Applied AI that survives contact with operations

Most AI projects fail in the same place. The model works in a demo. The workflow around it does not. Support still lives in three inboxes. Documents still arrive as PDFs in a shared drive. Onboarding still depends on one person who remembers the exceptions.

We do not sell a chatbot bolted onto that mess. We sell a measurable operational outcome, owned by the same senior team that redesigns the workflow and ships the software.

A tool in someone’s hands teaches us more in a week than a quarter of planning.

Workflow first

The first question is never which model. It is where work actually gets stuck: the handoff that drops, the document that has to be re-typed, the lead that sits for two days, the search that sends someone to Slack instead of a source of truth.

We map that path, cut the steps that do not earn their place, and only then build the system around it. Support triage, document processing, onboarding, and internal search are the surfaces we take on most often, not because they are fashionable, because they are where a working system shows up in a number a team already watches.

What we actually ship

Production software, wired into the tools the team already uses. A defined clock. A written scope before a larger build begins. The people researching, designing, and engineering the product stay in the room. No account-manager relay.

• An AI workflow in 21 days: one operational path in production, tied to a number the team already watches.
• A full MVP in 30 days: a custom web or mobile slice users can complete, at the same bar as Talise and Maren.
• Fast tickets when the job is a live page this week: a business site, a shop, a booking page, or a WhatsApp enquiry path.

Those clocks are short on purpose. We would rather put working software in front of real people and let their behaviour set the next week than coordinate a quarter around a deck.

The same bar as our own products

Talise and Maren are how we hold ourselves to this. Both started as hard product questions (can stablecoins feel like messaging, can Bitcoin become spendable without a sale) and both had to survive contact with real users, real rails, and honest constraints.

The client work uses the same stack of judgment. Interface is engineering. Edge cases get designed, not patched. Copy tells the truth about what the system will and will not do. If we cannot name the result, we will not start the build.

Bring the outcome you need. We will come back with the smallest useful next step, a question, or an honest no.