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Most AI advice is written for enterprises with a transformation office and a two-year horizon. This is the other thing: find the two or three processes worth automating, build them, and show the difference against a number measured before we started.
The usual failure is starting from the tool. A company buys a licence, runs a pilot, and six months later nobody can say whether it helped. The reason is almost always that nobody measured the starting state, so there is nothing to compare against.
We work the other way round. The first question is which task consumes the most human time for the least judgement, and how long it takes today. Once that number exists, the build has a target and the result is arguable rather than asserted.
Smaller companies have a real advantage here. A decision that takes a quarter inside a large group takes a week, so the loop from idea to working system is short enough that people stay interested.
Where the wins usually are
Extracting fields from documents that arrive as PDFs and photographs, then pushing them into the system that needs them. High volume, rule-heavy, and painful by hand.
Learn more →An assistant that answers from your own documentation and escalates when it should, on the channel your customers already use.
Learn more →The copying between a CRM, a spreadsheet and an accounting tool that somebody does every week. Usually the cheapest thing to fix and the fastest to notice.
Learn more →Recurring reports assembled by hand each month, rebuilt as something that produces itself and can be trusted.
Learn more →Scoping first, and it is short. We look at how the work is done now, time it, and come back with a ranked list of what is worth building. You own that list whether or not you continue with us.
Then we build the first item, not all of them. One working system in production teaches everyone more than a roadmap, and it gives the measurement something to land on.
We stay through the switchover. A system nobody uses produces nothing, so the training and the handover are part of the work rather than an afterthought.
Smaller companies are often better candidates than large ones, because there are fewer stakeholders and a decision can be made in a week. What matters is not headcount but whether a repetitive, high-volume process exists. If three people spend a day a week on the same task, there is something to work with.
It depends on scope, and we quote a fixed amount before starting rather than billing hours. Scoping is deliberately short and cheap so you can stop after it with a plan you own. We would rather lose a project at that stage than discover the mismatch three months in.
Almost never, and we will usually argue against it. Most of the gains we document come from connecting and automating around the systems a company already runs. Replacing a working system is expensive, slow and rarely the reason the process is painful.
By volume and by pain, not by what is technically interesting. We look for tasks that are frequent, rule-heavy and currently done by a person copying between systems. Those pay back quickly and prove the approach before anything larger is committed.
Then we say so. A lot of what gets sold as AI is a database query, a better form, or an integration that nobody got around to building. Naming that costs us a project and saves you a budget.
An hour on a call is usually enough to tell whether your processes have something in them. If they do not, we will say so.
Book a scoping call