See Komprise AI Preparation and Process Automation (KAPPA) data services examples and demonstration. Bring your custom data function. Komprise automates the rest.
Custom Metadata Extraction.
Any File Type. Petabyte Scale.
KAPPA data services (Komprise AI Preparation and Process Automation) deliver serverless metadata enrichment for unstructured data. Customize pre-built metadata extraction KAPPA functions to your use case. Examples include:
FASTQ Metadata Extraction
Image EXIF Metadata
OSDU / LAS Files
DICOM to JPG to S3
ESIF (Coming Soon)
ELN Extraction (Coming Soon)
PDF Metadata (Coming Soon)
Don’t see what you need? It’s easy to add a KAPPA function. Learn More.
Serverless Execution
Write a few lines of Python to define what to extract. Komprise provisions, scales, and executes the function across petabytes of file and object data with no infrastructure overhead.
Global Metadatabase
Every enriched tag feeds into the Komprise Global Metadatabase, making it instantly searchable with Deep Analytics and available for Smart Data Workflows and agentic AI pipelines.
Reusable Library
Komprise and its partners publish a growing library of pre-built data services that IT teams can configure for their specific requirements, covering industry-standard formats and enterprise-specific workflows.
KAPPA Data Services Library
Each data service below shows a real KAPPA use case, the business problem it solves, and the key benefits for IT and data teams.
DICOM Header Extraction
DICOM files carry rich clinical metadata in their headers: patient ID, study type, scanner model, imaging protocol. The storage layer sees none of it. KAPPA extracts that context as searchable tags, making imaging datasets AI-ready without moving a single file.
- Filter CT and MRI datasets by clinical criteria for AI training
- Maintain HIPAA governance with PII tagging before AI ingest
- Preserve metadata as data moves across storage tiers
- Search imaging archives using real clinical criteria at scale
DICOM
FASTQ Metadata Extraction
Genomics pipelines generate petabytes of sequencing data in FASTQ format, designed for bioinformatics tool compatibility but not AI-scale analytics. KAPPA extracts sequencer metadata, quality scores, sample IDs, and project codes to enable precise dataset curation without disrupting research workflows.
- Identify and archive interim files from completed projects
- Filter low-quality reads before AI pipeline ingest
- Reduce genomics storage footprint with metadata-driven tiering
- Support population-scale research with unified metadata search
.bam
Image EXIF Metadata Extraction
Post-production workflows strip embedded metadata from digital media assets, severing context from content. KAPPA reads EXIF, XMP, and IPTC headers at ingest, preserving camera settings, rights information, and production context as persistent, searchable tags across your media storage estate.
- Restore lost production context from post-processed files
- Enforce rights and licensing metadata at petabyte scale
- Build AI-ready media catalogs with accurate content tags
- Search by shoot date, camera model, or rights status
XMP
IPTC
OSDU / LAS File Metadata Extraction
LAS (Log ASCII Standard) files store well log data across proprietary platforms, making cross-vendor discovery nearly impossible. KAPPA extracts OSDU-compliant metadata from LAS files, delivering vendor-neutral discoverability of subsurface data across the enterprise with no platform migration required.
- Search well logs across platforms without vendor lock-in
- Apply OSDU standard tags for enterprise-wide compliance
- Feed curated subsurface datasets to geoscience AI models
- Operate at scale without disrupting existing workflows
OSDU
DICOM to JPG for AI Training
Clinical AI initiatives demand large, well-curated imaging datasets. DICOM files are distributed across multiple storage systems, mixed with studies from different modalities, body parts, and institutions. KAPPA builds a metadata-driven pipeline that runs itself: Deep Analytics filters the Global Metadatabase to maintain a precise, always-current dataset of chest CT scans, and a single KAPPA function converts those images to JPG and delivers them to S3 on schedule, as new data arrives, without manual steps.
- Build continuously refreshed AI training datasets from DICOM archives without manual curation
- Filter by clinical metadata to target specific modalities, body parts, or institutions
- Convert DICOM to JPG and deliver to S3 with a single KAPPA function
- IT and data science teams operate from one interface with no custom scripts or fragile export jobs
JPG
S3
More Data Services Coming
ESIF, ELN Metadata Extraction, PDF Metadata Extraction, and additional industry-specific data services are in development. Check back for new additions to this library.
How to Define a KAPPA Data Service
KAPPA data services run on Python — one of the most widely adopted languages in data engineering.
Use an Existing Library
Python has thousands of open-source libraries already available for reading domain-specific file formats. In most cases, a library that handles the extraction you need already exists. Import it, define the fields you want to extract, and Komprise handles execution across your entire data estate at scale. No infrastructure to build. No pipelines to maintain.
Write Your Own Extraction Logic
If a pre-built library does not exist for your file format or use case, you can write the extraction logic directly in Python. Define what to read from the file, what to tag, and what to load into the Global Metadatabase. Komprise handles the rest. Visit komprise.ai/KAPPA to learn more or reach out to your partner or account team to set up a KAPPA preview.
Ready to Enrich Your Unstructured Data for AI?
See how KAPPA data services can extract the metadata your AI models need, in hours, not months.