Docs: Echo Core | Oceanic AI

Technical Summary

Echo Core is a production-grade RAG (Retrieval-Augmented Generation) platform for enterprise document processing, semantic search, and intelligent retrieval. It handles massive document ingestion pipelines, provides configurable chunking strategies, and delivers sub-2-second query responses at scale with multi-tenant isolation and advanced security controls.

Core Capabilities

Massive Document Processing: Ingest 10,000+ PDFs with parallel processing and automatic chunking optimization
Hybrid Retrieval: Combines dense vector search with sparse BM25 for optimal relevance (MRR ≥ 0.80)
Multi-Modal Support: Process text, tables, images, and structured data from diverse document formats
Pipeline Studio: Visual configuration of ingestion, chunking, embedding, and retrieval workflows
Enterprise Security: Row-level security, SOC 2 compliance, audit logging, and BYOC deployment

Performance Benchmarks

Dataset Size	Ingestion Time	Query Latency (p95)	MRR
1,000 documents	10 minutes	< 800ms	0.82
10,000 documents	90 minutes	< 1.2s	0.84
100,000 documents	12 hours	< 2s	0.86

Architecture Overview

┌─────────────────────────────────────────────────────────┐
│              ECHO CORE RAG STUDIO                       │
├─────────────────────────────────────────────────────────┤
│                                                         │
│  ┌──────────┐  ┌──────────┐  ┌──────────┐             │
│  │Document  │  │ Chunking │  │Embedding │             │
│  │Ingestion │  │  Engine  │  │  Layer   │             │
│  └────┬─────┘  └────┬─────┘  └────┬─────┘             │
│       │             │             │                     │
│       └─────────────┼─────────────┘                     │
│                     │                                   │
│  ┌──────────┐  ┌────▼─────┐  ┌──────────┐             │
│  │  Vector  │  │  Hybrid  │  │Pipeline  │             │
│  │  Store   │  │ Retrieval│  │ Studio   │             │
│  └──────────┘  └──────────┘  └──────────┘             │
│                                                         │
└─────────────────────────────────────────────────────────┘
                       ▲
                       │
        ┌──────────────┴──────────────┐
        │    STORAGE LAYER            │
        ├─────────────────────────────┤
        │ • Qdrant (vectors)          │
        │ • PostgreSQL (metadata)     │
        │ • S3/GCS (documents)        │
        │ • Redis (cache)             │
        └─────────────────────────────┘

Core Components

Document Ingestion

Parallel processing of diverse document formats

• PDF, DOCX, XLSX, TXT, MD, HTML
• OCR for scanned documents
• Table extraction (Tabula)
• 100 docs/minute throughput

Chunking Engine

Intelligent text segmentation strategies

• Semantic chunking
• Fixed-size with overlap
• Section-aware splitting
• Configurable boundaries

Embedding Layer

Multi-model embedding generation

• OpenAI text-embedding-3
• Cohere embed-v3
• Voyage AI embeddings
• Batch processing optimization

Vector Store

High-performance similarity search

• Qdrant (primary)
• HNSW indexing
• Recall ≥ 0.92 @ top-10
• Multi-tenant collections

Hybrid Retrieval

Combined dense and sparse search

• Dense: vector similarity
• Sparse: BM25 keyword
• Reciprocal Rank Fusion
• Configurable weights

Pipeline Studio

Visual workflow configuration interface

• Drag-and-drop builder
• Template library
• A/B testing support
• Performance monitoring

Technology Stack

Backend

• FastAPI (Python 3.11+)
• LangChain framework
• Celery + RabbitMQ
• PyPDF, python-docx
• Unstructured.io

Storage

• Qdrant (vectors)
• PostgreSQL + pgvector
• S3/GCS (documents)
• Redis (caching)
• Elasticsearch (optional)

Observability

• Prometheus metrics
• Grafana dashboards
• OpenTelemetry tracing
• Sentry error tracking
• Loki log aggregation

Echo Core RAG Studio Platform