A grounded look at where a legacy tool, workflow, or pile of data is still handled by hand, and whether AI can actually help - or where it can't.
What's included
An honest assessment of where AI helps and where it doesn't
Data research and cleanup - most AI problems are data problems
Small, focused tools for classification, extraction, and search
Decision-support tooling, with a human kept in the loop
Integration into the systems you already run
Plain documentation of what the tool does and doesn't do
Example scenarios
A few hypothetical scenarios illustrating the kind of thing that comes up in practice.
The situation
A regional retailer running a 15-year-old inventory database that only a couple of employees know how to query directly.
The approach
A natural-language search layer on top of the existing database - no migration, no rebuild. Staff ask questions in plain English; the tool translates that into the actual query and returns a real answer.
What changes
The database stays exactly where it is. What changes is who can get answers out of it.
Who it's for
Businesses and agencies running systems that predate the current wave of AI tooling - a database nobody wants to touch, a manual process that's outgrown a spreadsheet, a backlog of unstructured data nobody's had time to make sense of. If the honest answer is "a script and some cleanup," that's what you'll get.
Pricing
Scoped per project after a look at what you're working with - reach out and we'll quote it.