Use casesSaaS product teams
SaaS product teams
Ship AI features without building the platform under them.
Your roadmap needs the feature, not six months of retrieval plumbing and policy work. We give product teams a governed substrate to build on, and stay out of the product decisions.
Measured
6 wks
From kickoff to a governed AI feature in production
Connects to
REST and SDKpgvectorKubernetesTerraform
Where it hurts today
Retrieval, evaluation and guardrails rebuilt per feature
Model pricing changes rewriting your unit economics overnight
Enterprise buyers asking security questions you cannot answer yet
Prototypes that never survive contact with real tenants
What changes with Zitrino
Retrieval, memory and policy available as services from day one
Model routing behind an interface, so swaps are configuration
Tenant isolation, logging and DPA answers ready for procurement
A path from prototype to multi-tenant production that is already walked
Capabilities
What we actually deliver.
Four pieces of work. Each one ships on its own and earns its place before the next is started.
01Modular APIs
Retrieval, agents, evaluation and policy exposed as endpoints you compose, rather than a framework you adopt.
02Model routing
Providers sit behind one interface with fallbacks and cost ceilings, so a price change is not a migration.
03Tenant isolation
Per-tenant keys, data boundaries and retention rules, verifiable in the logs you can show a customer.
04Embedded delivery
Our engineers work inside your repo and your review process, and hand back code your team maintains.
Built for this
The products we put in front of this problem.
Both are already in production elsewhere. Open one to see where it fits in this workflow.
Agentic platform on any model
Explore the productSee it on your own data, in days.
Tell us how the work runs today. We will come back with a demo on a slice of your environment and an honest read on what it takes to put it live.