We build custom AI systems, so treat our answer with appropriate suspicion — which is exactly why this page leads with the case for buying.
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If an existing product does 80% of what you need, buy it. A team of engineers has spent years on the edge cases you have not thought of yet, it works on Monday, and someone else carries the maintenance.
The instinct to build is usually driven by the last 20% — a workflow that does not quite fit, a field that is missing, an integration that is not there. That gap feels intolerable in an evaluation and is frequently tolerable in practice. Adapting your process to a good product is often much cheaper than building software to preserve a process that was itself an accident.
A useful discipline: before commissioning custom work, run the off-the-shelf option for a month with real users. A meaningful share of the "we need custom AI" conversations we have end there, and we would rather they ended there quickly.
| Criterion | Buy | Build |
|---|---|---|
| Time to first value | Days to weeks | 2 weeks to 6 months |
| Upfront cost | Low | Significant |
| Ongoing cost | Per seat or per use, forever | Hosting plus maintenance |
| Fit to your process | Adapt to the product | Exact |
| Uses your proprietary data | Limited | Fully |
| Differentiation | None — competitors buy it too | Yours |
| Maintenance burden | Vendor | You or a partner |
| Roadmap control | Vendor decides | You decide |
| Data residency options | Whatever they offer | Whatever you need |
| Risk if it fails | Switch vendors | Sunk cost |
The framing is a false binary more often than not. The strongest pattern we see is buying the commodity layer and building only the differentiated part: use a hosted model rather than training one, a managed vector database rather than operating a cluster, an off-the-shelf CRM rather than writing one — and build the retrieval, evaluation and workflow logic that is specific to you.
That keeps the custom surface small, which keeps it maintainable. A common failure is building everything because one part genuinely needed building; the resulting system is large, and its size is what eventually kills it.
With your own data and your own evaluation set, not the vendor demo. Ask for a trial on real inputs, including the hard ones. Ask how quality is measured and what happens when it is wrong. Vendor demos are constructed from cases that work, which is reasonable of them and useless to you.
That is a perfectly good outcome — you got value immediately and learned what you actually need, which is worth a lot when you do build. The thing to protect is data portability: make sure you can export your data and any accumulated labels or corrections, because those are the asset.
Much less than it was. Foundation models removed the need to train from scratch, so the work is engineering around a model rather than machine-learning research. A focused system that would have been a year and a research team in 2019 can be six to twelve weeks now. Ongoing inference cost is the newer consideration, and it is one to model early.
We do, regularly, and it is not altruism — an engagement we take on that should have been an off-the-shelf purchase ends badly for both of us and produces no reference. If a product does the job, saying so on the first call is the cheapest way to earn a call about something we should genuinely build.
Describe the problem and we will tell you honestly — including when the answer is a product you can buy this afternoon.
30-minute 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.
