Data & integration
Every AI engagement that stalls, stalls here. The data lives in four systems, two of them disagree, and the definition of 'active customer' depends on who you ask. We fix that layer first.
What you get
Pipelines, warehouses, and system integrations that make your data usable — because no model, agent, or dashboard is better than what feeds it.
01Source-of-truth mapping
Which system owns which field, where the duplicates come from, and what breaks when they disagree. Documented, not tribal.
02Ingestion with contracts
Typed schemas, validation at the boundary, and quarantine for bad records — so one malformed vendor feed doesn't poison a quarter of reporting.
03Transformations under version control
dbt models, tested and reviewed like application code. Metric changes come with a PR and a diff, not a Slack message.
04Integration without the brittleness
Idempotent syncs, replayable events, and dead-letter handling. Integrations fail; the question is whether they fail loudly and recover cleanly.
05Retrieval-ready data
When the AI work starts, the corpus is already chunked, embedded, permissioned, and refreshing on a schedule.
Shapes this usually takes
Most engagements take one of these three shapes. Which one fits depends on how well-defined the problem already is — tell us the situation and we'll say which, and what it would take.
Data audit
A written assessment of your sources, quality, and what it would take to make them AI-ready.
Contact usPipeline build
Ingestion, warehouse, transformations, and monitoring for a defined set of sources.
Contact usIntegration project
Two or more systems synced properly, with reconciliation and exception handling.
Contact usWhat we reach for
Boring where boring works, current where it matters. We pick for what your team can maintain after we leave — not for what looks good in a case study.
Where we've done this
Common questions
Do we need a warehouse before we can do AI?
Not always. Plenty of useful automation runs directly off operational systems. But if three teams report different revenue numbers, that will surface in your AI outputs too — and it'll be blamed on the AI.
Can you work with our existing data team?
Gladly. We often come in to build the layer they don't have bandwidth for, then hand it over with the conventions they already use.
What about legacy systems with no API?
We've screen-scraped, parsed fixed-width files, and driven a mainframe terminal session. It isn't elegant, but it's often the fastest honest path.
Is data & integration the right move for you?
Send us the situation. We’ll come back with a read on whether this is the right service, a rough range, and what we’d want to learn first.