Erhan Giral

speaker

389 appearances 1 recordings 1 series first heard Aug 2026 last heard 3 Aug

Erhan Giral’s voice in public audio — every appearance, attributed to the second.

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Recordings per month over the last 12 months — 1 in all, peaking in Aug 2026 with 1.

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Eye On A.I. · AI Agents Fixing Your IT Before You Even Know Something Broke | Erhan Giral & Ryan Manning, BMC Helix · 3 Aug 2026 podcast

So it certainly was like that so that you could cherry pick the services that the platform offers you.

But in the agentic world, we are quickly migrating everything towards agents nowadays.

This actually is a lot smoother and transparent from the point of view of the user.

Because they just naturally conversate.

They say, hey, I'm trying to do this, I'm trying to do that.

And there is sufficient intelligence baked into the agentic layer of the platform that it knows how to delegate these to all the other bots and models and what have you.

So we have a big umbrella term, we call it Helix GPT, which is essentially all the generative, all the reasoning, all the analysis tasks that the platform is generating is then routed to either a model provider of the user

or one of the models we host for the user either on their own premises or in their private clouds versus maybe a cloud offering of choice.

So this is the flexibility Ryan was talking about.

We actually, you know, we are able to deploy the components pretty much everywhere, including on premise.

So we have a...

rather sophisticated routing layer that knows what workload goes where, and it does all the metering and all the compliance and whatnot.

I would say you can run us in any cloud or hybrid or on-premise scenario.

So from that perspective, we do have a unique stance, I suppose.

meaning a lot of organizations are pretty nervous about sending their most intricate IP and data to third-party AI vendors because they see how capable these models are.

They can quickly learn things.

So there is a lot of interest in being able to run this level of intelligence in an air-gap manner, if you will.

so that nothing leaves that organization's boundaries.

So for that reason, we built a lot of scaffolding around these models so that A, they run very efficiently.

We look for parameter efficient models as much as possible, meaning I need to be able to run all this intelligence on

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