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ENGINEERING
Joshua sum
Founder, Morphic
8 min read
Customer conversations arrive from Slack threads, email chains, transcripts, and chat messages, each with its own formatting, noise, and shorthand.
Turning that chatter into clean, queryable records required an ingestion layer that could normalize wildly different input into one consistent shape.
Multi-channel chatter is hard to turn into structured data.
Most systems either drop unstructured text on the floor or force teams to manually tag and re-enter it. Neither scales past a handful of conversations a day.
We needed a pipeline that could handle volume without losing meaning.
An ingestion pipeline built for noisy, real-world conversation.
The ingestion layer normalizes every source into a common event format, then extracts entities, intent, and relationships before anything is written to storage.
By the time data reaches the workspace, it’s already structured and linked to the right record.
Normalize
Every channel’s raw payload is converted into one common event schema.Extract
Entities, intent, and key details are pulled out of the raw text.Link
Extracted data is matched to the right customer, deal, or project record.
The ingestion layer normalizes every source into a common event format, then extracts entities, intent, and relationships before anything is written to storage.
By the time data reaches the workspace, it’s already structured and linked to the right record.
A dedicated ingestion layer pays off across the whole product.
Handles high volume without dropping messages.
Keeps records deduplicated across channels.
Gives every downstream feature clean, structured input.
Good automation is only as good as the data feeding it. The ingestion layer is the unglamorous foundation that makes everything built on top of it reliable.