Recommendation & Personalisation for SaaS & Technology

Orqent Labs builds recommendation systems evaluated by controlled experiment, reported against revenue or engagement rather than a leaderboard metric.

In saas & technology, per-tenant economics and enterprise security review decide whether a feature can ship. That single fact reshapes how recommendation & personalisation has to be built here, the guardrails, the approval points and the evidence trail are design inputs rather than things bolted on before go-live.

The workload we are most often asked to take on first is usage-based metering for AI, usually integrated against customer data platform. We start from the constraint, not the capability, what the system must never do, who signs off, and what happens when it is wrong.

Multi-model by default, so a provider outage is a routing decision rather than an incident. You own the code, the models where they are open-weight, and the documentation to run it without us.