Enterprise AI solutions engineered for complex, multi-team organizations: data governance, access control, and AI systems that fit into existing infrastructure.
Enterprise AI fails on integration, not ambition
Most enterprise AI pilots die in the gap between 'works in a demo' and 'integrates with our identity provider, respects our data access rules, and passes our security review.' The AI itself is rarely the hard part; the surrounding infrastructure discipline is.
What an enterprise engagement covers
- AI systems that respect existing data governance and access-control boundaries
- Integration with existing identity providers, warehouses, and internal tools
- Audit logging and explainability built in for compliance and internal review
- Multi-team rollout planning so adoption doesn't stall at pilot scale
- Security review support ahead of production deployment
How we work
Map existing infrastructure and governance constraints before designing the system
Design for the security review from day one, not as a late-stage hurdle
Pilot with one team on real data, with clear success criteria
Scale the same architecture across teams rather than rebuilding per department
Typical stack
Frequently asked questions
Integration with existing identity, access control, and data governance systems; audit logging sufficient for compliance review; and rollout planning across multiple teams rather than a single pilot. The underlying AI techniques are often the same the surrounding infrastructure discipline is what changes.
A working pilot on real data with one team typically ships in 6–10 weeks. The longer variable is security and governance review, which depends entirely on your organization's process we design for that review from the start rather than treating it as a late surprise.
Yes enterprise AI work is largely integration work. We build against your existing Snowflake/BigQuery warehouse and SSO/identity provider rather than standing up parallel infrastructure the AI system would need to be reconciled with later.