Products → AI-Powered Code Modernization & Optimization Platform
Karsient RevoCode
Modernize Today. Continuously Engineer Tomorrow.
RevoCode continuously analyzes your modernized Databricks codebase — refactoring, optimizing, and evolving it after migration, so performance and cost stay under control long after cutover.
Why it exists
Problems RevoCode solves
Technical debt re-accumulates in the codebase even after modernization
Inefficient SQL and PySpark patterns creep in as new logic is added
Duplicated and dead code goes undetected across a growing codebase
No continuous visibility into architecture or maintainability quality
Databricks workload and cloud cost creep without a clear owner
How it works
Inside the RevoCode process
Refactor → Optimize → Reduce Technical Debt → Improve Performance → Continuously Modernize
Under the hood
How RevoCode finds what to optimize
RevoCode continuously profiles the modernized codebase — static analysis of code structure, plus runtime signals from actual Databricks job execution — to separate real bottlenecks from noise.
Analyzes query plans and Spark execution graphs to find expensive joins and shuffles
Runs static analysis across notebooks and jobs to detect duplicate and dead code
Correlates cost and performance data per workload to rank recommendations by impact
Tracks maintainability and technical-debt scores over time, not just a single snapshot
Capabilities
What's inside
AI-powered code refactoring
Recommends and generates refactored implementations for inefficient or complex code.
Code quality improvement
Continuously scores and flags code quality issues across the modernized estate.
SQL optimization
Identifies expensive joins, scans, and query patterns and recommends optimized alternatives.
PySpark optimization
Flags inefficient PySpark transformations and recommends distributed-processing best practice.
Performance recommendations
Surfaces the specific changes that will move the performance needle, ranked by impact.
Technical-debt reduction
Tracks technical debt over time and recommends a prioritized reduction plan.
Duplicate-code detection
Finds duplicated logic across notebooks, jobs, and pipelines.
Dead-code identification
Flags code and pipelines that are no longer referenced or executed.
Architecture improvement recommendations
Recommends structural changes to medallion layers, jobs, and data models.
Cloud optimization
Identifies inefficient cloud resource usage tied to specific workloads.
Databricks workload optimization
Recommends cluster, Photon, and Delta configuration changes per workload.
Cost optimization recommendations
Ranks optimization opportunities by expected cost impact, not just performance.
Maintainability scoring
Scores code maintainability so engineering leaders can track quality over time.
Continuous modernization recommendations
Delivers an ongoing backlog of modernization work rather than a one-time report.
Integrations
Works with what you already run
See it on your data
Get a walkthrough of Karsient RevoCode
We'll run it against a sample of your own schema or code so you can see exactly what it surfaces.
FAQ
Common questions
No — RevoCode assumes migration is already complete. It continuously analyzes and improves the modernized codebase rather than converting anything new.
The toolkit
The rest of the Karsient product suite
Karsient is a product company too