Architecture
Most AI builders have an LLM write code and hope it holds. Chromoly made a different bet: AI interprets what you want, and a deterministic engine builds it. This page explains the whole pipeline - prompt to production - and why it's built this way.
When an LLM writes application code directly, you inherit its failure modes in production: subtle differences between generations, code nobody reviews, security left to chance. That's fine for a prototype. It's how AI-built apps end up abandoned when they meet real data and real users.
Chromoly splits the job (deterministic AI generation, defined). AI does what it's genuinely good at - understanding a messy, human description of a business process. Everything after that is deterministic engineering: a compiler and a runtime that behave the same way every time. AI never improvises code into your production path.
You describe the system in plain language - optionally with a spreadsheet attached, whose columns and rows inform the data model. AI extracts the intent: what the system manages, who uses it, the process, what happens automatically. Ambiguity becomes follow-up questions or explicit assumptions you can react to.
The understanding is written down as a structured definition: entities, fields, relations, workflows, AI steps, screens, rules. This is the AppSpec. Everything downstream derives from it - the app, the API, the dependency map, the documentation. It's versioned on every change, and it's yours: export it any time.
A build engine turns the AppSpec into a running system: database schema, REST endpoints for every entity, screens, workflow definitions. Deterministic means the same AppSpec always produces the same system - regeneration can't silently change behavior, and there is no hand-written glue code to rot.
Systems run on Chromoly-managed infrastructure: an isolated, row-level-secured database per tenant, a workflow engine executing triggers and steps, transactional email and Slack outputs, inbound webhooks, and a per-build MCP endpoint so AI clients can operate the system under token scopes and rate limits. Live systems serve on your subdomain or custom domain.
AI also works inside your running system, as workflow steps: classify, extract, summarise, score, draft. These run on current frontier models with cost caps, retries, and provider fallback - and any step can be gated behind human approval. Low-confidence results route to a review queue instead of acting.
The one-sentence version: your prompt becomes a spec, the spec compiles into a system, and the system runs on managed rails - AI supplies judgment at the edges, never uncontrolled code in the middle.
This is where the architecture pays off. A change request ("add a budget field to projects and include it in the weekly digest") becomes a proposed diff against the AppSpec - not a patch to generated code.
Compare that to renaming a field in a hand-built automation stack: nothing warns you, three workflows break quietly, and you find out from an angry customer. That failure mode is the one this architecture exists to kill.
Lovable, Bolt, and similar tools have the LLM write the codebase. Fast to demo - but every regeneration is a new roll of the dice, security depends on what the model happened to write, and changes mean more generated code on top of old generated code. Chromoly regenerates from the spec, identically, every time.
Bubble, Glide, and Power Apps make you assemble the system by hand - the builder is deterministic, but you are the compiler. Weeks of dragging, and the dependency knowledge lives in your head. Chromoly generates the assembly and keeps the dependency map as data.
Zapier, Make, and n8n wire tools together but own no system of record and no dependency model - each zap is an island that fails alone, silently. Chromoly workflows live inside the system they operate on, monitored, logged, and mapped.
A freelancer's codebase is deterministic too - until they leave. The knowledge walks away; the maintenance never gets owned. Chromoly keeps the full system definition as structured, exportable data, and the platform is the maintainer.
Full comparisons: vs Lovable · vs Bubble · vs Retool · vs Zapier · vs n8n · vs Airtable · vs Power Apps · vs Make · vs Glide · vs Bolt
An honest framing first: in terms of what gets built, Chromoly is not unique - a code generator can produce a lookalike of almost any system on this site, and it can build plenty of things Chromoly deliberately doesn't. The uniqueness is in what the built thing can do. Five capabilities, each a direct consequence of the spec-first architecture:
The short version: anyone can build a copy of your system. Nobody else can promise it's the same system next month, show you what a change touches before it happens, or stay responsible for it running. The uniqueness lives in month three, not minute three.
Everything above ships wrapped in the operational layer most fast-build tools skip:
No. AI interprets your description into the AppSpec; the deterministic compiler builds the system from the AppSpec. AI also runs inside workflows as bounded steps with caps and approval gates. What it never does is improvise application code into production.
Entities and their fields, relations between them, workflows with triggers and steps (including AI steps), screens, and access rules. It's the complete description of your system as structured data - which is why it can be diffed, dependency-mapped, versioned, and exported.
The change becomes a diff against the AppSpec. Chromoly computes the blast radius from the dependency manifest, flags anything breaking, builds the result into preview, and deploys to production only on your approval - as a new version you can roll back from.
Yes. Records export as CSV/JSON, and the AppSpec - your system's full definition - exports too. Portability is a design requirement, not an afterthought.
Describe a system in plain language. Watch it become a spec, then a preview, then yours.
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