Most production AI work is not hard. It is classification, extraction, routing and rewriting, at volume, against a fixed schema. Sending that to a frontier model is the most common expensive default in the field, and it is often a short piece of work to fix.
All Model Families →
Provider-published specifications as of 1 September 2026, not figures we measured. Model families move quickly — check the provider's own documentation before committing to a number.
Roughly, anything from one to fourteen billion parameters — the range that fits on a single accelerator, often with room to spare, and in the lower half runs acceptably on a CPU. Every major family ships in this class: Llama, Qwen, Mistral, Gemma, Phi and the distilled variants of larger reasoning models.
The interesting property is not that they are cheap, though they are, by one to two orders of magnitude per token against a frontier API. It is that they are local. A model that fits on one GPU can run inside your building, at the edge, on a laptop, or in a container next to the service that calls it — with no network hop, no per-token bill and no third party in the data path. That changes what is architecturally possible, not just what it costs.
The practical architecture that follows is a ladder, not a choice: a small model handles the bulk, a gate detects the requests it should not have taken, and those escalate. Building that gate well is the actual engineering, and it is covered on hybrid model routing.
You build an evaluation set from real examples of that task — a few hundred is usually enough to be decisive — and run both. It is a day or two of work and it settles the question with evidence instead of instinct. In our experience, more of the workload typically moves down than teams expect.
Often not. A well-designed prompt plus a few examples closes most of the gap on a bounded task. Fine-tune when you have a stable task, a few thousand labelled examples, and a measured gap that prompting has not closed — in that order.
A single accelerator will serve a small model to a substantial number of concurrent users, and the smallest run on CPU for batch work. The cost conversation moves from per-token to per-hour, which is exactly what makes high-volume workloads viable — the bill stops scaling with usage.
Cost is the visible benefit and rarely the important one. The important one is that a model small enough to run on your own hardware removes the third party from the data path entirely, which removes the third-party-processor part of the governance conversation.
We measure which request classes a small model handles identically, then build the gate for the ones it does not.
30-minute technical call · no deck
What we built, what stage it is at, and a link to the live thing wherever there is one.
yourOS is an operating system with an AI at its heart. You talk to it like a person, out loud or typed. It makes your calls, plans your days and works on your projects with a crew of agents, on your own private server. Our own product, built from the first commit; the waitlist is open.
Website, backend and app assets for Sonora, a free AI sound-therapy app on iOS and Android published by Cognitive AI.
Brand, website, prototype, apps on five platforms, the AI backend and the algorithm-intelligence pipeline for Podswap, a social growth engine that runs without a human in the loop.
In closed beta: Roxy, a private AI assistant with permanent memory running on infrastructure the user owns. Brand, site, product, backend and conversational design.
Live at coherence.com: brand identity generated with AI agents we directed, the motion system and website for the group behind Sonora, AboutAI, WakeUp and MudShadows. Agent work remains in build.
Live at domainbooth.com for DomainBooth, a premium domain brokerage: a new animated mark, every page redesigned and rebuilt with designed mobile layouts, and a custom CMS for its sales record, featured names, press and enquiries.
Apps, platforms and campaigns for these teams, long before the AI work. Every one is written up on the work page — what we did, and where our part ended.
