We are sceptical of AI built to be demoed and specific about the AI we ship. Each solution below replaces a task, is measured against your real data before launch, and keeps a person in the loop where a mistake would cost money.
Semantic search, a grounded assistant and per-audience content on the site you already have or the one we build.
The problem: Most site search matches keywords and most chatbots answer from nothing. Visitors leave, or they call to ask something the site already says. Meanwhile the content team rewrites the same page for three audiences by hand.
Products built around document understanding, conversation, classification and scoring — with the evaluation harness that keeps them stable.
The problem: Prototypes built in a weekend impress in a meeting and collapse on real data. Costs spike, answers drift, and there is no way to tell whether a prompt change made things better or worse.
Invoices, forms and transcripts read into structured data, with a human approving anything below the confidence bar.
The problem: Manual data entry costs a full-time salary per few hundred documents a month, introduces errors that surface at audit, and delays payments and reporting. Template-based OCR breaks the moment a supplier changes their layout.
Models connected to CRM, ERP, billing, WhatsApp and email through typed APIs, tool calling and queues.
The problem: Point solutions add another login and another silo. Integrations built quickly break on rate limits, fire webhooks twice, or leak data the model should never have seen.
Generative tools widen the option space early; the strongest direction is hand-refined into a design system.
The problem: Design timelines are consumed by producing variations rather than evaluating them. Teams settle for the second direction they saw because a fifth would take another week.
Domain assistants, pricing and risk models, vision checks and multilingual layers — with a success metric agreed first.
The problem: Custom AI work is where budgets disappear: open-ended scope, no agreed measure of success, and a demo that never becomes a system. A pilot with a metric fixes all three.
Tools change; the discipline around them is the part we keep.
Competitor and SERP teardowns, requirement drafts from messy client notes, user-story generation, and the edge cases we would otherwise find in week three.
Model and vendor choices are made per project on cost, latency, data residency and quality — never by default.
A 30-minute demo on your own use case: what we would build, how long it would take with AI in the loop, and what it would cost. No deck, no obligation.