Intura Selected as Top 10 at Google for Startups AI Solutions Lab Indonesia

The Google for Startups AI Solutions Lab does not recruit broadly. Its first Indonesia cohort was assembled through a rigorous evaluation of AI product depth, market understanding, and founding team capability. Applicants arrived from across the archipelago, spanning verticals from healthcare and logistics to legal AI and consumer analytics. The panel was not looking for the most polished pitch decks — it was looking for the most credible AI theses, backed by evidence of real product progress.

Intura's thesis had always been a specific one. As AI reshapes the consumer discovery journey — replacing keyword searches with conversational AI queries on Gemini, ChatGPT, and their successors — the question of how a brand is perceived inside these models has become the most consequential unanswered question in modern marketing. We had been building toward an answer. The acceptance letter confirmed that Google's AI team saw the same problem we did.

The confirmation form came with a deadline: 3PM on November 18th. We signed it within the hour.

The AI Solutions Lab was structured around a simple and demanding premise: in four days, each startup would move from concept to a working AI product, evaluated by a panel of judges on December 1st. The programme was not a lecture series. It was a development sprint, scaffolded by Google's most senior AI infrastructure experts and designed to compress months of product iteration into a single focused week.

The journey began on November 20th with a virtual onboarding session — a brisk, purposeful walkthrough of the programme's architecture, requirements, and the specific technical capabilities each startup would be expected to demonstrate. The clarity of the structure was itself a signal: Google had designed this programme to produce results, not conversations about results.

From November 26th to November 28th, the cohort gathered in person at Google's Jakarta office. Two representatives per startup, as the programme's terms specified — a deliberate constraint that forced founding teams to send their most capable and most committed people. The in-person workshops covered Google Cloud AI Platform architecture, the technical anatomy of AI agents, and hands-on labs where startups built real features under the guidance of Google engineers and technical mentors.

At the centre of the technical curriculum was something that has quietly become the most important concept in applied AI: the AI agent. Not just a model that answers questions, but a system that reasons, plans, retrieves information, and takes actions across multiple steps to complete a goal. Google's programme went deep on this — covering agent architecture patterns, tool-use and function calling, retrieval-augmented generation (RAG), and how to orchestrate complex multi-agent workflows on Google Cloud.

For Intura, this curriculum arrived at precisely the right moment. Our product was already reasoning about brand perception across multiple data sources. The AI agents framework gave us a vocabulary and an engineering approach to make that reasoning more systematic, more scalable, and more capable of handling the complexity of real-world brand data — where signals arrive simultaneously from social media conversations, e-commerce reviews, and AI-generated responses across platforms like Gemini and ChatGPT.

The mentors were not generalists. Widya, Riza Fahmi, Yohan Totting, Alvin Prayuda, Juniarta Dwiyantoro, Stanley Dave, and others brought practitioner-level knowledge to every session — correcting architecture decisions, pushing back on shortcuts, and consistently raising the bar for what a production-ready AI system should look like. Their guidance was not soft encouragement. It was the kind of technical accountability that forces founders to think more rigorously about what they are building and why.

The framework Intura developed during the AI Solutions Lab addresses a problem that did not have a name eighteen months ago: AI visibility. As consumers increasingly turn to Gemini, ChatGPT, and other AI assistants to guide purchase decisions, the question is no longer just "what are people saying about my brand on social media?" but "how does an AI model describe my brand when someone asks for a recommendation?" and "what perception is being constructed about my product in the AI layer that mediates discovery?"

The framework Intura built measures brand performance across three stages that mirror the modern AI-mediated consumer journey: Discovery (how prominently is the brand surfaced in AI-generated responses?), Consideration (how is the brand framed and contextualised relative to alternatives?), and Decision (what signals in AI responses correlate with conversion intent?). Each stage is tracked across AI platforms, giving brand and marketing teams a real-time view of their AI presence — something that had never existed before in an accessible, integrated form.

The implications reach beyond marketing analytics. As AI platforms become the primary interface through which consumers research major purchases, understanding and optimising AI presence becomes a structural business capability — as foundational as SEO was in the search era, but with significantly more complexity and significantly higher stakes.

The final day arrived with the particular weight of deadlines that have consequences. On December 1st, the cohort presented their AI solutions to a panel of judges at Google's Jakarta office — a rigorous evaluation that assessed technical depth, product viability, market understanding, and the quality of the AI architecture underlying each solution.

The judges asked the kinds of questions that reveal whether a founding team truly understands what they have built — probing the edge cases, the scalability assumptions, the data pipeline decisions, and the go-to-market implications of the technical choices made. For Intura, this was a clarifying moment: the months of product development, the decisions made and reversed, the architectural choices — all of it had to cohere into a single, coherent story about a product that worked and a market that needed it.

When the results were announced, Intura was named one of the Top 10 startups of the inaugural cohort. The recognition was not something we had entered the programme to collect. But it confirmed something more important: that the framework we had built during those four intensive days was not just technically sound — it represented a genuine answer to a genuine problem that the market is only beginning to understand.

The tangible outputs of the AI Solutions Lab — the framework, the technical architecture, the Google Cloud credits, the Top 10 designation — matter. But they are not the whole story. The more durable value came from the people: the mentors who brought practitioner-level honesty to every session, the fellow participants who were each solving adjacent pieces of the same puzzle from different angles, and the Google team who created an environment where the standard was genuinely high and the support was genuinely substantive.

The fellow founders we met across those four days — navigating the same uncertainty, making the same kinds of difficult technical bets, wrestling with the same question of how to build products that are not just AI-powered but AI-native — represent a community that compounds over time. The conversations that began in workshop rooms and continued over lunch at the Google office have already led to introductions, collaborations, and a shared vocabulary for talking about what it means to build AI products in Indonesia in 2025.

To Widya, Riza Fahmi, Yohan Totting, Alvin Prayuda, Juniarta Dwiyantoro, Stanley Dave, Teherag Iqbal, J Putra, Evania Jiady, and everyone else on the Google for Startups team who made this programme possible — our gratitude is specific and sincere. You built something that produces real outcomes. We intend to honour that by continuing to build.

December 1st, 2025 was not a finishing line. It was a recalibration point — a moment where the work that had been done in relative isolation was validated against a demanding external standard, and found to be sound. The AI Solutions Lab compressed what might have been twelve months of iteration into four days of structured, high-pressure development. That compression has a lasting effect on how we think about building.

The brand performance framework we built during the programme is now at the centre of Intura's product roadmap. The AI agent architecture we learned and applied is the foundation on which we are building the next generation of our platform. And the network of founders, engineers, mentors, and Google ecosystem partners we connected with over those four days is the community within which the next chapter of Intura's story will unfold.

Indonesia is building its AI future right now. The Google for Startups AI Solutions Lab is one of the most serious efforts to ensure that future is built by Indonesian founders, on solid technical foundations, with a clear understanding of what the global market will require. We are grateful to have been part of its first chapter — and more committed than ever to writing the next one.