Data quality infrastructure for healthcare imaging

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.

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95%+

Studies resolved automatically

45k+

Studies processed per day

3 -

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

Traditional migration projects

12+ months, hourly billing, and quality that depends on who shows up. The archive arrives moved, not fixed.

Traditional migration projects

12+ months, hourly billing, and quality that depends on who shows up. The archive arrives moved, not fixed.

Traditional migration projects

12+ months, hourly billing, and quality that depends on who shows up. The archive arrives moved, not fixed.

Generic migration tools

Automated way to move the mess faster. Dirty in, dirty out — the new PACS inherits every error of the old one

Generic migration tools

Automated way to move the mess faster. Dirty in, dirty out — the new PACS inherits every error of the old one

Generic migration tools

Automated way to move the mess faster. Dirty in, dirty out — the new PACS inherits every error of the old one

Building it in-house

Imaging metadata is a specialist problem: dozens of vendors, languages and local naming conventions. Teams that try spend years rebuilding what already exists.

Building it in-house

Imaging metadata is a specialist problem: dozens of vendors, languages and local naming conventions. Teams that try spend years rebuilding what already exists.

Datamonk is the data quality infrastructure for healthcare imaging — proven on petabyte-scale archives.

We understand every study, then fix it

1 Understand

The platform reads every word of metadata across languages, vendors and abbreviations. Most studies resolve on lists, aliases, fuzzy matching and trained models; AI agents handle only the edge cases.

1 Understand

The platform reads every word of metadata across languages, vendors and abbreviations. Most studies resolve on lists, aliases, fuzzy matching and trained models; AI agents handle only the edge cases.

2 Map

Each token maps to a curated medical concept database, building full clinical context per study.

2 Map

Each token maps to a curated medical concept database, building full clinical context per study.

3 Detect

Missing and conflicting fields are found using all available information across the study.

3 Detect

Missing and conflicting fields are found using all available information across the study.

4 Validate

Every proposed fix is checked against the pixel data: the image itself. Clinical teams review grouped edge cases; no change is applied without approval.

4 Validate

Every proposed fix is checked against the pixel data: the image itself. Clinical teams review grouped edge cases; no change is applied without approval.

4 Validate

Every proposed fix is checked against the pixel data: the image itself. Clinical teams review grouped edge cases; no change is applied without approval.

Worked example — one tag

StudyDescription: "CT petrosum"

Tokenized input

"CT" · "petrosum"

Concepts understood

CT → Computed Tomography
petrosum → 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

In the daily workflow

Every new study caught and corrected at ingest. Priors surface, multi-site data reads consistently, and radiologists get 30–90 seconds back per study.

In the daily workflow

Every new study caught and corrected at ingest. Priors surface, multi-site data reads consistently, and radiologists get 30–90 seconds back per study.

At migration

Archives arrive at the new PACS or cloud normalized, SNOMED-coded and validated — in months instead of years.

At migration

Archives arrive at the new PACS or cloud normalized, SNOMED-coded and validated — in months instead of years.

For research & AI

Cohort selection starts with a query: consistently described studies are findable, traceable, and trustworthy across the archive. Pre-labeled instances turn annotation into review.

For research & AI

Cohort selection starts with a query: consistently described studies are findable, traceable, and trustworthy across the archive. Pre-labeled instances turn annotation into review.

Explore migration

Explore research & AI

Where teams use Datamonk

PACS-to-PACS migration

Full archive moves on vendor switch, data normalized in transit.

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.

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.

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.

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.

Talk to Partnerships

Hospitals, imaging networks & research teams

Migrating, consolidating, or preparing data for research and AI? Start with your own archive.

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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.