AI systems
AI systems that answer from your own data.
Work that takes too long, information nobody can find, decisions made without the full picture. The ordinary problems a company already has, handed to AI and automation that know the business.
What this means
The answers a company needs are usually already inside it, spread across a warehouse, a CMS, a folder of documents, and the people who remember where things are kept. We start in that material on day one, with the real records and all the mess they carry, so what the system returns is measured against what the business actually holds.
The guardrails go in from the start rather than after. Every answer can be traced back to the material it came from. Nothing the system does to your data happens without a check in front of it. A prediction shows what drove it, so somebody can look at a number and disagree with it for a reason.
The shapes are few. Something that answers questions over material a company already holds. Something that predicts a number the business currently guesses. Something that takes over work a person repeats. Most requests turn out to be one of those, or a combination, and which one it is usually becomes clear in the first conversation.
Questions
What people ask first.
What would you actually build for us?
That is what the first part of the work answers. Most companies have more opportunity than they can see from the inside: work a machine could do faster, information that should be a question rather than a search, decisions made on instinct because the numbers take too long to arrive. We look across the operation, find where AI and automation would actually save time or money, and start with whichever pays back soonest.
How does a first project start?
With a look at the data before anything is promised. A week or two establishing what is actually in the systems, what state it is in, and whether the question you want answered can be answered from it at all. If it cannot, that is worth knowing before a build rather than during one.