AI-Powered App Compiler
Turn Your Ideas Into
Production-Ready Apps
Describe what you want to build in plain English. Our multi-stage AI pipeline converts your description into a complete, validated application specification — database schema, API endpoints, auth rules, integrations, and workflows — all in seconds.
Scroll down to explore the architecture, features & sidebar panels
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How It Works
Your Idea
Describe your app in plain English
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Prompt Guard
Blocks gibberish, scribbles & nonsense inputs
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Stage 1: Intent
Extracts app type, features, entities & integrations
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Validate
Zod contract check
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Stage 2: Schema
Generates tenant-scoped database with relations
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Validate
Tenant check + relation graph
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Stage 3: AppSpec
Pages, APIs, auth, hooks & workflows
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Validate
Cross-reference consistency
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App Spec
Machine-readable, validated output
🔧 Auto-Repair EngineIf any stage fails validation, the targeted repair engine fixes only the broken parts (up to 3 attempts) — never re-generates the full prompt
Everything Built In
Multi-Stage Pipeline
Not a single LLM call. Each stage (Intent → Schema → AppSpec) gets its own specialized prompt, validated independently. Output of each stage feeds the next.
Prompt Quality Guard
Random scribbles like "asdjfkl" or empty inputs are blocked before they reach the AI. Only meaningful product descriptions pass through to the pipeline.
Validation After Every Stage
Every stage output is validated against strict Zod schemas. tenantId checks, bidirectional relations, cross-entity references — all enforced automatically.
Targeted Repair Engine
When validation fails, the repair engine sends only the broken fields back to the LLM — not the full prompt. Up to 3 targeted repair attempts with deterministic fallback.
14 Integrations with Real Schemas
Slack, WhatsApp, Stripe, Gmail, Salesforce, HubSpot, Notion, Airtable, Jira, GitHub, Zapier, Google Sheets, Twilio SMS, Webhooks — each with typed input/output schemas.
Workflow Generation
Integration hooks and workflow stubs are generated automatically based on your description. Trigger → Condition → Action flows with field mappings.
Config-Driven Model Routing
No hardcoded model names. A central routing config picks Gemini, Groq, OpenAI, Anthropic, or DeepSeek per stage with cost budgets and latency targets.
Cost Tracking & Budget Caps
Every LLM call tracks input/output tokens and estimated USD. Per-stage cost budgets prevent runaway spending. Total cost visible in real-time.
Real-Time SSE Streaming
Watch the pipeline execute live. Server-Sent Events stream stage starts, completions, failures, and repair attempts directly to your browser.
Persistent Run History
Every pipeline run is recorded in the database — prompts, outputs, costs, repair logs, events. Review past runs to evaluate how well the system performs over time.
Evaluation & Benchmarking
Run the evaluation script against real prompts to measure pass rates, repair frequency, and cost. Results are stored and surfaced in the Reports panel.
Deterministic Fallback
If all AI providers fail or are unconfigured, the pipeline still works using a deterministic code path — keyword-based intent extraction, template schemas, and rule-based AppSpec.
Explore each section using the sidebar navigation. Every feature has its own dedicated panel with detailed output, visualizations, and inspection tools.