AIdeator

AIdeator: From raw idea to build decision.

AIdeator is a complete idea validation system that takes you from raw idea to build decision — with scored desk research, structured user interviews, and behavioral experiments. Stop overthinking. Start validating.

Architecture · Configuration · Quick Start · Security & Privacy · Issues

What is AIdeator?

AIdeator is a local-first idea validation engine that turns raw product ideas into structured validation reports with demand, competition, and risk scoring. It combines:

• LLM-powered analysis via Ollama (local), OpenAI, Anthropic, or Mistral
• Search-backed signals via built-in web fetch, Tavily, or Exa
• Benchmark scoring with 0–100 scores and percentile comparisons
• Privacy modes that control what data leaves your machine

Think of it as your AI research analyst for product ideas — run it locally with zero external calls, or connect cloud providers for deeper insights.

Key Features

Quick Start

# Clone and install
git clone https://github.com/ARCHITECTURA-AI/AIdeator
cd AIdeator
python -m venv .venv
source .venv/bin/activate  # Windows: .venv\Scripts\activate
pip install -e .

# Configure (interactive wizard)
aideator config init

# Start the server
aideator serve

Default URL: http://localhost:8000

Architecture

aideator/          # Core package: CLI, paths, LLM & search providers
├── llm/           # LLM provider abstraction (Ollama, OpenAI, Anthropic, Mistral)
├── search/        # Search providers (DuckDuckGo, SearXNG, Tavily, Exa, Builtin)
api/               # FastAPI app, routes, config, middleware
engine/            # Run orchestrator, card synthesizer, benchmark
db/                # In-memory repositories (ideas, runs, reports)
models/            # Data classes (Idea, Run, Report)
templates/         # Jinja2 HTML templates (dashboard, reports, settings)
docs/              # Generated markdown reports & project documentation

Detailed overview: docs/architecture.md

Search Providers

AIdeator supports a fully-tiered search abstraction, bringing context to the LLM's idea validation:

Configure via aideator config init or set SEARCH_PROVIDER env var.

LLM Providers

Scoring System

Every validation run produces cards with 0–100 scores and automatic bands:

Scores are compared against an internal benchmark corpus of 12 known SaaS products to provide percentile rankings.

Privacy Modes

Set via APP_DEFAULT_MODE=local|hybrid|cloud environment variable.

Docker

# Build and run
docker build -t your-org/aideator:latest .
docker run --rm -p 8000:8000 --env-file .env your-org/aideator:latest

# Or with compose
docker compose up

CLI Commands

Flags: --host, --port, --reload, --db, --docs

Development

pip install -e ".[dev]"
make lint      # Ruff linting
make test      # Pytest suite
make dev       # Dev server with reload

Seed test data:

python scripts/seed_example.py

Configuration

• Config file: aideator.toml (auto-created by config init)
• Environment variables: override config file values
• CLI flags: override everything

Key variables:

Full reference: docs/config.md

Security

Local-only mode blocks all outbound HTTP by run-mode guardrails. See docs/security-privacy.md for:

• Data residency guarantees per mode
• API key handling and rotation guidance
• Container hardening recommendations

Version History

License

MIT. See LICENSE.