AI & Machine Learning

There's a lot of pressure right now to "add AI" to everything. Most of that pressure produces solutions in search of a problem — a chatbot nobody uses, a recommendation engine that recommends nothing useful, a slide deck about "AI strategy" that nobody acts on.

We approach AI projects backwards from how most consultancies do. We start with a problem you already have, then ask whether AI is actually the best tool for it.

What that looks like in practice

• Document processing: clients send you PDF invoices, contracts, or forms? An extraction pipeline can turn those into structured data in seconds, with human review only on the uncertain ones.
• Internal Q&A: your team has fifty Confluence pages and nobody can find what they need? RAG over those docs gives a working answer engine in a week.
• Classification and prioritization: support tickets, leads, applications — anywhere a human is currently triaging by reading, a model can usually prioritize for them.
• LLM-powered features: customer-facing writing assistants, summarization, draft generation — wrapped properly with usage limits and review.

What we'll tell you honestly

For some problems, a $5-a-month no-code tool already does what you need. For others, the answer is "this isn't actually a machine learning problem, it's a process problem." We'd rather save you the budget than build something that doesn't earn its keep.

Who this is for

Companies that have specific workflows where AI would help — not "we should do something with AI." Teams sitting on document or data archives they can't search well. Operations leaders whose people spend hours on tasks a model could do in seconds.