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Your AI Should Know What Your Business Knows

Retrieval-Augmented Generation (RAG) connects your AI to your real documents, databases, and knowledge bases — so it answers with your data, not guesses. We build production-ready RAG systems on Amazon Bedrock Knowledge Bases, secured and governed for enterprise use.
Book a Free RAG Assessment

The RAG Problem — Why It Matters

Foundation models like Claude, Llama, and GPT know a lot about the world. They know nothing about your product docs, your customer history, your internal policies, or your proprietary research. RAG closes that gap — safely and accurately.
Without RAG
  • AI makes up answers when it doesn't know something.
  • No way to keep AI knowledge current.
  • Security risk: AI trained on sensitive data.
  • Generic answers not relevant to your business context.
With Futuralis RAG
  • AI retrieves real documents before answering — with source citations.
  • Index updated in real time as your documents change.
  • Your data never leaves your AWS environment — no training required.
  • Answers grounded in your specific policies, products, and history.

What We Build

Internal Knowledge Base
Connect Confluence, Notion, SharePoint, Google Drive, or custom document stores to a secure AI assistant your whole team can query in plain English.
Customer-Facing AI Assistant
A product-embedded AI trained on your docs, FAQs, and knowledge base — answering customer questions accurately with zero hallucination.
Regulatory & Compliance Q&A
Purpose-built for legal, finance, and healthcare teams: AI that answers compliance questions with exact citations, audit logs, and access controls.
Multi-Source Research Assistant
Synthesize answers across structured and unstructured data — databases, PDFs, emails, and APIs — through a single natural language interface.

AWS Technology Stack

Amazon Bedrock Knowledge Bases

Managed RAG pipeline — chunking, embedding, indexing, and retrieval. No infrastructure to manage.

Amazon OpenSearch Serverless

Vector search layer for semantic similarity retrieval at scale.

Amazon Kendra

For enterprise search over structured repositories like SharePoint and S3 — with fine-grained access controls.

Amazon S3 + Textract

Ingestion pipeline for PDFs, scanned documents, and multi-format files.

Amazon Bedrock Guardrails

Content filtering, PII redaction, and topic controls — built into every response.

Amazon CloudWatch + Bedrock Model Eval

Accuracy monitoring, latency tracking, and automated regression testing for live RAG pipelines.

Typical Results

What enterprise teams see after putting their own knowledge to work with Futuralis RAG.
Contract review time
65 days – 3 days
Document processing agent + RAG knowledge base
Support resolution rate
43% – 78% first-contact
Customer-facing knowledge base AI
Internal search queries
8 tools – 1 interface
Unified internal knowledge assistant
Document ingestion time
Days – Minutes
Automated S3 ingestion pipeline

Ready to Ground Your AI in Your Own Knowledge?

Let's connect your documents, databases, and knowledge bases to a secure, enterprise-grade RAG system on AWS — answering with your data, not guesses.
Book a Free RAG Assessment

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