Built in India.
Read in forty languages.

We did not start as a software company.

Brillaince is built by DAV Media, a media, communications and advertising agency whose creative, media and technology teams work with brands including PepsiCo, Amazon, Jack Daniel's, Kingfisher and Tata Power, and alongside agencies including Wunderman Thompson, Ogilvy, DDB and AdFactors PR. At that end of the market nobody gets to be approximately right about reputation. A misread story costs a client something that shows up on a P&L.

So the problem we set out to fix was never a gap in a feature matrix. It was the specific, recurring experience of having four tools open, three numbers that disagreed, and a client on the phone who wanted one answer.

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Four tools open, three numbers that disagreed, and a client who wanted one answer.

Build the analysis layer first. Sources second.

Most monitoring products are assembled the other way around: acquire feeds, then layer reporting on top of whatever each feed returns. It is the faster path to a long source list, and it is why so many dashboards show numbers that cannot honestly be compared to one another.

We inverted it. One analysis engine was built first, and every channel, press, social, YouTube, AI answers, was brought in to run through it. Adding a source to Brillaince means teaching the existing engine to read it, not bolting on a vendor with its own idea of what negative means.

India made the hard decision for us.

A brand operating in India is discussed in a dozen languages before breakfast, and the story that damages it is rarely the one written in English. Every shortcut here, translate first, score later, quietly destroys the thing you were trying to measure.

So the engine reads natively. 40+ languages, 12 of them Indian, scored on the original text rather than on a translation of it. Building for the hardest market first is why the same engine now holds up across North America, Europe, the Middle East and Asia-Pacific without a second methodology bolted on for each.

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Build for the hardest language market first, and the rest of the world is a smaller problem.

Buyers stopped searching and started asking.

Somewhere in the last two years, a meaningful share of the questions that used to begin on a search page began going to a model instead. Which brand is safest. Which vendor is best. Is this company in trouble. The answer arrives confident, sourced from somewhere, and with no second page to scroll to.

That is a reputation channel, and almost nobody was watching it. Brillaince now checks what ChatGPT, Gemini, Claude, Perplexity and Google's AI Overviews say about a brand every day, whether it is accurate, whether a competitor is recommended instead, and which sources are feeding the answer.

One platform, from the first signal to the published response.

Press across 5,000+ sources. Social at post level. YouTube by video and channel. Every major AI engine, daily. A velocity clock measured against each brand's own baseline. One ranked queue instead of six competing dashboards. Drafts written from the live data behind an alert. An assistant that answers from your own workspace and says so when the data cannot. And an API, so none of it is trapped behind our interface.

It is used by brands, and by the agencies and communications teams working for them, in India and internationally.