invoices, contracts, delivery notes and forms read automatically, with anything unusual sent to a human.
Extraction · validation · exceptions
06
Custom integrations
Connecting the tools you already pay for. Most automation value is in the gaps between them.
APIs · middleware · handover docs
How a project runs
We automate one process at a time and put it live before starting the next. You see value before the invoice grows.
01
Shadow
We sit with the person who does the task and time it honestly. Some processes should be deleted, not automated.
02
Estimate
Hours returned per month, build cost, running cost. If the maths doesn't work we tell you before you commit.
03
Build & run in parallel
The automation runs alongside the manual process until the outputs match for two weeks straight.
04
Hand over
Documentation, training, and a monitoring plan. You can keep us on retainer or take it in-house.
Case note
Freight forwarder, month-end reporting
Two staff spent the first three days of every month pulling numbers from four systems into a spreadsheet. Now the figures arrive assembled on the 1st, with the exceptions flagged for a human to check.
Roughly 40 hours a month back, and the report is no longer late.
Case note
Financial services, fraud detection
A firm in the financial sector needed to catch fraudulent applications and transactions more reliably. The constraint that shaped everything: customer data could not leave their own infrastructure. That ruled out sending it to a public AI service, so we designed for deployment inside their perimeter.
We audited how the fraud team actually worked before proposing anything — what they look at, what they miss, and where the time goes. The system that came out of it runs locally: a language model grounded in their own case history and policy documents through retrieval, and tuned on their data, so its judgement reflects their patterns rather than the general internet.
It flags suspicious applications and transactions. The part the team valued most was what happens after the flag — pulling the related records together, surfacing comparable past cases, and drafting the written findings an investigator would otherwise assemble by hand.
Designed for on-premise deployment. No customer data leaves their infrastructure.
Free AI opportunity audit
We look at three of your recurring processes and estimate the hours each would return. Written up, no commitment.
Request the audit