Models are downstream of data. We build the pipelines, lakehouses, and contracts that let your AI be trusted — in regulated industries, at the scale of the real business, with lineage you can defend.
Every disappointing AI demo has the same root cause: the data substrate wasn't ready.
The model is the easy part. The hard part is contracts, lineage, freshness, governance, and the unglamorous discipline of making sure the right rows reach the right embedding at the right moment.
We treat data engineering as the load-bearing wall of an AI-native company — not as a cost centre to be outsourced.
From foundational lakehouses to production-grade retrieval — built for the workloads your models actually face.
Databricks, Snowflake, BigQuery, Iceberg. Bronze→silver→gold isn't religion; we choose the shape that fits your access patterns and the questions your business actually asks.
Kafka, Flink, Pulsar, change data capture. Decisions in milliseconds, not in tomorrow's batch window. Built with backpressure and replay assumed.
Embeddings, hybrid search, evals, drift detection. The retrieval layer is the unsung hero of every serious AI system — and the reason most fail in production.
Catalogues, contracts, PII isolation, residency. Defensible AI requires defensible data — and a lineage graph you can show the auditor.
Schema-as-code, producer responsibility, contract tests in CI, freshness SLAs. The discipline that makes downstream teams stop firefighting.
Feature pipelines, point-in-time correctness, online/offline parity. The plumbing that prevents the most expensive class of model bugs.
Tool-agnostic, opinionated about patterns. The shape we reach for in most engagements.
The model is the easy part.— Data Engineering at Revoleap
The substrate is the work.
A working session with our data principals — pipeline reliability, contract maturity, retrieval-readiness benchmarks.
Begin a conversation— Every team has a lakehouse. Few have the semantic layer on top of it that makes the lakehouse legible to a strategy team, a model, or an audit. That is the layer we obsess over.
