AI-POWERED PACS & VNA MIGRATION

Migrate imaging archives that arrive clean

The platform reads every study, fixes errors and gaps with clinical approval, and delivers a normalized, SNOMED-coded archive to the new system — in months, not years.

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See how it works ↓

Two ways to run a migration

What changes when software does the work

A migration that learns, fixes and validates before it moves.

Traditional migration project

12+ months of consultant hours

Hourly billing, open-ended scope

Quality depends on who shows up

Progress lives in status emails

Moves data, checks a handful of DICOM tags

The archive arrives moved, not fixed

Migration with Datamonk

Live in months — 50,000+ studies per day

Per-study pricing that drops as archives grow

One clinical approval fixes thousands of studies

Live dashboard of status and open items

100+ tags analyzed and fixed before a study moves

The archive arrives normalized and SNOMED-coded

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.

Every kind of archive move

Any PACS vendor and format, on both source and target side.

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 migrations

One naming standard across hospitals after mergers and network formation.

Multi-site migrations

One naming standard across hospitals after mergers and network formation.

Patient deduplication

Duplicate patient records detected and merged during the move. One patient, one record.

Patient deduplication

Duplicate patient records detected and merged during the move. One patient, one record.

Start with a Migration Readiness Report

10,000 studies from your current archive — any modality, any vendor. Error rates, data quality findings and an effort estimate, before the project starts. It begins with a 30-minute intro call.

Book an intro call