Irtiqa Intelligence

Irtiqa Intelligence is a production-grade lead intelligence platform designed around a FastAPI backend, SQLAlchemy data layer, SQLite-first storage, and a future PostgreSQL migration path.

Current Scope

The repository currently contains:

• SQLAlchemy ORM models.
• Alembic migrations.
• SQLite database configuration.
• Session management.
• Repository pattern.
• Architecture documentation.

Agent, API, scraping, and frontend implementation have not started yet.

Architecture

The current implemented schema contains eight core tables:

• companies
• contacts
• websites
• technologies
• intent_signals
• intelligence_scores
• outreach_messages
• agent_runs

Documentation lives in:

• docs/database.md
• docs/agents.md
• docs/workflows.md

Setup

Create a virtual environment and install dependencies:

python -m venv .venv
.venv\Scripts\activate
pip install -e .[dev]

For PostgreSQL support later:

pip install -e .[postgres]

Configuration

Copy .env.example to .env and adjust values as needed.

Default SQLite URL:

sqlite:///database/irtiqa.db

Database Migrations

Run migrations:

python -m alembic upgrade head

Check for schema drift:

python -m alembic check

Repository Boundaries

Repositories encapsulate database reads and writes for ORM entities. They do not own transaction boundaries. Use session_scope() or a higher-level service/workflow transaction boundary to commit or roll back work.

Development Rules

• Do not commit generated SQLite databases.
• Do not commit __pycache__ directories.
• Do not create mock production data.
• Keep architecture modular and SQLAlchemy models PostgreSQL-compatible.
• Keep agent implementation separate from database and repository layers.