Parsewise integrates Gemini models directly into its data processing pipelines and conversational workspace, Navi. This setup helps analysts safely upload, extract, and question their files in a logical sequence.

The process begins when a user uploads a batch of files. Parsewise uses Gemini 3.0 Flash to instantly extract all text, tables, and layouts, caching the data for rapid downstream analysis. This immediate extraction speeds up the entire pipeline for the end user. Gemini 3.0 Flash uses spatial and visual recognition to read legacy scanned pages and dense financial charts, spotting partially obscured text that traditional tools miss.

To build trust in its output, Parsewise uses the models' spatial understanding to pinpoint exactly where specific data such as a relevant number appears, highlighting the location on the original scanned page. With one click, human analysts can trace each result back to its source, making it easier to resolve inconsistencies across documents and greatly reducing the risk of hallucinations. Parsewise also needs to ensure its customers can trust the platform’s data security and regulatory compliance. By selecting regional data residency settings within the Gemini Enterprise Agent Platform, Parsewise restricts data processing to the European Union, ensuring it meets strict GDPR and SOC 2 requirements for its financial clients. “Gemini Flash stands out against previous third-party models we tested because it actively asks for more information when context is missing,” says Nikola Bozhinov, head of engineering at Parsewise. “Instead of jumping to an unverified answer, it interacts with our platform to request the exact details required to deliver a reliable result.”

Instead of using basic keyword searches, Parsewise relies on Gemini to read every single page across thousands of files. This exhaustive search ensures the platform spots revised figures or conflicting statements published years apart.

For Parsewise's customers, these technical capabilities translate directly into faster, more reliable business outcomes. Instead of manually sampling <5% of a large claims book, risk specialists assess the entire book end to end, spotting hidden risks instantly and accelerating their decision-making by up to 10x.

The benchmark tests how well a system can reason across nearly 89,000 pages of US Treasury Bulletins spanning 90 years. Parsewise used Gemini models to achieve a top score of 69.92% correctness, outperforming other leading models. The platform's deep search capabilities uncovered updated historical figures across 15 questions, several of which the Databricks research team has since accepted into the benchmark.

Looking ahead, Parsewise plans to test newer Google Cloud models to reduce processing costs even further for its users. The company is also growing its team and deploying its platform directly inside its customers’ secure cloud environments on Google Cloud.

“With Gemini models we can deliver results that are not only fast, but significantly more accurate than with other providers,” concludes Csegzi. “This allows individual customer pipelines to spin up parallel requests working through billions of tokens per minute, cutting execution times up to 10x. As a result, our customers can verify outputs quickly to build trust as we continue to explore new AI capabilities and redefine the future of risk analysis.”