Problem-first

Find the painful job before the project.

Projects matter, but people usually arrive with a messy workflow, a launch risk, a trust gap, or a half-finished idea. These lanes keep the conversation grounded.

Before the build

AI workflow readiness

Find the workflows that are actually worth automating, the data that is not ready, and the human controls that need to stay in place.

Before taking money

Launch trust

Make sure project copy, payment flows, support promises, privacy claims, and readiness evidence all tell the same story.

Before the audit

AI governance evidence

Turn AI tool sprawl into inventory, ownership, policy starter packs, and buyer-readable evidence.

Before the checkout

Direct software sales

Package direct distribution, licensing, refund posture, release evidence, and customer support into one honest sales path.

Before the model talks

Regulated document workflows

Use rulepacks, citations, schema validation, and no-advice language where a broad chatbot would be too risky.

Before the cloud

Local-first operator tools

Build useful tools that work close to the user, with clear boundaries around what leaves the machine and what does not.