Enterprise Data Migration
Move data without breaking the business.
Every mid-market company eventually has a data migration problem they can't ignore. A vendor gets acquired and the new owner starts raising prices. A merger lands two organizations on different systems. A long-promised platform replacement finally has a real deadline. A SaaS product gets sunset and the email arrives.
Migrations look like a one-time project on the slide deck. They almost never are. Real migrations are:
- Multi-month operations, not weekend cutovers
- Per-organization or per-customer, not all-at-once
- Auditable, with the ability to prove what moved, what didn't, and what failed
- Reversible, because something will always need to be re-run
- Synchronizing, because the old system has to keep working while the new one comes online
PKG builds migration engines that handle all of that. Not scripts. Engines.
What we build
A migration engine is a long-running operator that:
- Runs on a schedule (every 15 minutes, every hour — whatever the data rate justifies)
- Pulls a defined batch of work from a queue (an organization, a customer, a department)
- Executes the migration steps in order, with each step idempotent
- Records every action in an audit log
- Handles failures with backoff and retry, escalating to humans when retry won't fix it
- Can be paused, resumed, or rewound by the operations team without a deployment
Underneath it sits the canonical Postgres data store and the operations admin UI from our applications capability — so the customer's team can see exactly what's happening at all times.
What we've migrated
Patterns we've built and run:
- LMS-to-LMS — organization-by-organization migration between two completely different learning platforms with different data models, including learning paths, courses, sections, learner enrollments, and franchise-specific content overrides
- Database synchronization — keeping two operational systems consistent while a multi-month cutover runs
- Multi-source aggregation — pulling data from a dozen vendor APIs into a unified canonical store, with daily incremental sync
- Master-data protection — enforced safety guards that prevent accidental modification of golden-record templates while migrating customer-specific copies
What we don't do
- One-shot exports. If your migration is "export a CSV, transform it, import it once," you don't need PKG; you need a junior engineer for two weeks. We build engines for migrations that run for months.
- Lift-and-shift without remodeling. We model the data around your business in the canonical store, not around either vendor's schema. Otherwise the migration is just a re-platform of the original problem.
- Migrations without audit trails. Every action is logged. Every failure is traceable. If your auditor asks what moved, the answer is in the database, not in someone's memory.
How this fits with the rest
Most migration engagements end up needing the rest of the stack: a private Kubernetes cluster to run the engine, a custom application to operate it, a canonical data store that becomes the foundation for ongoing reporting and AI analysis. That's the Private AI Data Platform — and a migration engagement is often how it starts.