Rapid development of custom data functions to automate infrastructure and execution across large unstructured datasets.
AI needs high-quality data. Valuable context for unstructured data is trapped and unreachable. Komprise simplifies extraction and metadata enrichment tailored to your unique requirements with Komprise AI Preparation & Process Automation (KAPPA) data services. No more bulky plug-ins and connectors. Extract metadata in hours, not months.
KAPPA (Komprise AI Preparation & Process Automation) is a serverless data services framework for extracting, enriching and orchestrating metadata across unstructured enterprise data for AI workflows. This solution brief provides a KAPPA data services overview and reviews the key benefits, including:
- Easy definition and customization of data services
- Global search and metadata curation and extraction
- Built for autonomous agentic AI data orchestration
- Customizable setup and lifecycle management
- Reusable library of data services
KAPPA data services allow us to enrich file metadata and customize it to our clients’ needs with incredible ease and speed, while also conforming to their security models. This new functionality from Komprise is making a tremendous difference in the outcomes of our projects.
– Aaron Cardenas, CEO of P1 Technologies
Learn more about KAPPA data services.
What is Komprise KAPPA and why is it important for AI?
KAPPA (Komprise AI Preparation & Process Automation) is a serverless data services capability that helps organizations enrich, transform, and prepare unstructured data for AI at scale. It enables custom metadata extraction, tagging, masking, and processing across petabytes of file data without managing infrastructure. Read the press release.
How does KAPPA help reduce the cost and complexity of AI data preparation?
Many organizations spend months building scripts, connectors, and manual workflows to prepare unstructured data for AI. KAPPA simplifies this by allowing teams to run lightweight custom functions across massive datasets while Komprise handles orchestration, scaling, and execution. This reduces time-to-value and lowers operational overhead.
Why is unstructured data management essential for AI success?
Most enterprise data useful for AI lives in files, documents, images, PDFs, and other unstructured formats spread across NAS, cloud, and SaaS silos. Komprise helps discover, classify, and curate this data first, then KAPPA applies custom processing so only relevant, trusted data reaches AI pipelines.
Can KAPPA improve AI governance and compliance?
Yes. KAPPA can automate tasks such as sensitive data masking, metadata tagging, policy enforcement, and contextual enrichment before data is used in AI workflows. This helps reduce the risk of exposing private or regulated content to AI tools.
How does KAPPA differ from traditional ETL tools?
Traditional ETL platforms are typically built for structured databases and fixed pipelines. KAPPA is purpose-built for unstructured data and lets organizations run custom file-level actions across distributed storage environments, making it better suited for modern AI data preparation use cases.