Our entire delivery process is three steps: design around the model, build the full stack, deploy and keep owning it. No discovery phase that bills for a month of meetings, no handoff to a separate dev shop. The same small team that sketches the first screen ships the production build.
Process pages are usually where agencies hide. Six phases, a diagram with arrows, and the word "synergy". We'd rather show you what each step actually produces, using artefacts from our own products (Snapeto and AiFlow, both ours ). If you're evaluating any studio, ask them for the equivalent.
Step one: design, starting from the model
Most product processes start with screens. Ours starts with a capability test: before any UI exists, we test what the model can reliably do with real inputs. For Snapeto, that meant feeding vision models actual photos of messy, badly lit fridges before designing a single screen. The results set the product's boundaries.
Why this order? Because an interface designed before the capability test is fiction. If the model can't reliably read jar labels in the back row, the design has to handle that gracefully, and it's far cheaper to know on day two than in week eight.
- What this step produces: a capability map (what the model can do, at what confidence, at what cost per call), user flows built on that map, and high fidelity screens
- What it deliberately doesn't produce: a 40 page strategy deck. The strategy is in the flows
Step two: build, the whole stack
We build front to back: the interface, the API layer, the model orchestration, the data store. One team, one codebase narrative. On Snapeto that's a web app, a mobile build and a Python backend talking to the models; on AiFlow it's a node canvas where every node is a generation step with its own cost.
The part most builds skip, and the part we consider the actual job: engineering for the model being wrong. Confidence thresholds. Fallback paths. Retries that don't silently burn money. Caching so the same question never costs twice. In our builds this failure handling layer routinely takes as long as the happy path, and it's why the product survives contact with real users.
Step three: deploy, and keep owning it
Deployment isn't a handover ceremony. We ship to production infrastructure (our own site runs on Cloudflare's edge, our prototypes have shipped on Netlify and Vercel), wire up the boring essentials (analytics, error reporting, cost monitoring on every model call) and then stay accountable for the thing running.
Ai native products need this more than ordinary software, for one unglamorous reason: models change underneath you. Providers deprecate versions, prices move, behaviour shifts. Someone has to own that drift. We'd rather it be the team that built the product than your inbox.
Why three steps and not six
Because every extra phase is a seam, and seams are where products leak. The classic agency pipeline (strategy team hands to design team hands to dev team hands to QA) loses information at every handoff. A small senior team that carries the product end to end doesn't have that loss. It's also faster, and speed matters when the model landscape moves monthly.
The honest tradeoff: this model doesn't scale to fifty simultaneous clients, and we don't want it to. It scales to a small number of products built properly. If you want a body shop, we're the wrong call. If you want the team that treats AI as the engine, not the garnish, that's literally our thesis.
Frequently asked questions
How long does each step take?
Design typically runs one to three weeks depending on how many capability tests the product needs. Build is the long pole, usually six to twelve weeks for a v1. Deploy is days, but the ownership after it is ongoing.
Do you work with an existing product or only from scratch?
Both. For existing products we start with the same capability test, applied to your real data, and an honest assessment of whether your core loop should run through a model at all. Sometimes the answer is no, and we'll say so.
Who owns the code?
You do, fully. Nothing locks you to a platform, no credit meter, no proprietary runtime. That ownership difference is most visible when you compare custom builds against app builders that run on credits.
What stack do you use?
Typescript and Next.js on the front, Python (FastAPI) for model orchestration, deployed on edge platforms like Cloudflare. Model choice is per product: we pick per capability and cost, not per fashion.
© 2026 Dinimiciuil Labs. All rights reserved. Written on the build floor in Dublin. You are welcome to quote a short excerpt with a link back; please do not republish the full article without permission.