Explicit contracts
Identity, schemas, semantics, versioning, idempotency, and ownership defined at system boundaries.
Data engineering and integrations
Make operational data reliable, connected, and recoverable
We design data platforms and integrations for operations where duplicates, delays, incompatible contracts, and partial failures are normal conditions—not theoretical exceptions.
Connecting systems is not just moving fields
- The same records arrive duplicated, out of order, or with different meaning in each source.
- A slow or unavailable API blocks internal processes without a safe retry or replay path.
- Teams cannot explain where data came from, what transformed it, or how to correct it.
What we build
What we build
Recoverable flows
Ingestion, validation, deduplication, retries, dead letters, replay, and reconciliation designed for partial failure.
Observable operations
Metrics, traces, alerts, lineage, and runbooks that let teams detect and resolve problems without guessing.
Architecture from semantics to production
Architecture from semantics to production
Model meaning and failure
We define entities, time, consistency, volume, privacy, and failure modes before choosing batch, events, or synchronous calls.
Test the boundaries
We validate contracts, duplicates, disorder, timeouts, backpressure, and recovery with reproducible cases.
Design for the operator
The platform includes visibility, limits, and recovery procedures; it does not end when data reaches a table.
Good fit
- Operational processes that depend on multiple sources, partners, APIs, or events.
- Data that must be traceable, reconcilable, and recoverable after failure.
- Integrations where meaning, identity, and consistency matter more than connecting endpoints.
Not the best fit
- An isolated dashboard with no ownership or quality work at the source.
- Copying information between two tools without additional operational requirements.
- An architecture chosen from a technology list before understanding the flow.
Prior experience + reference architecture
See the data engineering case From scattered sources to trusted data
The pipeline case explains decisions, limits, and provenance. It is not presented as a Codiva client or commercial outcome.
Discuss the data your operation needs to trust
Bring the flow, systems, and most expensive failure. We will tell you whether we are the right team and what evidence is missing.