A delivery stack for real-world AI.

We run a structured discovery sprint that maps your process landscape, identifies where AI creates real leverage (not just where it sounds plausible), and produces a prioritised use-case shortlist you can act on. The output is a specific, sequenced roadmap — not a slide deck of possibilities.

Most teams skip this step and build the wrong thing. A two-week strategy sprint costs a fraction of a misallocated three-month build, and it tells you whether to proceed at all.

2-week sprint → written output → optional build handoff

We map the process, identify the automation boundary (what AI handles vs what stays with humans), and build a system that runs reliably in your operational environment. This includes AI decision-support layers that help your team move faster without removing human judgement where it matters.

Automation works best when it's designed around the failure modes, not just the happy path. We build with exception handling, audit trails, and escalation logic from the start — so the system handles edge cases without creating new manual work.

3–6 week build → deployment → optional operations retainer

We scope, design, and build — full-stack web, iOS, and Android. We focus the first version on the core workflow that validates your hypothesis, with instrumentation to measure what's working before adding the next layer. No feature creep, no scope inflation.

A six-week MVP isn't a corner-cutting exercise. It's a forcing function on clarity — if you can't describe the three things the first version must do, you're not ready to build. We'll tell you when you are.

Scoping week + 5-week build → production launch

We design and build integration layers that connect your stack into one coherent, maintainable system. APIs, webhooks, data pipelines, auth and access controls — built to be observable, resilient to partial failures, and easy to extend when your stack changes.

Most integration failures aren't about the APIs — they're about the assumptions baked into the glue code that nobody documents. We make integration logic explicit, testable, and visible.

1-week design + build sprint → integration testing → deployment

We implement observability across your system: accuracy tracking, latency and cost monitoring, user adoption analytics, and failure mode logging. Then we run structured iteration cycles — reviewing what the data shows and making targeted improvements on a cadence that fits your team.

AI systems don't degrade loudly. Accuracy drops a few percentage points, edge cases accumulate, and by the time someone notices, the damage is done. Early monitoring prevents the expensive rebuilds.

Setup sprint (1 week) → ongoing monthly retainer or one-off audit