The Challenge
The client’s existing Laravel-based platform relied on manual triggering to start the AI matching process, which caused inefficiencies and deployment delays. The main challenge was to integrate a Python-based semantic search algorithm into the Laravel environment using Laravel Forge, bridging two different technology stacks (Python and PHP) through a secure SSH connection. Additionally, the project had to be completed under a tight two-week deadline, requiring precise coordination, automation setup, and zero downtime deployment.
Project Overview
Industry
Technology / SaaS
Location
Europe
Timeline
2 weeks (rapid delivery)
Status
Our Solution
Cognivox Labs implemented a secure, maintainable integration connecting the Python semantic search (FastAPR) to the Laravel application and production environment. Key elements of the solution:
FastAPR (Python) integration
Integrated the FastAPR semantic search algorithm as a separate Python microservice and exposed endpoints to the Laravel app for scoring and matching.
Secure connection (SSH tunnel)
Established a secure SSH tunnel between the Laravel Forge server and the Python service to enable stable, authenticated inter-service communication.
Background workers & daemon
Implemented a dedicated daemon and configured Laravel Queue Workers for asynchronous processing, ensuring resilient background matching and high throughput.
Automatic & manual triggers
Automatic matching trigger when a user creates a new “need”, plus a manual override for backend administrators to re-run or debug matches on demand.
API endpoints & documentation
Exposed RESTful endpoints for frontend consumption and documented the matching function and deployment steps for maintainability and handoffs.
CI/CD & Forge deployment
Integrated with Laravel Forge and CI/CD to enable automated, zero-downtime deployments and consistent production configuration.
Technologies Used
Results & Impact
99.9%
Uptime achieved
60%
Faster deployments
Long-Term Vision
- Automated Triggers: Fully autonomous matching whenever needs are created, with retry and backoff logic.
- Real-time Updates: Live matching status and results surfaced to users via REST endpoints or websockets.
- Advanced Analytics: Continuous monitoring of matching performance, precision/recall metrics and operational telemetry.
- Extensibility: Clear documentation and modular microservice boundaries to support future model swaps or scaling.