What challenges did Entis face before AI implementation?

Improve speed and relevance of insights derived from unstructured financial data for investment decisions. Process large volumes of complex textual data including reports, patents, and websites effectively. Enhance financial market analysis beyond traditional figure-based methods to include qualitative document context.

What was the impact of the AI solution?

Delivered faster and more insightful analysis of thousands of financial documents each day. Improved overall quality of investment decisions for asset management clients. Achieved a cost-effective AI platform adoption with smooth operational integration.

Source

GCP

Entis enhances unstructured financial data analysis with Vertex AI and PaLM 2

Use case typeInvestment researchUpdated Jun 13, 2026

Entis, a financial services company in the Netherlands, integrated Google Cloud Vertex AI and PaLM 2 into its platform to analyze unstructured financial data for investment insights. The solution combines structured financial data with semantic vector analysis of documents like reports and patents using fine-tuned PaLM 2 models. This integration improved analysis speed and relevance for thousands of financial documents daily, enhancing investment decision quality. Entis partnered with Google Cloud partner Xebia for implementation and values the simple integration and competitive pricing of Google Cloud AI. The embedding service transforms unstructured data into semantic vectors enabling more meaningful information comparison beyond keywords.

Organization: Entis
Industry: Finance
Location: Netherlands

Reported outcomes

Strategic outcomes

Better decisions & insightImproved investment decision qualitySpeed & agilityFaster analysis of financial documentsNew product / capabilityEnabled semantic unstructured data analysisCost efficiencyCost-effective AI platform adoption

Primary read

Use case focus

Showing 3 of 3

1Document Analysis
2Investment Decision Support
3Data Vectorization

Improve speed and relevance of insights derived from unstructured financial data for investment decisions.
Process large volumes of complex textual data including reports, patents, and websites effectively.
Enhance financial market analysis beyond traditional figure-based methods to include qualitative document context.

Integrated Vertex AI to fine-tune PaLM 2 for semantic vector analysis of unstructured data.
Combined structured financial data with advanced document AI capabilities using Google Cloud Document AI.
Partnered with Xebia for expert implementation support.
Leveraged Google Cloud's embedding service for efficient transformation of unstructured data into actionable insights.

Technologies

Vertex AI PaLM 2 Document AI

Delivered faster and more insightful analysis of thousands of financial documents each day.
Improved overall quality of investment decisions for asset management clients.
Achieved a cost-effective AI platform adoption with smooth operational integration.

Implementation partners1

Xebia

Sources & evidence1

Groundedness: 4/5

Primary source

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Customer evidence