See Iris Control on your LLM stack
A 30-minute walkthrough for CTOs and platform teams. We map Iris Control to the models, products, and tenants you run today.
On the call:
- Routing and automatic failover across the providers you use
- Data scoping per product or tenant, with a log of every call
- Evals on your own data and grounding checks before answers ship
- A pilot plan for one production use case
Works with the models you already use
From the team behind IrisAgent, running AI in production for Dropbox, Zuora, and Tekion.
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What Iris Control does for your platform team
One layer for every LLM call, across every product, tenant, and provider. More on the product: Iris Control.
Stay up
Route each use case to the right model and fail over automatically when a provider degrades, so a live AI feature doesn't go down with it.
Stay in bounds
Keep every call inside the product or tenant data it is allowed to use, limit which tools each workflow can call, and log every prompt and answer.
Stay right
Pick models by how they score on your own data, and check answers against approved sources before they reach a customer.
One layer you own
Policy lives in one place your platform team runs, instead of in the gaps between a gateway, a logging tool, an eval notebook, and per-app guardrails.
What happens after you book
No long sales cycle to find out if it fits.
Book a time
Tell us which models and LLM features you run today. We tailor the walkthrough to your stack.
30-minute walkthrough
We walk your platform team through failover, data scoping, and evals on a use case like yours.
Pilot on one use case
If it fits, we put Iris Control in front of one production LLM feature and measure it on your traffic.
FAQ
Short answers for CTOs and platform teams.
Who should join the call?
The CTO, VP of Engineering, or head of AI platform, plus an engineer who owns your LLM integrations. Product leads for customer-facing AI features are welcome too.
What should we prepare?
Nothing formal. A rough list of the models you use, the LLM features in production, and how you route and log calls today is enough.
Can we try Iris Control before the call?
Yes. The live demo runs on real models with no signup: take a model down, scope a call to one product, and run a grounding check on a made-up answer.
How is this different from an AI gateway?
A gateway is a proxy: one endpoint, many models, and failover. Iris Control does that job and adds the policy around it: data scoping, tool-use limits, evals on your data, and grounding checks on answers.
See Iris Control on your own LLM traffic.
Book your Iris Control demo