DATA ENGINEERINGThe data layer that decides if your AI works.
AI, analytics, and automation only work when the data layer is boring in the best way: contracted events, idempotent consumers, replay paths, lineage, retention, and freshness checks before stale data turns into bad decisions.
- 01Two-week auditFixed price. Domain map, risks, and a delivery plan.
- 02Working sliceOne real workflow shipped end to end.
- 03Progressive rolloutTraffic moves in steps; rollback stays ready.
- 04HandoffRunbooks, dashboards, and docs your team keeps.
System of record behind the deterministic IndepAI finance core
IndepAIWhat we build
Streaming architecture
Kafka/NATS topics, partitioning, schemas, consumer contracts, DLQs, and replay tooling.
Operational PostgreSQL
Schema design, indexing, migrations, replication, backup, and performance checks.
Analytics and lakehouse design
Warehouse/lake choices by workload, not trend; cost-aware storage and compute boundaries.
Governance and lineage
PII tagging, retention, access control, audit trails, and data-quality monitors.
Stack in production
The tools we run for this work, and what each one is there to do.
Event streaming with contracts and replay.
The default system of record.
Fast analytics on raw event volumes.
Warehouse for governed, shared analytics.
Tested, versioned SQL transformations.
Scheduled pipelines with retries and lineage.
Change data capture from operational databases.
What you get
- 01Source-of-truth event schema contracted between producers and consumers.
- 02Dead-letter queues, replay tooling, idempotent consumers.
- 03Data-quality monitors for freshness, volume, distribution, and schema drift.
- 04Cost-aware partitioning, retention, and warehouse/lake lifecycle policies.
Start with a two-week audit.
Fixed price. We map the domain, ship a working slice, and hand over documentation your team keeps, whatever you decide next.