Document and form processing
Invoices, purchase orders, claims, applications — read, extracted into structured fields, checked against what you already hold, and routed. The keying-in stops; the checking stays where a mistake would be expensive.
AI integration that starts with the workflow, not the model.
Most AI projects begin with a technology looking for a use, and end as a demo nobody runs. We begin by finding where the hours actually go, automate that part, and measure it — so you can tell whether it worked rather than hoping it did.
Where it earns its keep
Six places the hours are usually hiding.
The pattern is the same each time: someone reads something, decides what it is, and types it somewhere else.
Email and request triage
An inbox that has to be sorted before anyone can act on it. Classified, prioritised, drafted against, and put in front of the right person with the context already attached.
Assistants grounded in your own data
Answers pulled from your documents, your tickets, your handbook — with the source shown next to the answer, so a person can check it. Retrieval first, so it says “I don’t know” instead of inventing.
Automation across tools you already pay for
The copying between your CRM, your accounting system and the spreadsheet in the middle. Wired together properly, running on a schedule, failing loudly rather than silently.
Drafting that follows a shape
Quotes, reports, summaries, replies — anything written the same way every time from information you already hold. Drafted for a human to approve, not sent unread.
Extraction and classification at volume
Where the work is reading a lot of something and deciding what it is. Measured against a labelled set before it goes near production.
How you’ll know it works
Quality as a number, not a feeling.
A demo proves a model can do something once. None of that tells you what happens on the thousandth document, or what it costs. So we hand over the measurements alongside the system.
An evaluation set built from your real cases, before rolloutAccuracy
Token and API cost modelled per run and per monthCost
A defined human checkpoint wherever an error is expensiveRisk
Fallback behaviour when the model is wrong or unavailableFailure
The hours the workflow took before, measured again afterPayback
What we won’t do
AI for its own sake.
Use a model where a rule would do
If the honest answer is that a form, a lookup table and a database solve it, we’ll build the form and the database, charge you less, and you’ll get a system that’s right every time instead of nearly always.
Put an unchecked model in front of customers
Anywhere a wrong answer costs money or trust, a person stays in the loop. That isn’t caution for its own sake — it’s what makes the rest of the automation safe to run unattended.
Hide the running cost
Per-run and per-month costs are modelled before rollout, not discovered on an invoice. If the automation costs more than the hours it saves, we’ll say so and stop.
A note on experience
We were engineers before this was a category.
All three of us were building and shipping software before the current wave of models existed. That matters more than it sounds: an AI feature is still a system that has to be deployed, secured, monitored, paid for and maintained by someone. We use these tools daily. We aren’t dependent on them. More on how we’re set up.
Point at the workflow that eats the most hours.
We’ll tell you whether AI is the right tool for it, what it would cost to run, and how you’d measure whether it worked.