Omnira
Your docs become a chatbot. In 5 minutes.
Upload your PDFs, FAQs, and knowledge base. Omnira turns them into an AI assistant that actually knows your business. Embed it on your site with one line of code.
Key Metrics
The Problem
Generic AI chatbots are frustrating. Ask about your return policy and ChatGPT hallucinates. Train a custom model and you're looking at months and millions. There's no middle ground.
The Solution
RAG (Retrieval-Augmented Generation) bridges the gap. Upload your docs, we chunk and embed them, and when users ask questions, the AI searches your knowledge base first—then answers with your actual content as context.
Key Features
Document Intelligence
PDFs, Word docs, Markdown, even web scraping. Omnira digests it all.
One-Line Embed
45KB widget. Paste the script tag, chatbot appears. Style it to match your brand.
Model Flexibility
GPT-3.5 for speed, GPT-4 for accuracy, GPT-4 Turbo for both. You choose.
Analytics Dashboard
What are customers asking? Which docs get cited most? Find content gaps.
Conversation Memory
Follow-up questions work. "What about shipping?" after asking about returns—it remembers.
Auto-Update
Drop new docs in the folder. Omnira notices and re-indexes automatically.
Before & After
Technology Stack
Frontend
Backend
Database
AI/ML
Technical Highlights
Vector Search with pgvector
Semantic similarity search in PostgreSQL. No separate vector DB needed.
Smart Chunking
500-1000 tokens per chunk with 100-token overlap. Context stays coherent.
Hybrid Search
Keyword matching + semantic similarity. Best of both retrieval worlds.
Widget Performance
<500ms initialization, <5MB memory. Doesn't slow down your site.
Challenges Overcome
PDF parsing quality varies wildly
Multiple extractors with fallback chain. pdf-parse → pdfminer → OCR as last resort.
Chunk boundaries cut mid-sentence
Smart splitting on paragraph breaks with overlap ensures no context loss.
Lessons Learned
- RAG accuracy depends more on chunking strategy than model choice.
- Users don't read docs, but they'll happily ask a chatbot the same questions.
- pgvector is good enough. You don't need Pinecone for most use cases.