Case Studies › Merchflow
Business Operations & Data

From Manual Data Entry to Automated ETL Processing at Scale

Merchflow processed data from hundreds of vendors — each with different formats, structures, and update frequencies. The manual handling was unsustainable. We built the system to automate every step of it.

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M
Merchflow
Business Operations & Data
500k+
records processed/day
90%
less manual processing
8 wks
to production
8 weeks
timeline
This engagement is best for
Operations teams processing high-volume, multi-source data
Platforms managing data from many vendors or partners
Businesses replacing manual data entry and transformation workflows
Teams needing reliable, auditable data pipelines for downstream reporting
The Transformation

Before & After

Before
✕ Vendor data processed manually by the operations team each day
✕ Hundreds of vendors each using different data formats and structures
✕ Errors introduced through manual transformation and entry
✕ No audit trail for data changes or processing failures
✕ Scaling to more merchants required proportional team growth
After
✓ Fully automated ETL pipeline processing all vendor data without manual input
✓ Normalisation layer handles multiple formats automatically
✓ Built-in error detection, alerting, and retry logic
✓ Complete audit trail with data lineage tracking
✓ New merchant onboarding requires near-zero manual effort
What We Built

Deliverables & Scope

Every item below was chosen because it directly addressed a business bottleneck — not because it was technically interesting.

01
Multi-source ETL pipeline with format normalisation layer
02
Redis-based job queue for high-throughput async processing
03
Error detection, alerting, and automatic retry logic
04
Admin dashboard for pipeline monitoring and manual overrides
05
Audit log system with full data lineage
06
Merchant onboarding API for self-serve data integration
ROI Logic

Why This Generated
Real Business Value

Automating the data pipeline effectively 10×'d Merchflow's processing capacity without adding headcount. The 90% reduction in manual work freed the operations team to focus on merchant relationships and product improvements — and eliminated the class of data errors that previously reached downstream systems.

Key Outcomes
500k+
records processed/day
90%
less manual processing
8 wks
to production
Why It Worked

The Decisions That
Made the Difference

Good execution matters. But the right early decisions matter more.

01
Redis queue architecture absorbed burst data volumes without performance degradation
02
The flexible normalisation layer adapted to new vendor formats without code changes
03
Admin override capability maintained operational control during edge cases
04
Proper error handling and retry logic prevented silent data corruption in production
Tech Stack
Node.js Express.js React.js PostgreSQL Redis
Integrations
100+ Vendor APIs Internal Admin System Reporting Dashboard
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