Hi, I am super excited to launch contextual AI, AI that understands you , your goals and Objectives.

I've been thinking about something that most people using AI every day probably don't pay enough attention to.

Are we spending more on AI than we actually need to?

Think about how we use AI today.

We send almost every query to a premium model. Whether it's a complex reasoning task, a simple rewrite, summarising a document, or generating a quick response, we often end up using the same expensive intelligence.

But does every task really need the most powerful model?

I believe it doesn't.

At Ekva One, we've been working on a different approach.

Instead of simply routing every query to one AI model, we focus on understanding the objective, retaining the right context, and choosing the right intelligence for the task.

The idea is simple:

• Use premium models when the task genuinely requires them.

• Use more efficient models for tasks that don't need expensive reasoning.

• Preserve context instead of repeatedly starting from scratch.

• Optimise token consumption without compromising the quality of the outcome.

Our goal is to help users reduce AI costs by up to 70%.

And we don't want you to be locked into one model.

ChatGPT, Claude, Gemini, Grok and other leading models — one access point, with a shared contextual intelligence decision layer.

We're building Ekva One because we believe the future isn't about using more AI. It's about using the right AI, for the right objective, at the right cost.

We're preparing for our Product Hunt launch on November 6.

https://www.producthunt.com/products/ekva-one?launch=ekva

I'd genuinely love to hear from the community:

Do you actively monitor your AI token consumption and model costs, or do you simply use your favourite model for everything?

Curious to hear how other founders and developers are approaching this.

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