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Zeotap builds marketer’s AI companion with Vertex AI

In today’s fast-changing marketing world, data is king. Marketers are under pressure to show solid returns on investment (ROI). With tightening budgets, marketers find themselves leaning heavily on data for strategic planning, audience targeting, performance evaluation, and efficient resource allocation.

As businesses strive to better understand their customers and deliver meaningful experiences, Customer Data Platforms (CDPs) have emerged as crucial tools in a marketer’s kit, enabling brands to build unified profiles of their customer data from all channels. They clean and standardize data across sources for easy integration with AdTech and MarTech platforms.

One pioneering CDP providing marketers with innovative and comprehensive customer solutions is Zeotap. By partnering with Google Cloud, and leveraging the same cloud technologies powering Search, YouTube, and Google Maps, Zeotap has built an intuitive marketers’ CDP.

As our collaboration redefines how brands manage and engage with customer data, this blog shows how Zeotap is leveraging Google’s generative AI prowess to enable marketers to derive even more value from their customer data by creating a CDP that is easy to use yet robust, drive deeper insights and marketing success.

Building a marketing companion with Vertex AI

Building effective and impactful marketing campaigns requires new ways to build deeper relationships with your customers while delivering results. For large brands, with multiple customer touch points, complex segmentation models are essential to provide context and time-based alerts and offers. However, these models can be difficult for non-technical users to understand and leverage effectively.

Ada™ Zeotap’s AI Companion is here to guide marketers through intuitive steps to build and analyze customer data to make insight-driven decisions. The seamless, accessible introduction enables all marketers, regardless of technical skill, to unlock valuable insights from their data. By simply conversing with Ada and describing their business goals and available data, marketers can effortlessly build custom segments that Ada will translate into actionable rules to review, save, or activate.

Architecture overview

The foundation of this application lies atop Google’s Large Language model (LLM) PaLM2 on Vertex AI, which possesses an extensive understanding of human language and context. This model serves as the core component responsible for interpreting natural language commands. The deployment includes autonomous agents using this powerful LLM, serving as asynchronous threads of thought that coalesce together toward one common goal. Zeotap uses an ensemble [1] of such agents [2] called Mixture of Experts that work as a team to refine their ideas to provide a clear, straightforward response. Before taking any action, the automated assistants map out exactly what they plan to do using a method called ReAct [3].

Data flow

When a user describes a segment to build, our system gathers the relevant catalog from internal data stores and aggregates it into a (JSON) LLM-readable format. After precise prompt tuning and elaborately crafted flows, we provide the AI with the user’s perceived intent, reference information, and plenty of sanity checks. Once the semantic intent is understood, the AI queries the metadata from the backing databases and identifies the relevant entities. Each of these entities is refined via a business context aware, exhaustive set of sanity checks through custom tailored heuristics to keep the agent’s hallucinations in check. Special care is taken to ensure that these processes do not change or interfere with the underlying client data.

Vector similarity search

Vertex AI’s Vector Search employs machine learning to grasp the essence and context of disorganized data. It relies on huge pre-trained models (text-embedding-gecko in this case) that have a broad range of knowledge and interpret meanings with great accuracy. These models can translate words, sentences, or paragraphs into numerical representations. These numerical representations encapsulate the root meaning, and as a result, similar numbers match similar ideas.

To break down the task into operators and values, we make two distinct requests to the Vertex AI’s LLM PaLM2 (text-bison) using the same basic information. Each request involves the context (user’s input), available values, and the previous agent’s response within the input. Because the pool of operators is limited, the AI Companion can consistently provide a reasonable response without needing further refinement. However, the actual answer may be wrong or missing. Additionally, the agent can only use a limited range of values and doesn’t understand columns with multiple possibilities. To address this, we compare Ada’s answer to the possible value for that column until we find a match using similarity search.

Once we have built these three structured groups, the primary role of PaLM-2 text-bison concludes. At this point, we employ these well-structured groups to construct SQL queries, which run using a designated client SQL Query Engine. We use this output to pre-fill the segment conditions which the user can verify and save the audience for activation.

Better together: Zeotap + Google Cloud

Zeotap and Google Cloud are working together to transform how companies manage and engage with their clients. Our collaborative solutions are already driving value for Zeotap’s customers, offering an innovative, user-friendly interface that prioritizes simplicity and results. By harnessing the power of Google Cloud’s gen AI models, we are committed to making data-driven marketing more accessible and efficient.

Google’s generative AI technology has been instrumental in helping us unlock new possibilities for our customers. The synergy between Zeotap’s platform and Google’s advanced models has enabled us to deliver innovative solutions that improve accuracy, efficiency, and personalization. We are excited to continue collaborating with Google and exploring the potential of generative AI to transform the industry.

Our vision extends beyond audience refinement; we are dedicated to enhancing user experiences and pioneering innovative solutions. This involves streamlining data integration, automating data mapping, and equipping marketers with cutting-edge AI technology for effortless customer insights. In the upcoming months and years, Zeotap is committed to continuing our collaboration with Google Cloud to capitalize on all of the benefits that gen AI will bring to our customers.

Learn more about Google Cloud’s open and innovative generative AI partner ecosystem. Read more about Zeotap and Google Cloud.


Chen, Zixiang, et al. “Towards understanding mixture of experts in deep learning.” arXiv preprint arXiv:2208.02813 (2022).Karpas, Ehud, et al. “MRKL Systems: A modular, neuro-symbolic architecture that combines large language models, external knowledge sources and discrete reasoning.” arXiv preprint arXiv:2205.00445 (2022).Yao, Shunyu, et al. “React: Synergizing reasoning and acting in language models.” arXiv preprint arXiv:2210.03629 (2022).Ji, Bin. “VicunaNER: Zero/Few-shot Named Entity Recognition using Vicuna.” arXiv preprint arXiv:2305.03253 (2023).

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