MetroMind
Enterprise AI Document Intelligence Platform
- Author
- Rounak Neema
- Category
- Language
- Status
Enterprise AI · Document Intelligence
STOP SEARCHING FOR FILES.
START QUERYING INTELLIGENCE.
Event-driven OCR → embedding → vector search, secured by RBAC and audited end-to-end.
▶ LIVE DOCUMENT PIPELINE
API_GATEWAY
→
AUTH
→
RABBITMQ
→
OCR
→
EMBED
→
VECTOR
→
AUDIT
// INGESTION PIPELINE TRACE
[08:42:11.001] INFO [API_GATEWAY] Received payload: Q3_Operations_Report.pdf (4.2MB)
[08:42:11.045] INFO [AUTH_SVC] Validating JWT signature... OK (Role: Ops_Lead)
[08:42:11.102] INFO [RABBITMQ] Published to exchange `document.process` [RoutingKey: ocr.start]
... worker node assumed ...
[08:42:12.441] INFO [OCR_WORKER] Extracted 12,408 words. Confidence: 98.4%
[08:42:13.015] INFO [EMBED_SVC] Generated 48 vector chunks via all-MiniLM-L6-v2
[08:42:13.155] INFO [VECTOR_DB] Indexed successfully to Milvus. Collection: operations_docs
[08:42:13.158] AUDIT [LOG_SVC] Ingestion complete. TraceID: 9f8a-4b2c
// LIVE EVENT STREAM
Structured Facts
- Built by
- Rounak Neema
- Languages
- Go & Python
- Architecture
- 12+ Microservices
- Features
- OCR & Vector Search
Go Microservices • Python OCR Workers • RabbitMQ • Milvus Vector DB • PostgreSQL • JWT + RBAC • Redis Caching • Go Microservices • Python OCR Workers • RabbitMQ • Milvus Vector DB • PostgreSQL • JWT + RBAC • Redis Caching
The Problem
Data is trapped in scanned PDFs. Security policies are ignored.
Managing and searching large volumes of transit documents across departments is inefficient. Standard SQL databases can't read scanned text, and basic full-text search doesn't understand context. Furthermore, sensitive inter-departmental documents require strict isolation and access tracking.
The Solution
Event-driven intelligence with cryptographic access controls.
- 01/ An automated OCR pipeline extracts text asynchronously.
- 02/ Text is chunked and embedded into semantic vectors.
- 03/ A high-performance API Gateway validates JWT and RBAC.
- 04/ Every retrieval is logged for compliance and telemetry.
Frequently Asked Questions
What is MetroMind?
MetroMind is an Enterprise AI Document Intelligence Platform that transforms scanned documents into a secure semantic search engine.
How does it process documents?
It uses an automated OCR pipeline to extract text asynchronously and chunk it for semantic vectors using embedding models.
What languages is MetroMind built in?
MetroMind is built using Go for microservices and Python for OCR workers.
Does MetroMind support vector search?
Yes, it uses Milvus Vector DB for high-performance semantic vector search capabilities.
How is access control managed?
Access is managed via JWT and strict RBAC microservices ensuring secure document retrieval.
How does MetroMind handle large volumes of documents?
It employs an event-driven architecture with RabbitMQ and 12+ microservices to process large workloads.
Who built MetroMind?
MetroMind was architected and built by Rounak Neema.