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AtomBase builds AI products that automate knowledge work, starting with software development. We build on top of existing frontier models. We do not train our own. Money goes into product, context engineering, and distribution, not billion-dollar training runs.
At the center is the company-wide context graph and agent-reliability stack. GoShark is the wedge and revenue engine. Sibling products like AI Code Review and Project Management reuse the same shared layer; applied research keeps us on the capability frontier and feeds reliability back into every product.
The strategic point: the moat is the shared layer, not any one product. Each product makes that layer stronger; the stronger layer makes the next product faster to build.
The whole project is one coherent object. No feature ships that breaks the single-context principle.
Every artifact is versioned, patchable, and cascade-aware. Generate-once-and-forget is the competitor's weakness, not ours.
The definition-of-done is encoded as an automated eval before a module is built. Reliability is engineered, not hoped for.
Start from curated working scaffolds and let the model fill the gaps. More reliable than blank-page output.
Anything touching a customer's code is auditable, reversible, and human-gated. We sell safety into serious buyers.
Free and local models through development; paid frontier models only when orchestration and monetization justify it.
Claude is the recommended primary brain, but the provider is swappable behind an interface. Never hard-couple.
Company and strategy layer: brand, research, and the product portfolio.
Real, maturing monorepo. Spec stage live (Brief → Requirements → Analysis → Docs); remaining stages in progress on the same graph. Bootstrap stack, ~$0 recurring.
Solo founders and small teams (1–5 devs) building SaaS: the wedge, not the ceiling.
AtomBase exists because the tools we use to build software forgot the project. Every assistant sees a file or a ticket; none of them understand the whole thing: the requirement, the design it shaped, the code that implements it, the test that proves it, and the incident that reveals it failed.
I started AtomBase to close that gap with a single AI that understands the entire project, shipped first as GoShark. We build on frontier models rather than training our own, so everything we have goes into the one thing that compounds: a living, project-wide context graph.
If that mission resonates, whether you want to build with us, back us, or be among the first to use it, I'd like to hear from you.