The programming languages, frameworks, databases, and infrastructure that power the Glass platform. This is the technical detail — for the sales overview, see the main Glass page.
Each service picks the right language for the job — PHP for domain logic, Go for high-throughput, Python for AI pipelines.
| Service | Language / Framework | What It Does |
|---|---|---|
| CRU-Glass | PHP 8.3 · Vite · ES6 | Core monolith — 11 dashboards, ticket UI, admin, AI report builder |
| glass-core | PHP 8.3 · Slim 4 · JWT RS256 | Auth & identity — SSO/OIDC, MFA, 7-tier RBAC |
| glass-tickets | PHP 8.3 · Slim 4 | PSA/ITSM — lifecycle state machine, SLA sidecar, rules engine |
| glass-assets | PHP 8.3 · Slim 4 | CMDB — asset lifecycle, dynamic health scoring, warranties |
| glass-cyber | PHP 8.2 · Slim 4 · Wazuh | Security posture — Wazuh ingestion, sandboxed remediation |
| glass-rmm | PHP 8.3 · Slim 4 · MeshCentral | RMM — remote shell, patch management, hybrid deploy scripts |
| glass-email | PHP 8.2 · Slim 4 · EWS | Email — Exchange ingestion, AI triage & noise scoring |
| glass-unifi | PHP 8.2 · Slim 4 · Whisper | Telephony — UniFi Talk, voicemail → text via AI transcription |
| glass-chat | PHP 8.3 · React 18 · WebSocket | Real-time messaging — WebRTC voice/video, call transcription |
| glass-qbo | PHP 8.2 · Slim 4 · OAuth2 | Billing — QuickBooks PKCE, AES-256 tokens, invoicing |
| glass-superops | PHP 8.3 · Slim 4 · GraphQL | PSA sync — bidirectional SuperOps.ai with HMAC webhooks |
| glass-procurement | PHP 8.2 · Slim 4 | Procurement — purchase-order lifecycle, supplier catalog |
| glass-feedback | PHP 8.2 · Slim 4 | Bug tracking — AI developer-prompt generation, threaded notes |
| glass-crm | Go 1.25 · Chi · React SPA | CRM — campaigns, Redis Streams, HMAC webhooks |
| glass-sentinel | Go 1.22+ · Chi | XDR engine — Wazuh/Rspamd/ClamAV, multi-tenant security |
| glass-ai-worker | Python 3.11 · FastAPI · Pydantic | AI worker — Dual-LLM sandbox, pgvector RAG |
Highlighted rows = Go / Python (non-PHP)
Glass runs entirely on your own hardware — two workloads: the Docker platform (~29 containers) and local AI inference on a GPU or unified-memory machine.
| Workload | Minimum | Recommended | What CRU runs |
|---|---|---|---|
| Docker platform (no AI) | 8 cores · 16 GB · 256 GB SSD | 16 cores · 32 GB · 512 GB NVMe | 80 cores · 512 GB |
| AI inference (Ollama) | 16 GB VRAM / 32 GB unified | 48–64 GB VRAM / unified | 128 GB unified (Blackwell) |
| Combined (one machine) | 16 cores · 64 GB · GPU | 32 cores · 128 GB · GPU | DGX Spark + Supermicro |
Runs the full platform; AI swaps models between requests. No deep reasoning.
Full Dual-LLM + all AI features (except deep reasoning). Best for 5–10 techs.
Everything — all models resident, deep reasoning, headroom for future models. CRU's setup.