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Nexus — Integrated Telecom Operations Platform
A private, multi-service platform for telecom operations combining CRM workflows, knowledge management, network visibility, and operational automation.
Evidence note: High-level summary only. Verified via public profile description and repository documentation; confidential operational details omitted.
Context
Active internal work at ATI Internet. Nexus is described here at a high level based on verified profile and repository documentation; source code and operational details are private.
Problem
Telecom operations involved fragmented workflows across customer service, commercial management, network data, and internal documentation, creating manual effort and inconsistent operational visibility.
Constraints
- Must integrate with existing operational systems and processes rather than replace them all at once.
- Operational data handling requires care: workflows should avoid exposing personal or sensitive customer information.
- Deployment and changes need to be reliable in a real production environment.
Architecture
- Web CRM built with Next.js and React for operational, commercial, and metrics workflows.
- Python services (including the Fibrinha bot) for automation, data collection, and integrations.
- PostgreSQL as the primary data store with schema-driven data access.
- Versioned inter-service contracts (JSON schemas/samples) to formalize communication boundaries.
- Docker-based deployment with CI/CD automation and self-hosted runners for production delivery.
- Chrome extension components to connect browser workflows with internal systems where appropriate.
Key decisions
- Adopted a contract-first approach for inter-service communication to reduce integration drift.
- Separated concerns across web, bot, extension, and infrastructure modules within a monorepo.
- Used role-based access control and documentation gates to support governance and maintainability.
- Prioritized aggregated operational telemetry over personal data in integrations.
Implementation
- Implemented modules for commercial workflows, metrics/market views, CS/NOC inventory visibility, task tracking, and an integrated knowledge base.
- Built automation and ingestion flows that push structured, aggregated data into the platform.
- Established CI checks, deployment workflows, and operational documentation practices.
Challenges
- Evolving architecture while the platform is actively used in operations.
- Bridging legacy and modern systems without disrupting day-to-day work.
- Balancing delivery speed with reliability, access control, and maintainability.
Outcomes (verified)
- A unified internal platform direction that brings multiple operational domains into one system.
- Documented engineering practices (contracts, CI/CD, modular structure) supporting ongoing development.
- Specific business metrics are not publicly disclosed.
Lessons learned
- Clear contracts and boundaries make distributed operational systems easier to evolve.
- Automation delivers the most value when it fits existing human workflows instead of fighting them.
- Documentation and governance are part of the system, not an afterthought.
Stack
- Next.js
- React
- TypeScript
- Python
- PostgreSQL
- Drizzle ORM
- Docker
- GitHub Actions
- Zod