Three packages. Clear scope.
Pick the entry point that matches where you are: map the opportunity, prove it on one workflow, or scale what already works. Each package has a fixed scope and a timeline you know upfront.
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The packages follow the way we have delivered automation for years: first find the processes where volume and exceptions pile up, then automate one process from start to finish, with people involved where their judgement matters, and expand only what the data confirms. The full picture of this approach, including an ROI calculator you can run with your own numbers, is on the service page.
See the service: AI Process AutomationThe packages
Most clients start with Discovery or go straight to Pilot when the target process is obvious. Scale is for companies that already have a successful pilot behind them.
Find the process where automation pays off first.
One process automated and proven in daily operation.
Extend automation to more processes across the company.
Discovery output is vendor-neutral: you can run the pilot plan with any team. If you continue to a Pilot with us, the Discovery fee is credited against it.
This is how the Pilot runs in practice, from launch to unattended operation.
The process runs end to end on real inputs within the first two weeks, with every action logged and a human approving each output.
Accuracy and exception rates are measured on live cases and reviewed weekly with your team. On real work, not on hand-picked examples.
Cases the system is confident about flow through without review. Your team handles only the exceptions, where human judgement is needed.
Runbooks, monitoring, and documentation. Your team operates the automation; we stay available but not required.
Picture a familiar setup. A manufacturer runs an ERP that is fifteen years old. The migration quote has seven digits and a two-year timeline, so it keeps being postponed, quite sensibly. Meanwhile a thousand invoices a month arrive as PDF attachments and paper scans, and the ERP cannot process either. So the work falls to people: every invoice is printed, retyped, matched, and forwarded by hand, a few minutes each, every working day. In an engagement like this we do not touch the migration question at all. We build an intake layer around the ERP: emails and scanner feeds land in one pipeline, OCR and LLM extraction turn each document into structured fields with a confidence score, vendor and client matching runs against the ERP's own master data, and a thin adapter writes validated entries into the old system exactly the way an operator would. When the system is not confident about a document, it sends it to a short review queue instead of guessing. The accounting team receives an orderly list ready for posting, each entry with the original document attached and a full history of how it was processed. The ERP stays. The migration decision keeps its own timeline and its own budget. What ends is the typing.
A composite of engagements we meet in manufacturing and distribution, with illustrative figures. Your numbers come out of Discovery.
Document pipelines and operational workflows we have shipped.
Fraud detection for a UK cashback platform: machine learning scores every scanned receipt before money is paid out, catches altered amounts, duplicates, and impossible shopping patterns, and sends unclear cases to a person.
View Case Study
A document processing platform that turns receipts and order emails into clean, itemised purchase data.
View Case StudyNot sure where to start? Begin with the AI Readiness Audit