Intura Lands #9 Product of the Day on Product Hunt

Product Hunt has, over the past decade, become one of the most important launch venues in global tech. It is where Notion, Loom, Figma plugins, and countless developer tools first found their early audiences. For AI-native startups in particular, the platform has become a kind of proving ground — a place where the global community of builders, engineers, and product people decides, in real time, whether a new tool deserves their attention.

Launching well on Product Hunt is not a guarantee of long-term success, and it isn't meant to be. What it does provide is a concentrated moment of feedback from exactly the kind of users who matter most to early-stage AI products: people who build with LLMs every day, who feel the friction of model selection and evaluation in their own workflows, and who can quickly tell the difference between a product that solves a real problem and one that doesn't.

For a team building from Indonesia, the platform's global reach matters even more. A strong launch on Product Hunt puts an Indonesian-built product in front of an audience of investors, engineers, and operators across San Francisco, London, Singapore, Berlin, and beyond — a kind of visibility that is otherwise difficult to engineer from this part of the world. That is the context in which we approached our launch. And it is why finishing in the top ten feels meaningful.

The version of Intura that launched on Product Hunt was focused on a specific, sharp problem in the AI builder's workflow: the difficulty of choosing the right LLM, configuring it well, and continuing to monitor whether that choice is still the best one as conditions change.

Anyone who has shipped an AI product knows the loop. You pick a model. You craft a prompt. It works in testing. You ship it. Then costs balloon, latency drifts, a new model is released that's cheaper or faster or smarter — and suddenly you're back at the start, running ad-hoc comparisons in spreadsheets, copy-pasting outputs into documents, trying to remember which prompt version produced which result on which model. Intura was built to replace that chaos with structure.

Our platform helps teams compare, test, and optimise AI models — running real tests against live APIs (OpenAI, Anthropic, Gemini, DeepSeek, and others), capturing response time, token usage, input and output, and surfacing the data needed to make confident, evidence-based decisions about which model to use for which task. The features highlighted in our launch included version control for prompts and model configurations, A/B testing across LLM setups in real time, collaborative workspaces for technical and non-technical team members, performance monitoring, and data-driven optimisation tooling.

That framing resonated. The conversations in the Product Hunt comment thread, the feedback from fellow founders, and the questions from prospective users all confirmed something we already believed: the pain of LLM evaluation is universal, and the appetite for better tooling is real.

When we picked our mascot — a rabbit — it wasn't just for the visual charm. It was a deliberate reference to Theseus, the maze-solving mechanical mouse built by Claude Shannon in 1950, one of the earliest demonstrations of a machine learning to navigate complexity through trial, memory, and adaptation.

For us, the rabbit represents the same spirit translated to the modern AI builder's reality: the curious, twisty, often non-linear journey of finding the right model setup for a given user, a given task, a given moment. You try one path. You hit a wall. You back up. You try another. Eventually, with the right tools and the right data, you find the way through. That metaphor sits at the heart of what Intura is building — and the response to it on Product Hunt suggested it landed with the audience we hoped to reach.

In a typical day on Product Hunt, hundreds of products launch. The leaderboard is fiercely competitive — dominated by well-funded startups, established companies releasing new features, and global brands with significant marketing reach. To finish in the top ten as a first-time launch from a small team is, by any reasonable measure, a strong result.

But the rank itself is only part of the story. What we paid attention to more closely was the quality of the engagement: the depth of the comment threads, the specificity of the questions, the kinds of users who upvoted and followed. The community that gathered around our launch was not casual. It was made up of people who understood the problem, who had felt the pain themselves, and who wanted to know exactly how we were solving it. That is the kind of audience that compounds over time. And it is the kind of audience we built Intura for.

Product Hunt was, for us, a beginning rather than a destination. The launch confirmed something important — that the problem we're working on resonates with the global builder community, and that there is real demand for the kind of structured, decision-grade tooling we're putting into the world.

In the months since, the Intura platform has continued to evolve — expanding from LLM evaluation tooling into the broader category of AI-native brand intelligence, where the same underlying capabilities (real-time model evaluation, structured comparison, decision-grade analytics) are now applied to a different and equally urgent problem: helping brands understand how they are perceived in the AI era, across AI search, social listening, and e-commerce signals.

That evolution would not have been possible without the early signal we received on Product Hunt — the validation that the technical foundation we were building had real value, and that the team behind it could ship something the global community wanted to engage with. We are grateful to every person who upvoted, commented, followed, or shared our launch. Finishing #9 Product of the Day was a moment we will remember. What we do with it — and what we build next — is what will actually matter.