Overview
A European SaaS provider managing internal and client documentation required an enterprise-grade AI retrieval engine. Their existing system relied on conventional keyword search, which failed to return relevant results across hybrid data formats. Each enterprise tenant stored thousands of files in different formats, creating fragmented information silos. The client needed a single RAG-based platform capable of integrating text documents, tabular datasets, and structured relational records while ensuring complete isolation of customer data.
Client Context
A European SaaS provider managing internal and client documentation required an enterprise-grade AI retrieval engine. Their existing system relied on conventional keyword search, which failed to return relevant results across hybrid data formats. Each enterprise tenant stored thousands of files in different formats, creating fragmented information silos. The client needed a single RAG-based platform capable of integrating text documents, tabular datasets, and structured relational records while ensuring complete isolation of customer data.
Core Challenges
Data Fragmentation
Information existed across relational databases, PDFs, Word files, and CSV datasets. Traditional retrieval pipelines were unable to bridge structured and unstructured sources, leading to incomplete context generation and low-accuracy responses.
Tenant Isolation and Compliance
The system had to support multi-tenant deployment with full encryption and namespace separation. Each tenant’s vector store and relational data had to remain completely segregated to meet GDPR and ISO-27001 compliance standards.
Latency and Cost Control
Handling millions of documents per tenant led to significant embedding and inference costs. Existing solutions showed high token consumption and query latency exceeding acceptable enterprise thresholds. The new platform required cost-efficient retrieval with predictable performance at scale.
Solution Overview
Hybrid Retrieval Pipeline
The core retrieval layer merged semantic vector matching with relational joins. Qdrant handled vector embeddings for text, while PostgreSQL managed structured fields. A middleware orchestrator coordinated both layers to deliver a single, ranked response context.
Security and Multi-Tenancy
Each tenant operated in a separate namespace with AES-256 encryption at rest and mutual TLS in transit. Role-based access (RBAC) was enforced through Azure AD SSO integration, maintaining strict authentication and access hierarchy.
Optimization and Cost Efficiency
Redis caching and query thresholding reduced redundant embedding requests. Cohere re-ranking optimized retrieval precision, cutting inference costs while preserving contextual accuracy.
Solution Description
Process Flow
Uploaded documents and datasets were parsed, chunked, and embedded using OpenAI Ada and Cohere Embed.
Metadata and structured data were stored in PostgreSQL, while embeddings were indexed in Qdrant.
Queries triggered a hybrid retrieval process combining vector and SQL lookups.
Results were re-ranked by Cohere and passed to GPT-4 for contextual answer generation.
Redis caching reduced repeated query loads, ensuring consistent response times under 1.2 seconds per request.
Operational Impact
Decreased average query latency by 68 percent.
Reduced embedding and inference costs by 35 percent.
Achieved complete tenant data isolation verified under internal compliance audits.
Enabled unified retrieval across structured and unstructured formats with over 93 percent relevance accuracy.