Smart AI Customer Assistant
Your business knowledge — available 24/7, in any language.
We build enterprise RAG (Retrieval-Augmented Generation) chatbots trained on your PDFs, documentation, website, and internal knowledge bases. Using LangChain and OpenAI's latest models with pgvector semantic search, your chatbot answers accurately and never hallucinates outside its knowledge domain.
Smart AI Customer Assistant
Live Architecture Spec
pgvector RAG
Vector Search
99.4%
Accuracy
< 1.2s
Latency
// pgvector Semantic RAG Search
const docs = await vectorStore.similaritySearch(query, 5);
const answer = await llm.generate(docs);Our Delivery Process
Knowledge Base Audit
We analyze your existing documentation, PDFs, FAQs, and website content to define the training corpus.
Vector Database Setup
Document chunking, embedding generation, and pgvector indexing — your knowledge becomes semantically searchable.
LangChain RAG Pipeline
Custom retrieval chain with re-ranking, citation tracking, and confidence thresholds to prevent hallucination.
Chat Widget Build
Embeddable React chat widget with streaming responses, conversation history, and lead capture forms.
Analytics & Monitoring
Question analytics, unanswered query logs, and weekly performance reports so you can improve the bot continuously.
Every Deliverable, Included
SaaSFlow Global
"Our customers get instant answers at 2 AM. Customer satisfaction jumped 40%."
Common Questions
Will it make things up (hallucinate)?
We implement confidence thresholds and citation tracking. If the answer isn't in your knowledge base, the bot says so honestly.
Can it capture leads?
Yes — we build lead capture flows triggered when the bot detects purchase intent or unanswered questions.
How often can I update the knowledge base?
Instantly — your admin dashboard lets you add, edit, or remove documents and the vector index updates in real-time.