Users start with fragments, not specifications. Goals, pages, flows and design constraints are implied rather than explicit.
Intent Compiler
Base76 Research Lab
AI Product Architecture Layer
From vague ideas to executable AI specs.
Intent Compiler turns unstructured product intent into build briefs, structured JSON, and compressed execution prompts your AI stack can actually build from.
Less ambiguity
Reusable specifications
Token-efficient execution
Why Prompting Breaks
Prompting collapses when intent is still fuzzy.
AI output becomes unstable when users are forced to guess structure too early. Intent Compiler adds a deterministic layer between the idea and the generation.
The model fills the gaps inconsistently. Architecture drifts, UX intent mutates and implementation quality becomes fragile.
Teams burn cycles rewriting prompts and correcting assumptions instead of building from a stable brief.
Compiler Process
A product pipeline, not a one-shot prompt box.
Each step reduces uncertainty and increases operational clarity before AI execution begins.
Capture the initial direction without forcing technical language.
Extract likely goals, constraints and surfaces.
Pre-fill the form with editable recommendations.
Confirm or edit the extracted structure before compile.
Compile the final brief into executable AI artifacts.
Live Example Generator
Turn your idea into an executable spec.
Start with intent extraction. The system suggests form values first, then you can edit them before compiling the final artifacts.
Intent extraction
Suggested form values
AI-derived defaults from your intent. Review them, adjust anything that looks wrong, then compile.
Domain
website
Product
website
Goal
lead generation
Style
minimal
Color
neutral
Pages / features
home, about, contact
Audience
customers and stakeholders
Input example
"I want to build an AI research lab website"Start from plain language instead of inventing a polished prompt.
Intent extraction
{
"domain": "football team",
"product": "website",
"goal": "lead_generation",
"elements": ["team", "matches", "gallery", "contact"],
"style": "playful",
"color": "high_contrast"
}
Suggested form values
Domain: football team Product: website Goal: lead generation Pages: team, matches, gallery, contact Style: playful Color: high contrast
Generated spec
{
"product": "website",
"industry": "AI research lab",
"goal": "credibility + publishing",
"pages": ["home", "research", "projects", "contact"],
"style": "editorial technical"
}
Execution tokens
SITE| DOMAIN:FOOTBALL_TEAM| GOAL:LEAD_GENERATION| PAGES:TEAM,MATCHES,GALLERY,CONTACT| STYLE:PLAYFUL| COLOR:HIGH_CONTRAST
Token-efficient execution layer
Lower cost
More stable builds
Reusable prompts
Output As Product
What the compiler returns.
These are the actual live outputs from the current compile request.
AI Prompt
JSON Spec
Compressed Tokens
Token-efficient execution layer
Lower cost
More stable builds
Reusable prompts
Works With
Plug into your existing AI build workflow.
Generate structured prompts for your AI coding tools instead of starting from a blank chat box every time.
Claude
GPT
Cursor
Bolt
Lovable
v0
Method And Trust
Research framing should feel like part of the product.
01
Stabilize intent first
Clarify goals and constraints before AI execution rather than patching instability afterwards.
02
Make artifacts reusable
Store outputs as briefs, specs and compressed representations instead of ephemeral chat fragments.
03
Reduce variance in build systems
Give downstream models cleaner, smaller and more reproducible prompts to operate from.
Early Access
Turn your idea into a build spec.
Join early builders testing the workflow and shaping the next version of the compiler.