About Browsi

Browsi is an AI-powered marketing intelligence platform that helps brands, agencies, publishers, and content creators gain greater visibility and control across the digital advertising ecosystem. Built on the same technology that powers Browsi’s publisher ad optimization business, Browsi analyzes more than 130 billion ads each month across the open web and turns large-scale ad exposure data into actionable insights on competitor presence, creative strategy, share of voice, market movement, viewability, engagement, geography, and audience trends. Inside Browsi’s InsightHub, PolarisAI enables marketers to query raw data in natural language, move beyond static dashboards, and uncover granular insights faster. As the platform expands across additional channels, Browsi is focused on creating greater market transparency and helping marketing teams make decisions with clarity instead of guesswork.

Challenges

Browsi needed to overcome both product and technical challenges in order to bring its next-generation marketing intelligence platform to market. Marketers lacked the transparency, flexibility, and competitive visibility required to understand true campaign performance across the digital advertising ecosystem. At the same time, Browsi was building PolarisAI, an AI-powered insights layer inside InsightHub, and brought in Commit to add LLM engineering capacity and shorten the path from concept to production.

Key challenges included:

• Limited advertising transparency: Marketers struggled to understand true campaign performance across channels, segments, and the broader advertising pipeline. 
• No competitive benchmarking: Users could view their own ad performance but lacked the ability to compare results against competitors and market activity. 
• Restrictive static dashboards: Existing dashboards relied on predefined questions and pre-aggregated data, limiting ad-hoc analysis and deep drilldowns. 
• Need for raw data access: Browsi wanted to enable users to query granular, unaggregated data across brands, platforms, creatives, geographies, and audience segments. 
• Pressure to move quickly: To capture the market opportunity, Browsi wanted to compress its delivery timeline and add senior LLM engineering capacity alongside its own team.

Solution

To help Browsi overcome the limitations of static dashboards and accelerate its AI product roadmap, Commit worked alongside Browsi’s product and engineering team on the initial release of PolarisAI, an LLM-based AI agent inside InsightHub, built on Browsi’s data platform and product vision. The solution introduced a conversational analytics experience that allows marketers to ask natural language questions directly against Browsi’s advertising data, moving beyond predefined dashboards and enabling faster access to granular, ad-hoc insights.

PolarisAI was built to support:

• Natural language data exploration: Users can ask questions in plain language instead of relying on fixed dashboard views or predefined reports. 
• Direct access to raw data: PolarisAI connects to Browsi’s raw data, allowing users to analyze information at a much deeper level of detail. 
• Ad-hoc competitive analysis: Marketers can compare performance against competitors, explore market activity, and investigate specific brands, platforms, creatives, geographies, and data segments. 
• Conversational context: PolarisAI maintains context across questions, enabling users to continue exploring a topic without starting over. 
• Dynamic outputs: The solution can generate graphs, surface specific ad creatives, and compile insights into slide decks. 
• Accelerated delivery: Commit’s LLM expertise helped Browsi bring the first production version of PolarisAI to market in approximately three months.

Results

PolarisAI became a foundational part of Browsi’s platform, now in production and serving as a key sales driver, helping position the product beyond a standard dashboard experience.

Key results included:

• 80%+ user satisfaction with intuitive, chat-based analytics. 
• 60%+ user engagement in multi-turn analytical conversations. 
• Faster time-to-data through real-time access to performance signals. 
• Faster self-serve answers without waiting on analyst reports. 

“Commit brought strong LLM expertise and real momentum to the PolarisAI project. Working alongside our team, they helped us move from concept to production quickly and with confidence. Beyond the technology, the team was professional, responsive, and great to work with.”

‍Matan Ghuy Waron, Chief Architecture Officer, Browsi

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