Data, Memory & Knowledge. Pipelines

Automated Data Pipelines for Always-Fresh AI Knowledge

Connect every data source your organisation has and keep your AI knowledge base current within minutes, with 100+ connectors, automatic chunking, and 24/7 monitoring.

Pipeline Status
ConfluenceLag: 2m 14s48,221 docs
Google DriveLag: 3m 01s12,005 docs
SalesforceLag: 1m 42s8,740 docs
GitHubLag: 4m 55s92,100 docs
4 pipelines · All healthy · 24/7 monitored
< 5min
Ingest lag
100+
Connectors
Auto
Chunking
24/7
Monitoring

Overview

What are AI data pipelines?

A RAG system or knowledge base is only as good as its data. Stale documents produce stale answers. AI data pipelines continuously sync your source systems, wikis, drives, CRMs, ticketing tools, code repositories, into a clean, chunked, embedded knowledge base. Pre-built connectors eliminate custom integration work, and 24/7 monitoring ensures pipelines recover automatically from failures.

What's included

100+ pre-built connectors

Connect Confluence, Notion, Google Drive, SharePoint, Salesforce, Zendesk, GitHub, Jira, Slack, and 90+ more sources in minutes, no custom code.

Incremental sync

Only changed documents are re-processed on each sync cycle, keeping ingest lag under 5 minutes without unnecessary compute costs.

Intelligent chunking

Documents are chunked by semantic boundaries, paragraphs, sections, code blocks, not arbitrary token counts, for higher retrieval accuracy.

Embedding pipeline

Chunks are embedded automatically using your configured embedding model. Model changes trigger automatic re-embedding of the full corpus.

Schema transformation

Map source system fields to a canonical schema with transformation rules, normalising dates, currencies, and identifiers across data sources.

24/7 health monitoring

Pipeline health is monitored continuously. Failed syncs trigger automatic retry with exponential backoff and on-call alerts for sustained failures.

How it works

From setup to production

01

Connect

Authenticate and connect source systems using pre-built connectors. OAuth-based connection typically takes under 5 minutes per source.

02

Configure

Set sync frequency, chunking strategy, embedding model, and destination (vector store, knowledge graph, or both).

03

Sync

The initial full sync runs immediately. Incremental syncs keep the knowledge base current within minutes of source changes.

04

Monitor

A pipeline health dashboard shows sync lag, error rates, and document counts per source. Alerts fire before failures impact end users.

01

Connect

Authenticate and connect source systems using pre-built connectors. OAuth-based connection typically takes under 5 minutes per source.

02

Configure

Set sync frequency, chunking strategy, embedding model, and destination (vector store, knowledge graph, or both).

03

Sync

The initial full sync runs immediately. Incremental syncs keep the knowledge base current within minutes of source changes.

04

Monitor

A pipeline health dashboard shows sync lag, error rates, and document counts per source. Alerts fire before failures impact end users.

FAQ

Common questions

Related

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Keep your AI knowledge base current with automated pipelines

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