From Your Raw Data
to a Complete AI-Ready
Knowledge Base
The RAG platform that handles the entire pipeline. Connect your data, chunk it intelligently, embed it, and load it into your own vector database. So you can focus on building AI agents, not the hard parts.
Every Problem Has a Clear Solution
Pick a challenge to see how IngestIQ handles it.
Large Content Fails in Most Systems
Long content like big PDFs, long articles, scraped web pages, transcripts, or any large text often breaks in traditional setups. When everything is sent to an LLM at once, it hits input and output token limits before processing the full content.
Semantic Batch Processing for Any Type of Content
Instead of pushing the entire content into the LLM, we process it intelligently:
- We send the full content to the LLM only to detect semantic breakpoints (meaningful sections)
- The content is split into batches based on meaning, not random length
- Each batch is processed individually and can run in parallel for speed
- Works for PDFs, long articles, web-scraped content, transcripts, audio/video text, and more
Long content like big PDFs, long articles, scraped web pages, transcripts, or any large text often breaks in traditional setups. When everything is sent to an LLM at once, it hits input and output token limits before processing the full content.
Semantic Batch Processing for Any Type of Content
Instead of pushing the entire content into the LLM, we process it intelligently:
- We send the full content to the LLM only to detect semantic breakpoints (meaningful sections)
- The content is split into batches based on meaning, not random length
- Each batch is processed individually and can run in parallel for speed
- Works for PDFs, long articles, web-scraped content, transcripts, audio/video text, and more
From Raw Data to
Intelligence in 4 Steps
Connect
Teams waste weeks writing custom ingestion scripts for every data source. One breaks, the whole pipeline stops.
One interface for all your data. FilesGoogle DriveWeb ScrapeAudioVideoGoogle SheetImages Drop it in and move on.
Process
Most parsers strip out tables, headers, and layout, turning structured documents into a wall of text your AI can't reason over.
Structure-aware parsing powered by OpenAIGeminiClaudeVoyageJina Tables, headers, and context are fully preserved.
Store
Most platforms lock your vectors into their own storage. Switching later means rebuilding everything from scratch.
Vectors land in YOUR database. PineconeQdrantMilvuspgvectorMongoDB You choose today, switch tomorrow.
Serve
You've processed and stored your data, but connecting it to AI agents usually means writing custom integrations for every tool.
Search across all your knowledge bases with one API call. Or expose them as MCP servers for your AI agents.
Your Data.
Your Database.
Our Engine.
We are the processor, not the storage. IngestIQ connects to your own infrastructure, ensuring you maintain complete ownership over your proprietary intelligence. We do not use your data to train the model or for any other purpose.
Real Use Cases by Industry
Clear outcomes for legal, finance, healthcare, and manufacturing teams.