Databricks AI Engineering
You have AI models that work, and now they need to keep working. RevoData handles the heavy lifting of AI infrastructure on Databricks, so your team can focus on what they do best: innovating.
Is AI engineering something you need?
Your business relies on a Gen AI tool and you need it to stay accurate.
Your compute costs are growing faster than your AI use.
You have a proof of concept and want to take it to production.
Your team updates models by hand instead of building new features.
The benefits of Databricks AI Engineering
Move from code to production in days, not months.
Guard against silent problems like model drift and hallucinations.
Optimize Databricks clusters so compute costs don’t outpace growth.
Automate repeatable tasks and spend less time firefighting, more time on new features.
Seven pillars of a reliable AI engine
Reproduce the exact state of your application at any time. Get AI outcomes you can rely on.
Automate repeatable tasks. Give your engineers time to build new features.
Version code, data and models together. Trace and audit every insight.
Test code, data and models before they reach production. Catch errors and unwanted results early.
Track data quality, model performance and errors. Catch model drift before it affects your business.
Keep development and production code apart. Decide what is deployed, and when.
Combine development and operations in one team. Work in rapid sprints that deliver continuous value.
AI models as robust as enterprise software — from proof of concept to global scale.
Talk to an AI engineer
Generative AI takes a different discipline
Keep your “chat with your data” tools grounded in facts.
Turn raw model responses into business-ready actions that fit your systems.
Treat prompts like code, so outputs stay consistent and predictable.
Monitor for hallucinations in real time and protect your brand reputation.
Frequently asked
What does AI engineering cover?
Reproducibility, automation, versioning, testing, monitoring, deployments and ways of working — all on the Databricks platform.
Our model is still a proof of concept. Can you help?
Yes. That is what AI engineering is for: we take your model from proof of concept to production, and from there to global scale.
Do you work with Generative AI?
Yes. We build pipelines for RAG evaluation, prompt management, guardrails and last-mile enrichment.
How do you catch model drift?
We monitor data quality, model performance and errors, so drift shows up before it affects your business.
Will our compute costs grow with our AI?
Not faster than your growth. We optimize your Databricks clusters to keep compute costs in line.
Build an AI lifecycle that lasts
From proof of concept to global scale, we bridge the gap.
Talk to an AI engineer