Production AI on your data — inside your perimeter.
We build and maintain CalQuity engine deployments for regulated organisations. On your hardware. On your data. The data doesn't leave.
What we build
Four capability surfaces. One engine underneath.
Self-hosted foundation models
Foundation models on your hardware — no model traffic crosses your perimeter.
Proprietary RAG pipelines
Retrieval against your documents with entitlements baked in and source citations.
Multi-agent workflows
Due-diligence packs, discovery review, prior-auth ops — composed agents, not chatbots.
Governance and audit
Audit logs, entitlements, no-training guarantees — replayable per query and export.
How we engage
Three modes. Pick the one that matches your risk model.
4–6 weeks
Prototype
Focused build on one workflow. IP transfers on completion.
When
Prove it works on your data before the larger build.
4–6 months
Build & IP Transfer
Complete production deployment — code, index, workflows owned by you.
When
Production AI you operate long-term.
Ongoing
Build & Maintain
We operate with you — model updates, governance, capability extension.
When
Production AI without running an AI team.
From CalQuity · Worldview
The cloud-AI question is solved for the wrong people.
For most teams, AI infrastructure is a solved problem. For some teams, that exchange is impossible — the data cannot leave.
The right answer is the engine running where the data already lives. CalQuity built the engine in production for institutional finance research. Labs is how it deploys for your firm, fund, or hospital.
Get started
Start a CalQuity Labs engagement.
Tell us what you'd build inside your perimeter. We respond within one business day.