Self-Maintaining Knowledge for Accurate Responses
RAG System with Chain-of-Thought Retrieval
RAG enhances LLM responses by retrieving relevant context from a knowledge base before generating answers.
Self-Cleaning
Auto
Deduplication
Integrations
3000+
CRMs/ERPs/KBs
Executive Summary
What we built
A Retrieval-Augmented Generation (RAG) system that automatically cleans, structures, and prioritizes knowledge using Chain-of-Thought reasoning — ensuring voice agents always have accurate, up-to-date information.
Why it matters
RAG enhances LLM responses by retrieving relevant context from a knowledge base before generating answers. This ensures accuracy (responses grounded in data), currency (up-to-date without retraining), and specificity (domain and client-specific knowledge).
Results
- >90% retrieval precision
- >85% retrieval recall
- >95% answer accuracy
- <2% hallucination rate
Best for
- →Healthcare patient FAQs and procedure information
- →Education enrollment and program details
- →E-commerce product information and policies
- →Enterprise knowledge management
Limitations
- Quality depends on source document quality
- Real-time scraping requires scheduling configuration
- Large knowledge bases require horizontal scaling
How It Works
A two-layer detection system where each covers the other's weaknesses.
Ingestion Layer
Multi-format document parsing
- Parse DOC, DOCX, PDF, TXT, RTF documents
- Handle XLS, XLSX, CSV spreadsheets
- Extract from HTML, JSON, XML, YAML
- OCR for images, transcripts for audio
Processing Pipeline
Cleaning, chunking, and embedding
- Remove duplicates and fix formatting
- Organize into logical chunks
- Rank by relevance and recency
- Generate semantic vectors
Chain-of-Thought Retrieval
Reasoning-based context selection
- Understand query intent
- Decompose into sub-questions
- Retrieve and synthesize chunks
- Validate for contradictions
Product Features
Ready for production with enterprise-grade reliability.
Multi-Format Ingestion
Support for DOC, PDF, XLS, CSV, JSON, XML, images (OCR), and audio transcripts.
Self-Maintaining
Automatic cleaning, deduplication, and prioritization keeps knowledge base current.
Chain-of-Thought Retrieval
Reasoning-based retrieval that understands intent, decomposes queries, and validates results.
3000+ Integrations
Connect to HubSpot, Salesforce, Zendesk, Calendly, Shopify, and thousands more.
Dynamic Web Scraping
Automatic content extraction with scheduled refresh cycles and change detection.
Omni-Channel Memory
Agents retain context across voice, text, and email channels.
Integration Details
Runs On
Cloud (Vector Store) + API integrations
Latency Budget
Zero cold-start impact on latency
Providers
HubSpot, Salesforce, Zendesk, Calendly, Shopify
Implementation
1-2 weeks for standard integration
Frequently Asked Questions
Common questions about our voicemail detection system.
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