Data pipelines and data ownership
Your AI is only as good as the data you actually own
Most AI projects stall on data trapped inside vendor software. We get it out first, verify it, and keep it current. Then everything else gets easier.
The short answer
A data pipeline continuously copies records from your business systems into a database you control, where they can be queried, reported on, and used by AI. It also protects you from vendor lock-in, because leaving a platform no longer means losing your history.
What this stops
- Reports you cannot build because the vendor will not export it
- Ten years of history that disappears if you cancel
- One vendor holding the only copy of anything
- AI projects stuck waiting on clean data
What we build
What this looks like in practice
- Continuous mirrors
- Ours keeps 2.7 million rows across 78 data domains current from a field service platform.
- File capture
- Recordings and documents a vendor shows you but never exports, captured and hashed.
- Verified backups
- A second copy at a different company with different credentials, checked at both ends.
- Reporting layers
- Clean tables your dashboards and AI tools can trust.
The rule on this one
One copy is not a backup. Anything irreplaceable goes to a second vendor and gets verified by checksum, and we test the restore.
CF-01 · Case file
Ten years of call audio had exactly one home. Now it has two
A multi-branch home services company.
- 2.7M
- rows mirrored
- 78
- data domains
- 188,832
- files on a second vendor
- 172.1 GiB
- independently backed up
Questions
What people ask us
Often built alongside
Can we get our data out of our software vendor?
Usually more than you think, through APIs and exports, and often less than you need. Check your contract too, because some terms limit how data can be collected. We review that before we build.
Why does AI need a data pipeline?
Models answer well only when they can see accurate, current, connected data. If your records sit in separate vendor tools, AI can only work with what you paste in. A pipeline gives it one reliable source.
How long does a data mirror take?
A single source can be live in a few weeks. A full mirror across dozens of data domains takes months, mostly because of rate limits and the time needed to verify it against the source.
Tell us what is eating your week
A free discovery call with a solutions architect, or a blueprint in about a minute.