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Use Cases

AI-Powered Data Pipelines

Agents that ingest, clean, enrich, and route data with LLM reasoning at every step. An analytics team at a logistics company automated 80% of their weekly report generation with Diaflow.

Pipeline run
Records ingested1,240
Enriched by LLM1,180
Duplicates removed60
Run time4.1s

How it works

Data pipeline agent flow showing ingestion, enrichment with LLM reasoning, deduplication, and output routing

Customer story

An analytics team at a logistics company in Southeast Asia was manually assembling weekly operations reports from three different data sources. The process took 4-6 hours every Monday and was prone to errors when data formats changed upstream.

They built a Diaflow pipeline that ingests data from their APIs and databases, uses an LLM step to normalize and enrich records, deduplicates with configurable rules, and routes output to PostgreSQL. A second agent generates the weekly report from the cleaned data and posts it to Slack. The full pipeline runs in under 10 seconds.

"We didn't realize how much of our Monday mornings were going into data prep. Now the report is ready before anyone logs in. The team actually trusts it more because the LLM catches formatting issues our old script missed."

Head of Analytics, logistics data team, SEA

Key features

Multi-source ingestion from APIs + DBs
LLM-powered data normalization
Configurable deduplication rules
Webhook and DB output routing
Full run trace for debugging
Scheduled or event-triggered runs

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