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How Cloud AI Manager Turns Connected Content Into Usable Answers

Underneath the simple search interface is a sophisticated retrieval-augmented generation (RAG) system built on Claude AI. Here’s how it works.
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Connect
Link your documentation platforms, CRM, communication tools, and file storage through OAuth integrations.
Index
Cloud AI Manager ingests and indexes your content using advanced retrieval algorithms with automatic updates.
Ask
Type questions in natural language and get accurate answers with citations from your actual business data.

Step 1: Multi-Source Content Ingestion

Cloud AI Manager connects to your systems via native APIs and OAuth. Once authorized, it pulls content on a scheduled basis — documents, messages, CRM records, call transcripts, project updates, and more.

Structured Data Extraction

CRM fields, project metadata, user attributes are preserved for filtering and context

Unstructured Text Processing

Documents, Slack threads, and emails are parsed to extract readable text while preserving formatting

Permission Mapping

Access controls from source systems are mapped to Cloud AI Manager to ensure users only see content they're authorized to view

Incremental Updates

Only new or modified content is re-indexed, keeping your knowledge base current without full re-syncs

What Gets Indexed
Documents
PDFs, Word files, Google Docs, Notion pages, Confluence articles
Conversations
Slack messages, Teams chats, email threads, call transcripts
Structured Records
CRM contacts, deals, tickets, Jira issues, project tasks
Web Content
Internal wikis, knowledge bases, documentation sites
RAG Pipeline Architecture
  • Content chunked into semantic units (paragraphs, sections, records)
  • Each chunk converted to vector embeddings using state-of-the-art models
  • Vectors stored in high-performance index with metadata tags
  • Keyword index built for hybrid search combining semantic and lexical retrieval

Step 2: Intelligent Indexing & Vector Storage

Raw content is transformed into searchable knowledge through a multi-stage indexing pipeline. This is where the magic happens — converting documents and conversations into mathematically searchable vectors.

Semantic Chunking

Content is intelligently split based on meaning, not just character count, preserving context

Vector Embeddings

Each chunk becomes a high-dimensional vector that captures semantic meaning for similarity search

Metadata Enrichment

Source, timestamp, author, tags, and permissions attached to each indexed chunk

Hybrid Search Index

Combines vector similarity with keyword matching for best-of-both-worlds retrieval

Step 3: Query & Retrieval-Augmented Generation

When you ask a question, Cloud AI Manager doesn’t just search — it understands intent, retrieves relevant context, and generates a grounded answer using Claude AI.

Query Understanding

Your question is analyzed for intent, entities, and context to guide retrieval

Hybrid Retrieval

Vector similarity search combined with keyword matching surfaces the most relevant chunks

Permission Filtering

Results automatically filtered based on your access rights — you only see what you're allowed to see

Context Assembly

Top-ranked chunks assembled into context with source attribution

Answer Generation

Claude AI synthesizes retrieved context into a natural language answer with inline citations

Why Retrieval-Augmented Generation?

RAG combines the best of search and AI generation while avoiding the pitfalls of each approach alone.
What RAG Gets Right
  • Answers grounded in your actual data, not training data from the internet
  • Citations link back to source material so you can verify every claim
  • Stays current as your data changes — no model retraining required
  • Natural language understanding instead of rigid keyword matching
  • Synthesizes information from multiple sources into coherent answers
What Traditional Search Can't Do
  • Keyword search misses synonyms and related concepts
  • Returns a list of links, not synthesized answers
  • Can't combine information from multiple sources
  • Requires exact term matching for relevance
  • Users must read through multiple documents to find answers

Powered by Claude AI

Cloud AI Manager uses Anthropic’s Claude as its underlying language model. Claude excels at understanding nuance, synthesizing information from long documents, and generating accurate, helpful responses without hallucination.
Combined with best-in-class retrieval technology, Cloud AI Manager delivers answers you can trust — every time.

See the Technology in Action

Book a demo to see how RAG-powered search works with your actual business data.
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