Engagement 02
Data Infrastructure
Before a model or an agent can do anything useful, your data has to be clean, connected, and flowing reliably. We call this data plumbing — unglamorous, but nothing else works without it.
Who this is for
Teams whose data lives in silos, is inconsistent, or isn't yet reliable enough to build models or agents on top of — often the right starting point before Forward-Deployed Engineering or Agentic Systems.
Typical engagement
Scoped in weeks around your highest-priority data sources first.
Why this exists
Most stalled AI projects don't fail because of the model — they fail because the underlying data is scattered across systems, inconsistent, or too unreliable to trust in production. We build the pipelines and infrastructure that make everything downstream possible.
What's included
- Audit of existing data sources, quality, and accessibility across your systems
- Pipeline design to unify and clean data from disparate sources (CRMs, internal tools, logs, docs)
- Reliable, monitored data flows built for production use — not one-off scripts
- Documentation and handoff so your team can maintain and extend the pipelines
Next step
Once your data's ready, most teams move into building.
Forward-Deployed Engineering →