AI GENERATION · CORE ENGINE

Three engines.
One application model.

AI generates the structure from a prompt. The platform deterministically emits production code. Developers extend in real Java, Python, or JavaScript. All three engines write to the same model.

01 / AI ENGINE

AI generation

Prompts shape the system. Tables, queries, pages, security profiles, business rules.

Probabilistic
02 / PLATFORM ENGINE

Platform generation

Each configuration emits code: CRUD, REST + GraphQL, RBAC, cache, observability — error-free, every time.

Deterministic
03 / DEVELOPER

Developer extension

Java, Python, or JavaScript per element. You write only the differentiating logic.

Human-authored
How generation works

Each engine has a job.
None of them overlap.

AI is good at intent. The platform is good at correctness. Developers are good at the 5% that's truly novel. Weezzi gives each one its lane.

"Build a marketplace" tables roles pages
01 · AI generation

Intent → structure

You describe what the system needs to do. The AI chooses what tables exist, what roles can do what, what pages connect, what business rules apply.

  • Natural-language prompts shape the application model
  • Pulls from Weezzi business patterns, not generic LLM output
  • Edits the same model that visual editing and code edit
MODEL CRUD REST GraphQL RBAC i18n Cache Auth Logs Trace
02 · Platform generation

Structure → real code

Once the model is set, the platform emits the same artifacts every time. Thousands of lines, error-free, idempotent. A decade of business software patterns, encoded.

  • CRUD, REST, GraphQL, OpenAPI from one schema
  • RBAC, multi-language, cache, dashboards, observability
  • Standard Java / Python / JavaScript output — no proprietary runtime
Order.java // generated by Weezzi public class Order { // custom logic public BigDecimal calcVAT() { return rules.apply(this); }
03 · Developer extension

Real code, only where it matters

The boilerplate is generated. You write the methods that differentiate your business — and only those. Standard languages, standard tooling, standard debugging.

  • Java natively · Python via GraalPy · JavaScript via GraalJS
  • Element-level language choice — pick per component
  • Full breakpoints in Java; console for Python & JS
What gets generated

What other platforms ask you
to build, Weezzi generates.

From a prompt, the system emits the layers a real production application needs — and the editing surfaces operators use after launch.

Application Model single source Schema Queries REST GraphQL Auth RBAC Cache Pay Media Logs i18n Mail Site Editor Backoffice A/B test Heatmaps Newsletter Loyalty CMS Dashboards

One model.
Four concentric layers.

The model in the center is the source of truth. Every layer outward is generated from it — and stays in sync as the model evolves.

Tables & entities model
Roles & profiles model
Workflows model
Pages & menus model
CRUD code code
REST & GraphQL code
Multi-language code
RBAC enforcement code
Auth & sessions service
Cache layer service
Observability service
Site Editor runtime
Dashboards runtime
A/B + heatmaps runtime
The application model

Three editors.
One model underneath.

AI edits, visual edits, and code edits all write to the same underlying model. There's no translation layer between them — and no merge conflicts.

AI ENGINE Prompts & intent "Add a customers table" AI BUILDER UI Visual editing drag · drop · configure DEVELOPER Real code java · python · javascript { } APPLICATION MODEL · single source of truth Tables 12 entities 38 fields Roles 4 profiles 36 perms Workflows 7 flows on-event Pages 14 pages 41 components Queries 22 queries + slices Rules business + validation i18n multi-lang dictionary
// every actor reads from and writes to the same canonical model
Deterministic vs probabilistic

The AI gets to be creative.
The platform doesn't.

Two engines, two contracts. Probabilistic where exploration helps. Deterministic where the same input must produce the same output, every time.

Probabilistic · AI engine

Same prompt. Different valid solutions.

The AI explores the design space. "Build a marketplace" can become several reasonable shapes — different table choices, different role splits. You pick the one you want and refine.

Run 1
12 tables
Run 2
10 tables
Run 3
14 tables
// variation is a feature, not a bug
Deterministic · Platform engine

Same model. Always the same code.

Once the model is set, generation is a pure function. Nothing is freestyled. No hallucinated method names, no inconsistent error handling, no "creative" SQL. Production code stays predictable across every run.

Run 1
2,847 LoC
Run 2
2,847 LoC
Run 3
2,847 LoC
// idempotent · diffable · review-friendly
Generation quality

Trained on a real platform.
Not on a generic LLM.

Generic code models hallucinate API surface and invent inconsistent patterns. Weezzi's generation is grounded in 20+ years of business application architecture, encoded as patterns the AI can actually emit.

17.5M Lines trained on

Real production code from a SaaS serving hundreds of businesses.

20+ yrs Platform heritage

Battle-tested patterns for schemas, roles, workflows, and pages.

20+ Business modules

HR, projects, invoices, ecommerce, content, services, more.

0 Hallucinated APIs

Generated code targets a closed surface — the platform's own.

Quality comes from the boundary.

The AI doesn't generate raw application code from scratch. It edits a model. The platform — deterministic, audited, versioned — is what produces the final code.

  • No hallucinated method names. Code emission is a pure function of the model.
  • No inconsistent patterns. CRUD, auth, RBAC, i18n always emit the same shape.
  • Patterns from real businesses. HR, projects, invoices, services, ecommerce — encoded as reusable shapes.
  • The tutorial corpus stays in sync. Generated docs match generated code by construction.
Generate yours

Skip the wiring.
Generate the system.

Pilot Q2 2026. Founders, agencies, and one mid-market design partner per cohort. No credit card.