Data Engineering

Data engineering shouldn’t take months. Describe what you need, and Corvic builds the pipeline, runs it on a schedule, and keeps the result fresh.

Pipelines that run every morning

Set it once: pull from your sources, transform, and land a fresh table before anyone logs in.


Bring	The sources and the table you want at the end
Connect	Databases and storage
Start with	The New Data Pipeline playbook, or describe it in Builder Chat
You get	A scheduled pipeline, versioned in GitHub if you like

Try asking: “Every day at 6am, pull yesterday’s orders from Postgres, join with customers, and refresh the revenue table.”

Connect APIs, documents, and tables into one graph your agents can reason over, with no keys exposed.


Bring	Documents and tables, plus the services to connect
Connect	Anything in the Connector Vault
Start with	The Knowledge Graph from Documents or Graph over Your Tables playbook
You get	A knowledge graph and agents that cite it

Try asking: “Which customers mentioned in support tickets also have open opportunities over $100k?”

Ingest raw datasets, clean and profile them, and produce ML-ready features.


Bring	Raw CSV, Parquet, or JSON, and a description of the ML task
Connect	HuggingFace, Kaggle
Start with	The Dataset Cleaning & Feature Prep playbook
You get	A cleaned, profiled table and a feature set

Try asking: “Profile this dataset, fix the obvious quality issues, and build features to predict churn.”

Add columns to any table with an LLM, code, or the live web: sentiment, categories, firmographics.


Bring	The table to enrich
Start with	The Augment a Table with New Columns playbook, or LLM Augment
You get	New columns on every row, rerun as data changes

Try asking: “Add sentiment and a topic label to every review in this table.”

Watch the full walkthrough: Data Enrichment.

When a run works, save it as a playbook so anyone on your team can rerun it, or publish the result as a live app. See all use cases.