Everything you need to build
Enterprise RAG
From ingestion to retrieval, IngestIQ provides the complete infrastructure for building production-grade AI agents.
Complete RAG Pipeline
Everything you need to build production-grade AI agents, from ingestion to retrieval.
Ingestion Engine
Connect to your data wherever it lives. Real-time sync and automatic updates.
Audio Upload
MP3, WAV, M4A support
File Upload
PDF, DOCX, TXT, CSV
Google Drive
Docs, Sheets, Slides
Google Sheets Sync
Scheduled auto-sync at custom intervals
Image Upload
OCR & Vision analysis
Video Upload
MP4, MOV, AVI
Web Scrape
Crawl any website
Processing Pipeline
Transform raw data into structured, queryable intelligence.
OCR Engine
Extract text from images/PDFs
PII Redaction
Auto-remove sensitive data
Semantic Chunking
Context-aware splitting
Audio Transcription
Speaker diarization
Video Analysis
Scene detection & OCR
Retrieval & Search
Enterprise-grade search capabilities across any destination.
Dynamic Destinations
Pinecone, Qdrant, Milvus, MongoDB, pgvector
MCP Server Integration
Dedicated URLs for isolated KBs
Specialized Agents
Hyper-focused department-level bots
Parallel Neural Search
Concurrent search across all KBs with merged & re-ranked results
Hybrid Retrieval
Semantic search, question-based reranking & metadata filtering
Where Ingestion Meets Intelligence
See how your data goes from raw files to AI-ready answers, step by step.
No More Token Limits
Semantic Batching
Recursively chunks documents by meaning, not character count. 500-page PDFs? No problem. Tables, headers, and page numbers stay attached to every chunk.
- Recursive splitting by paragraphs and sentences, not arbitrary character counts.
- Visual chunking that preserves table structures and layout context.
- Metadata preservation. Page numbers and headers stay with every chunk.
- Grounded context means ground truth answers, zero hallucinations.
Find the Right Answer, Not Just Similar Text
Hybrid Search + Reranking
Combines semantic search with AI-generated question matching. Cross-encoder reranking ensures the most relevant results surface first. Filter by metadata, date, or source.
- Content matching. Semantic search across your entire document corpus.
- Question matching. Queries matched against AI-generated questions per chunk.
- Cross-encoder reranking. Scores query-document pairs for precision.
- Metadata filtering. 'Show me contracts from 2024 signed by Alice'.
One Query, Every Department
Cross-Knowledge Base Search
Create separate knowledge bases for legal, HR, engineering, support. Search across all of them simultaneously. Unified ranking, smart filtering.
- Multi-KB search. Query all your knowledge bases at once.
- Aggregated results. Unified ranking across all sources.
- Smart filtering. Filter by knowledge base, date, or metadata.
- Category organization. Structure your data by team or topic.
Your Data Becomes an AI Tool
MCP-Native Routing
Expose your knowledge bases as Model Context Protocol servers. Claude, GPT, and other agents can tool-call your documentation directly. No custom integrations needed.
- Standard protocol. Native support for the open MCP standard.
- Tool calling. LLMs query your data like a database.
- Agent hand-off. Seamlessly pass context between specialist agents.
- Secure tunneling. Expose local data safely to cloud LLMs.
Ready to ingest?
Join hundreds of engineering teams building the next generation of AI agents.