2.9M unfilled roles
The talent wall
2.9M unfilled data roles globally. Mid-market teams can't compete for the engineers this work demands.
Auraa
The fastest path to
AI-ready data on Databricks.
Tell Auraa what you need in conversational language. Agents discover your sources, build your pipelines, enforce quality, and register everything in Unity Catalog, autonomously, from day one.
The gap Databricks customers face
Buying Databricks and operationalising it
are completely different problems.
70% maintenance load
The backlog that never clears
Sources the business needs sit in a queue for months. Engineers are consumed by maintenance, not value creation.
12–18 months to value
The activation gap
Databricks customers spending 12–18 months, still not in production. The platform sits idle. The business waits.
Auraa closes that gap. Autonomously. From day one.
The platform
Three things Auraa eliminates permanently.
Autonomous agents that discover, ingest, govern, operate, and build your entire Databricks lakehouse, encoding senior data engineering expertise at unlimited scale.
01
Explode capacity
Reduce engineering requirements by 77%.
Traditional data activation demands an army of engineers. Auraa's agents encode the expertise, your team focuses on the work that actually differentiates your business.
02
Clear the backlog
First source live in under 15 minutes.
Conversational interface. No pipeline code. No configuration. No ticket. The backlog that's been growing for 18 months clears itself.
03
Activate from day one
Deployed in 4 hours. 70% lower cost.
Platform up and running in four hours, not four quarters. 70% lower total cost than a traditional implementation.
Say it. Auraa builds it.
From prompt to Lakehouse.
Autonomously.
No notebooks. No JIRA ticket. No pipeline code. Tell Auraa what you need in conversational language, agents profile your sources, build the pipelines, enforce data quality, and promote tables to Silver in Unity Catalog. Your Databricks lakehouse, built autonomously.
Conversational onboarding Live session
You
Auraa
Profiling source, 47 tables found
Building Bronze pipelines, 3 schedules configured
Applying data quality rules
Promoting to Silver with full lineage
Registered in Unity Catalog
Pipelines live. Silver-ready. Audit trail active.
Governed by Unity Catalog
Silver — production-ready
Bronze — raw, ingested
SQL Server R&D — 47 tables connected
How Auraa works
Autonomous. End to end. On Databricks.
Four steps for fully agent-driven, fully governed, fully on your Databricks workspace.
01
Discover
Identify & profile
Auraa identifies and profiles your data sources. Tell it what you need in plain language. No schema mapping, no manual configuration.
02
Ingest
Build the pipelines
Agents build pipelines automatically. Schema drift is handled without a ticket or a page.
03
Govern
Register & secure
Every pipeline, decision, and data object registered with full lineage and access control, automatic, auditable, queryable.
04
Operate
Monitor & recover
Autonomous monitoring. Agents handle degradation and failure signals. Your team stays focused on what matters.
Coming Full autonomous self-healing — arriving October 2026.
The numbers
Measured outcomes.
%
Lower total cost vs traditional implementation
hrs
From zero to deployed Auraa platform
%
Reduction in engineering team requirement
<min
To onboard your first source, conversationally
Built for
Built for enterprises where AI is the goal, and data is the bottleneck.
Stop throwing an army of engineers at a problem that agents can solve.
Pipeline backlogs, manual onboarding, maintenance that never ends — Auraa automates the 70% of data work that should never have needed a human in the first place.
Your Databricks investment should be working. Not waiting.
Organisations spend many months trying to get from contract to production. Auraa closes that gap — autonomously ingesting sources, enforcing quality, and delivering value from day one.
Resource library.
Beyond Buttons and Prompts: How We Let AI Agents Build Their Own UI
How Auraa Semantic Flow renders human-in-the-loop AI agent interactions identically in web and chat from one JSON descriptor, no new frontend code...
Read the blog
Reducing AI Agent Documentation Costs by 42x Without a Vector Database
How Covasant Engineering built semantic documentation search for AI coding agents at $2–10/month using only Databricks SQL Warehouse,...
Read the blog
How We Made Databricks Apps APIs Fast Without Breaking Our Single Source of Truth
Agentic AI is in production, but is your governance ready? Explore visibility, control, and accountability gaps, plus six principles every enterprise...
Read the blog
Rethinking Data Engineering: What If Your Pipelines Were Driven by Data, Not Code?
What if data pipelines were driven by data, not code? Learn how Auraa's metadata-driven, AI-first approach replaces 6-month builds with hours...
Read the blog
Rethinking Data Engineering: An AI-First, Metadata-Driven Platform for Databricks
Auraa is Covasant’s Databricks-native, agent-driven data platform that automates ingestion, data quality, governance, and analytics across the Lakehouse through a modular suite of components orchestrated by AI agents.
Download the white paper
Sub-Second on the Lakehouse: How Auraa Serves APIs from Databricks Without Breaking the Single Source of Truth
Your data platform has one job: be the single source of truth. But as your teams build more on top of it, APIs, AI agents, real-time applications, a quiet tension emerges.
Download the white paper
Auraa: The Fastest Path to AI-Ready Data on Databricks
Auraa delivers governed, AI-ready Databricks lakehouses faster and at significantly lower cost than traditional implementations...
Download the brochureQuestions that enterprise leaders ask us
If your question is not here, our team will answer it directly.
Talk to a Specialist →What is Auraa and how does it automate data engineering on Databricks?
Auraa is Covasant's Databricks-native data platform that uses autonomous AI agents to automate data engineering across your lakehouse. It turns raw, siloed data into AI-ready data by discovering sources, building data pipelines, enforcing data quality, and governing everything in Unity Catalog, without manual pipeline code. Auraa builds and manages the full medallion architecture (Bronze, Silver, and Gold layers) on Delta Lake, so your Databricks investment delivers value from day one.
How do you automate Databricks pipelines without writing code?
You automate Databricks pipelines with Auraa by describing what you need in plain language, and its AI agents build the pipelines for you. This conversational, no-code approach replaces hand-written notebooks: Auraa profiles each data source, generates autonomous data pipelines on Delta Lake, applies an ELT flow with schema enforcement, and promotes governed Delta tables through the Bronze and Silver layers. Schema drift and change data capture (CDC) are handled automatically, so no ticket or manual reconfiguration is needed.
What is the medallion architecture and how does Auraa build it?
The medallion architecture is the Databricks data design pattern that organizes a lakehouse into Bronze (raw), Silver (cleansed), and Gold (business-ready) layers to improve data quality at each stage. Auraa builds this medallion architecture autonomously: agents ingest raw data into Bronze, cleanse and conform it into Silver, and shape Gold-layer data products for analytics and AI. Every layer is built on Delta Lake and registered in Unity Catalog with full data lineage.
How is Auraa different from a traditional Databricks implementation?
A traditional Databricks implementation gives you the platform but still needs a team of data engineers to build ingestion, transformations, data quality, and governance, often taking 12 to 18 months to reach production. Auraa's autonomous agents do that data engineering work for you, cutting time to value from quarters to hours and reducing total cost of ownership by around 70%. It is the fastest path from buying Databricks to running a governed, production lakehouse.
Can AI agents reduce data engineering cost and team size?
Yes, Auraa's AI agents reduce data engineering team requirements by up to 77% and lower total Databricks implementation cost by roughly 70%. Agentic data engineering automates the repetitive 70% of data work, source onboarding, pipeline building, schema-drift handling, and maintenance, so your existing engineers focus on data modeling, Gold-layer products, and AI use cases instead of being a bottleneck. It scales senior data engineering expertise without scaling headcount.