PrismBot RAG Implementation
A production-grade, multi-tenant AI chatbot platform enabling organizations to deploy custom, context-aware assistants across web and WhatsApp channels.
Platform
Web (SaaS) + WhatsApp Business API
Duration
3 Months
<300ms
Vector search latency
4x
Retrieval relevance improvement
10
Parallel embedding lookups
Project overview
Demonstrated that retrieval quality is the most critical factor in building reliable AI systems. Combined structured ingestion, advanced retrieval strategies, and multi-agent orchestration.
Platform
Web (SaaS) + WhatsApp Business API
Duration
3 Months
Type
AI & Chatbot
Stack
10 technologies
The challenge
Organizations struggled to deploy reliable AI chatbots due to poor retrieval accuracy, lack of contextual understanding, and absence of production-ready infrastructure for multi-channel delivery.
Context loss due to naive top-K retrieval
Fragmented document chunking causing incomplete responses
No human fallback for failed AI interactions
Disconnected systems across web and WhatsApp channels
Risk of cross-tenant data leakage in shared environments
What we set out to do
- 01
Build a robust retrieval system that handles semantic query variations
- 02
Maintain structured and context-rich document chunking
- 03
Enable seamless human escalation with fallback mechanisms
- 04
Support multi-channel chatbot delivery (web + WhatsApp)
- 05
Ensure strict tenant-level data isolation across all layers
How we solved it
Structured Ingestion Pipeline
Preprocessed documents using LLMs to remove noise and enforce structured formatting. Content was chunked with contextual headers.
Key decision
Structured ingestion before embedding
Result
Improved retrieval accuracy and response completeness.
Advanced Retrieval Strategy
Implemented query expansion, parallel searches, and reranking techniques to improve relevance and diversity.
Key decision
Multi-query retrieval with MMR reranking
Result
~4× improvement in answer relevance.
Efficient Vector Storage
Used PostgreSQL with pgvector and HNSW indexing for fast approximate nearest neighbor search.
Key decision
HNSW indexing with namespace filtering
Result
Sub-300ms retrieval latency at scale.
Multi-Agent Architecture
Designed a modular agent-based system where different agents handle retrieval, enrichment, and response generation.
Key decision
Multi-agent orchestration over monolithic logic
Result
Improved scalability and maintainability.
Real-Time Communication & Escalation
Enabled real-time chat using WebSockets and implemented human escalation with push notifications and email alerts.
Key decision
Built-in human fallback system
Result
Seamless transition between AI and human support.
Measurable impact
4x
Increase in retrieval relevance
300ms
Vector search latency
100%
Escalation delivery via push + email
0
Cross-tenant data leakage
Tech stack
What we learned
This project demonstrated that retrieval quality is the most critical factor in building reliable AI systems. By combining structured ingestion, advanced retrieval strategies, and multi-agent orchestration.
- 01
Retrieval quality impacts output accuracy more than prompt tuning alone
- 02
Structured preprocessing significantly improves embedding performance
- 03
Multi-agent systems scale better than monolithic pipelines
- 04
Human escalation must be a core system feature, not an afterthought
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