Akhara AI - Verification for Enterprise AI

Hi, we're Akhara AI.

So Akhara is building the verification stack that makes AI agents deployable in high stakes enterprise workflows.

Most enterprise AI pilots fail to deliver measurable business impact because companies struggle to govern these non deterministic agents.

For example, a support agent may sound helpful and perform well most of the time, but a single incorrect action can create financial loss, regulatory exposure or even cause customer harm.

What Akhara does is converts enterprise rules into machine checkable verifiers.

We simulate agents against those policies before deployment and enforce the same runtime checks, detecting drift and blocking unsafe actions before they execute.

To demonstrate this, imagine a financial services company has launched a credit card support agent.

The company built a voice agent using 11 labs.

Customers can call the agent to ask questions such as what is my current balance?

When is the next payment due?

now some of these requests only require the agent to retrieve information.

But actions like can I cancel my card?

Can cause serious consequential changes to the customer's financial account.

Now let's test this agent in our simulation.

In our simulation environment we have a run here, where we have a simulated user that is trying to cancel their card ending in 4222.

The agent starts asking for the full name of the user before it actually cancels this card.

Now at step seven it starts to realize essentially that the user is not who they say they are.

The user is impersonating their wife and trying to cancel their credit card.

The agent fails to detect that goes ahead, looks at the card and ends up actually canceling this credit card.

Now this is an example of unverified path, something that can happen even in production.

And the way to traditionally catch this right now today is via observability platforms.

They record the conversation, flag the issue later and after the fact that the incorrect action has already occurred.

At scale, this can lead to significant financial leakage and inconsistent policy enforcement, where customers can learn how to exploit the agent.

In our environment we are already able to detect that there is a missing policy that needs to be attached to this agent.

And that missing policy is to be able to do comprehensive KYC checks before a severe action like canceling someone's credit card.

Now we use the source, which is the enterprise knowledge and policies to detect and code, verifiers.

Our verifiers are essentially deterministic, tests that the agent has to pass before it actually can call, cancel card, which is a tool.

Call over here, before cancel card.

There are various fields that the agent has to collect, like the name, date of birth, and potentially an OTP and address as well.

let's add this to our agent now, which sits now in the policies that are latched to this agent.

Once the policy is latched at runtime, when we encounter the same scenario where a user is trying to cancel the card and the, user tries to impersonate someone else, now our latch kicks in, which is the KYC identity gate before the irreversible action that is to cancel the card.

And the verdict here is to block the card.

Because important KYC information such as date of birth, OTP address are not present for this action to go through, the agent tries, as you can see here, to collect that necessary information.

When the agent is unable to collect all the necessary information that satisfies our, deterministic verifier, it issues that it is unable to cancel the card, which is the right behavior.

The key difference here is simple.

Without Akhara, the enterprise discovers that the agent made the wrong decision after the action has already occurred.

And with our, software, the action is evaluated against enterprise policy before it executes.

We're not replacing the company's agent and model or business logic, but we are providing this independent verification layer between the agent intent and and consequential enterprise action.

This is how we help move companies from experimenting with agents to deploying them safely at scale.

Thank you.