Plain English in. Production pipeline out. Governed data product on Day 1. Athyna turns conversation into a workflow. Composer turns the workflow into a Git-versioned, K8s-deployed pipeline โ drag-and-drop, no Python. Reeve publishes the output as a data product with an owner, a contract, an SLA, and a DaaS API. The recipe becomes a product.
Most data teams report spending roughly 60% of their working time investigating, diagnosing, and repairing pipelines that broke overnight โ schemas that drifted, sources that changed, queries that silently returned the wrong rows. Three days a week, per engineer, lost to firefighting.
Three products on one shared semantic layer โ operated by AI agents, reviewed by humans. The recipe an analyst tests interactively in Athyna gets promoted to a production pipeline in Composer with one prompt. The pipeline's output gets published as a governed data product in Reeve with a DaaS API on Day 1. Augmentation, not replacement.
Pair with the AI Data Analyst or AI Data Engineer and describe what you need โ "dedup customers, encrypt SSN, impute null age with median, group by demographics." Athyna compiles the workflow, runs it on the in-memory query engine, and saves the output as a Virtual Live Dataset. Zero data copy. <500ms median transform. 20ร faster.
Once the recipe works, the AI Data Engineer promotes it into Composer as a Git-versioned, K8s-deployed pipeline. Drag-and-drop operators โ Dataset, Blend, Transform, MIL โ wire onto a canvas. Schema contracts validate at design time. Quality gates fire before bad data leaves the pipeline. 9 templates. CDC + streaming. Probabilistic Entity Resolution and SCD-0/1/2/3 built in.
The output isn't a dead screenshot. It's a published Data Product with a name, an owner, a contract, an SLA, a TrustScore, and an API on Day 1. Search it, subscribe to it, consume it. Built on Data-as-a-Product and Data Mesh principles โ federated by domain, governed centrally. Mesh that ships, not mesh that argues.
SemantIQ Active Metadata tracks every transform with column-level lineage โ forward impact ("if I change this, what breaks?") and backward root-cause ("this dashboard is wrong โ where did the data come from?"). Schema drift caught at the door. Plain-English migration reconciliation via the Analytics Data Lake. The firefighting tax disappears.
Three AI agents operate the canvas. Three products do the work. Athyna captures the recipe in plain English. Composer promotes the recipe to a production pipeline with one prompt. Reeve publishes the output as a governed data product with a DaaS API. All on the same in-memory query engine and the same semantic layer โ so what an analyst tests on Monday becomes a subscriber-ready product by Tuesday.
See Athyna, Composer, and Reeve running on your data โ conversation to pipeline to data product, with a DaaS API on Day 1 โ in a 30-minute demo.