Cutting P99 Latency from 750ms to 210ms in Six Weeks
How Digiteria Labs partnered with a fintech marketplace to unlock real-time decisioning.
Shipping AI features is meaningless if the platform cannot meet real-time SLAs. A fintech marketplace hired Digiteria Labs to eliminate decision-engine delays that were blocking fraud prevention and customer approvals.
The Challenge
- Legacy Airflow pipelines copying data across three warehouses.
- Kafka topics without schema enforcement causing downstream replay storms.
- API layer throttled by synchronous joins on bloated Postgres tables.
The net result: 750–900ms p99 latency and risk models that missed windowed events.
Digiteria Labs Approach
- Data Contract Sprint: Introduced Confluent Schema Registry, wrote compatibility checks, and enforced producers to publish within typed envelopes.
- Operational Lakehouse: Migrated to Iceberg tables on top of object storage with low-latency snapshot isolation. Implemented incremental compaction jobs with auto-vacuum.
- Feature Serving Pod: Stood up Redis + DynamoDB hybrid for hot features, implemented asynchronous fan-out to downstream stores.
- Runtime Guardrails: Added service budgets, synthetic monitoring, and automated rollback to previous feature sets.
Outcomes in 6 Weeks
- p99 latency dropped from 780ms → 210ms.
- Model approval accuracy improved 12% with complete event windows.
- Incident volume reduced by 68% due to contract enforcement and replay tooling.
- Finance team projected $480k annual savings from decommissioned warehouse copies.
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