We build AI agents that close the ticket, not the demo.
Orivia is an agentic AI engineering firm. We design the orchestration, wire the tools, prove it with evals, and run it in your cloud - with the software and QA engineering to keep it standing.
An assistant answers. An agent finishes.
Plans the task, acts through permissioned tools, cites its evidence, and knows when to stop. That's the whole platform, on one page.
Software for logistics, finance and IT operations.
Purpose-built software for the systems you already run, plus the tooling to verify who you're doing business with.
Everything an agent needs around it.
AI is the point, but agents fail without solid engineering underneath. One squad covers both.
Agentic AI systems
Multi-agent workflows that plan, act and verify — with the boundary you set.
Applied AI
Models pointed at a named workflow, with retrieval quality treated as an engineering problem.
Software engineering
The products agents live inside — built to carry real transaction volume.
Testing, QA & evals
Two scoreboards: one for the software, one for the model. Both run in CI.
AI platform & DevOps
Infrastructure as code, model gateways, observability, and a cloud bill you can explain per task.
AI strategy
Which workflows to automate, in what order, and which ones AI should not touch.
The part that decides whether it survives contact with production.
Before an agent takes live traffic, it's scored against examples from your own workflow. After launch, the same scoreboard keeps running.
Where the agents already know the vocabulary.
Regulation, legacy systems and peak-season load shape an agent's design more than the model choice does.
What buyers ask before the first agent.
Four of the most common questions. The full list, and a way to ask your own, live on the contact page.
See all FAQsIt plans a task into steps, calls your systems through tools, checks its own output against evidence, and either completes the work or escalates with a rationale. A chatbot returns text; an agent returns a finished outcome and an audit trail.
Agents only reach systems through typed tools with scoped permissions and hard limits: approval boundaries, rate caps, allowed record types and blast-radius rules. Anything outside the boundary stops for human approval, and every run is replayable from the log.
We define an eval set before writing the agent, then gate releases on task success, faithfulness, correct escalation, latency and cost. A controlled pilot only happens when the evidence supports it.
Whichever model fits the step, security needs and evaluation results. The system can route across providers, use private endpoints, or use your preferred stack when that is the right choice.
Not ready to build? Start with the free AI readiness review.
Send one workflow. We'll come back with an honest read on whether an agent helps it, what it would need to prove, and what to do instead if it doesn't.