Synapse
Multi-Tenant Agentic RAG Platform
Enterprise multi-tenant Arabic–English RAG platform serving 50+ enterprise tenants with zero data leakage.
The Engineering Challenge
Enterprise clients across the Middle East required bilingual knowledge querying over proprietary institutional documents.
Core ProblemServe 50+ enterprise tenants on a shared platform with strict zero cross-tenant data leakage, sub-second latency, and bilingual Arabic-English precision.
- Zero cross-tenant data leakage at any cost
- Arabic script tokenization & morphological retrieval challenges
- p95 query response time < 350 ms across mixed document formats
System Architecture
End-to-end data pipelines and component isolation boundaries.
LangGraph multi-agent orchestration routes requests to tenant-isolated Qdrant vector collections with RBAC enforcement directly at the retrieval layer. Reranked using Jev for precision before synthesis.
Key Engineering Decisions
Retrieval-Layer Isolation over Prompt Sandboxing
Tenant boundaries are enforced cryptographically in the query filter and collection access layer rather than relying on prompt instructions.
Dedicated Per-Tenant Collections
Isolated Qdrant collections per client prevent vector space interference and guarantee compliant data separation.
Automated Evaluation Harness
Built DeepEval scoring pipeline evaluating retrieval faithfulness, answer relevancy, and hallucination rate across a 90-question benchmark.
Production Verification & Results
- Maintained 287 ms p95 retrieval-to-answer latency across 50+ active enterprise tenants
- DeepEval test benchmark achieved 94% retrieval faithfulness on 90 production questions
- Zero reported cross-tenant data bleed incidents across 12+ months in production
Production Stack & Tooling
Next Iterations & Roadmap
- Dynamic collection partitioning with auto-scaling vector replicas
- Fine-tuned bilingual embedding model for specialized Middle Eastern legal vocabulary
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