Turn dirty imaging archives into trustworthy, AI-ready data
Whether data is migrating to the cloud or flowing through daily workflow, Datamonk analyzes, cleans and normalizes imaging metadata at scale — with clinical teams approving the edge cases.
Studies resolved automatically
Studies processed per day
Faster go-live than traditional projects
Backed by Healthcare Pioneers:
Dirty imaging data is costing you every single day.
Priors that don't surface at read time. The same study described differently at every site. Research datasets that fail validation. AI models misrouting studies on inconsistent descriptions. And the classics every archive knows: thousands of imports labeled only “outside study,” copy-paste mistakes repeated ten thousand times.
And at every migration, data quality drives most of the work and cost.
The usual approaches don’t fix the data
We understand every study, then fix it
Tokenized input
"CT" · "petrosum"
Concepts understood
CT → Computed Tomographypetrosum → Petrous Bone
Issues found
Laterality — missing
AnatomicRegion — missing
Validated output
CT of petrous bones
SNOMED 241522007
The platform learns from every study
The platform has already seen the naming habits of dozens of vendors, languages and sites — issues are recognized, not rediscovered on your archive.
Detection and automation improve with every project: more studies fixed automatically, fewer needing manual review.
Everything is auditable end to end: every fix traceable, every clinical approval logged.
Works at migration, or just every day
Where teams use Datamonk
PACS-to-PACS migration
Full archive moves on vendor switch, data normalized in transit.
Cloud & VNA consolidation
Decades of multi-vendor archives into one clean cloud archive.
Multi-site harmonization
One naming standard across hospitals after mergers and network formation.
Research & AI dataset prep
Archives delivered analysis-ready: normalized, coded, validated.
Continuous data quality
Keep new studies clean after go-live — same checks, applied at ingest.
Migration assessment
Pre-sales archive scan: error rates and effort estimate before the project starts.
PACS vendors & integrators
Your migration factory. Win deals on go-live speed, run installed-base programs on software margins, keep the customer relationship.
Hospitals, imaging networks & research teams
Migrating, consolidating, or preparing data for research and AI? Start with your own archive.
Built for clinical data from day one
Dedicated regions with a dedicated tenant per hospital. GDPR- and HIPAA-native.
Find out what's actually in your archive
Most engagements start small: a Data Quality Report on 10,000 studies from your archive — any modality, any vendor. Full DICOM-hierarchy analysis, sample corrected files, delivered in 4–6 weeks. It begins with a 30-minute intro call.