AI That Actually Knows Your Business

Generic AI gives generic answers. We build RAG systems that pull from your docs, your data, your products - so your AI sounds like an expert on YOU, not Wikipedia.

Build Your RAG System

See How It Works

Key Metrics

• Answer Accuracy: 95%
• Fewer Support Tickets: 80%
• Response Time: <1s

RAG-Powered Sources

What's our return policy?

According to our return policy, items can be returned within 30 days of purchase for a full refund.

Grounded in Your Data

Why RAG?

The Problem with Generic AI

ChatGPT is smart, but it doesn't know YOUR business.

• 0% Of your data ChatGPT knows
• 40% Hallucination rate without RAG

Your Data, Your Answers

RAG grounds AI in YOUR documents, ensuring accurate, relevant responses.

RAG Solutions for Every Use Case

From customer support to internal knowledge - AI that speaks your language.

Knowledge Base RAG

AI that searches your internal docs, wikis, and manuals. Employees get instant answers without digging through SharePoint.

Product Catalog AI

Answer questions using your actual inventory and specs. "Do you have this in blue?" gets a real answer, not a guess.

Customer Support RAG

Chatbots that pull from your help docs and ticket history. Resolve issues faster with context-aware responses.

Document Q&A

Upload PDFs, contracts, or reports and ask questions. Legal review, due diligence, research - all faster.

Multi-Source RAG

Combine databases, APIs, and documents into one AI interface. The single source of truth your team needs.

Hybrid Search

Vector + keyword search for best accuracy. When semantic search isn't enough, we add traditional search power.

How We Work

From Data Chaos to AI Clarity

1. Data Audit
   We analyze your existing documents, databases, and knowledge sources. What do you have? Where does it live? How current is it?
2. Architecture Design
   We design the optimal retrieval strategy - chunking, embedding models, vector databases, and search algorithms tailored to your data.
3. Build & Index
   We process your documents, build the vector index, and create the retrieval pipeline. Includes testing for accuracy and edge cases.
4. Integration
   We connect RAG to your existing systems - Slack, Teams, your website, CRM, helpdesk. Wherever your users are.
5. Monitor & Improve
   We track what questions get asked, what answers work, and continuously improve accuracy. RAG gets smarter over time.

FAQ

What is RAG and how is it different from regular ChatGPT?

RAG (Retrieval Augmented Generation) connects AI to YOUR data. Instead of relying only on what ChatGPT learned during training, RAG retrieves relevant information from your documents and uses it to generate accurate, up-to-date answers.

Is my data secure?

Yes. We can deploy RAG using local models (not in the cloud) or with SOC2/GDPR-compliant cloud providers like Azure OpenAI. Your data is never used for model training.

How long does it take to implement a RAG system?

A basic POC with a single data source takes 2-4 weeks. A full production system with multiple integrations, access controls, and monitoring takes 6-12 weeks depending on complexity.

What types of documents can RAG work with?

Almost anything: PDFs, Word docs, web pages, Notion, Confluence, SharePoint, Google Drive, databases, APIs, and more. If it contains text, we can index it.

How accurate is RAG compared to regular AI?

Much more accurate for your specific use case. Generic AI hallucinates about 30-40% of the time when asked about specific company information. Well-implemented RAG reduces this to under 5% while citing sources.

Can RAG handle multiple languages?

Yes. Modern embedding models work across languages. Your Hebrew documents can answer English questions and vice versa.