From schema to running backend.
Auxen turns a database schema into deployable, production-shaped code across eleven frameworks. Claude drafts the schema, holds the architecture across dozens of files, and explains every decision it makes.
What we walked into.
Every enterprise team has the same first-week problem. Someone has an idea and sketches a schema on a whiteboard. Then a good engineer spends five days translating that schema into a project skeleton, wiring authentication, laying down migrations, writing the first controllers and tests, and stitching it into the deploy pipeline. Five days of work before the interesting work can begin, multiplied by every new service, every new client, every new proof of concept.
Existing scaffolding tools produce hollow starter code. Existing code assistants help write a single function. Nothing bridged a schema definition to a full, production-shaped backend without losing the architectural coherence.
What we built.
Describe it, Claude drafts it
A developer describes what they are building. Claude drafts the schema, the relationships, the entity model, and the aggregate boundaries. Then Auxen generates the backend in whichever framework the team already lives in: Java Spring Boot, NestJS, FastAPI, Django, Go, and six others.
Production-shaped, not scaffolding
The output is production-shaped code with authentication wired in, migrations aligned to the schema, tests generated for the entity paths, and a repository the team can push and deploy. The first pass that used to take a good engineer a full week now lands in the time it takes to read Claude's explanation of what it wrote.
Why Claude makes it work
Three capabilities carry the product. Structured extraction that survives messy real-world briefs, so a founder's paragraph and a CTO's spec produce the same coherent schema. Long-context reasoning that holds an architectural decision across dozens of files, so an aggregate boundary chosen on file one is still respected on file forty. And the ability to explain, in a paragraph, why it chose an aggregate root over a foreign key, which turns Auxen from a code generator into a learning tool.
Private by deployment
The private deployment path runs Claude through AWS Bedrock, which unlocks the customers who cannot send schemas through a public AI tool for compliance reasons.
“It felt like Claude understood what we meant, not just what we typed.”
Claude, and the frameworks it writes
The result.
Auxen is live with a small cohort of enterprise pilot teams across financial services and healthcare. The feedback we hear most often is the same phrase in different mouths: it felt like Claude understood what we meant, not just what we typed.
That is the win, and Claude is what makes it possible. Structured extraction that does not lose the plot, long-context reasoning that keeps the architecture coherent, and output that explains itself as it lands.
A companion that remembers.
Merkx is a live AI companion built so that memory is the product and conversation is only the surface. Claude sits at the memory, response, critique, and personality layers that make it feel like a presence.
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Tell us what you're trying to ship.
A 30-minute scoping call with the engineers who would do the work.