At Databricks, our automation vision is to automate all aspects of the business, making it better, faster, and cheaper. For the sales teams, we are digitally transforming our seller experience by providing genAI agents that assist the seller across the sales lifecycle. Our goal is to augment the seller experience with AI capabilities by seamlessly integrating them into their day-to-day tasks and providing a simpler, more effective way for sellers to retrieve information assets as well as orchestrate actions by automating repetitive manual administrative tasks.
Our “Field AI Assistant” is built on the Databricks Mosaic AI agentic framework and provides a way for sellers to query and interact with data across multiple data sources. It integrates with several key platforms including:
The AI application is used to:
The field assistant responds to seeded prompts based on user and page context and also provides a chat-like interface for open-ended queries on the above-mentioned datasets.
Sellers are typically overwhelmed with the volume of information thrown at them. They need access to data residing in various siloed applications, as part of their normal day-to-day routine. They require easy access to account, opportunity, and use case data that resides in our CRM, as well as customer market insights and account intelligence, including account consumption data that resides in our lakehouse. In addition, they also need access to sales content - enablement playbooks, competitive sales collateral as well as product knowledge base articles and product roadmap documents. It isn’t just limited to data retrieval, but the true efficiency gains occur when the repetitive manual tasks they perform on a daily basis based on the data insights they retrieve can be fully automated. That is exactly what the role of the field AI assistant is - help the sellers in the day-to-day tasks including information retrieval, distilling the insights from the information, and performing actions based on those insights.
Using the Databricks Mosaic AI agent framework, we built a field AI assistant by integrating both structured and unstructured data from multiple data sources. The solution provides a comprehensive approach personalized and tailored for our sellers, available on-demand in our CRM. Some of the capabilities offered include:
Customer insights provide a 360-degree customer account view with:
Data hygiene alerts
Sales collateral
Orchestrate action
Our field AI assistant solution is built entirely on our Databricks tech stack. It allows integration into multiple and diverse data sources and provides a scalable infrastructure framework for data retrieval, prompting, and LLM management. It is built using the compound AI agentic framework and supports the addition of multiple tools (SQL queries, Python functions) that are all governed through our Unity Catalog governance layer.
Human inputs are inherently ambiguous; LLMs have now given us the ability to use context to interpret the intent of a request and convert this into something more deterministic. To service the request, it might be necessary to retrieve specific facts, execute code, and apply a reasoning framework based on previously learned transformation. All of this information must be reassembled into a coherent output that is formatted correctly for whomever (or whatever) will consume it.
That is exactly what the field AI assistant does to respond to the queries from the sellers. The field AI assistant has 1 driver agent and multiple tools and functions that perform the deterministic processing.
That said, our solution architecture is designed to allow for flexibility in adopting new models as they become available in our Mosaic AI agent framework.
At Databricks, we have leveraged the Mosaic AI Agent Framework which makes it easy to build a genAI application like the field AI assistant. Using this framework, we have defined evaluation criteria and we leverage LLM-as-a-judge capability to score the application responses. The Mosaic AI Gateway provides access controls, rate limiting, payload logging, and guardrails (filtering for system inputs and outputs). The gateway gives the user constant monitoring of running systems to monitor for safety, bias, and quality.
The components that we leveraged for our field AI assistant are:
Data is messy - Leveraged Lakehouse, iterative expansion of datasets, focused on data-engineered pipelines and building clean, GOLD Single Source of Truth datasets
Measuring ROI is difficult - Be prepared to experiment with small focus groups in the pilot. Building evaluation datasets for measuring model effectiveness is hard and requires focused effort and a strategy that supports rapid experimentation
Data and AI Governance is a MUST - Engage early with Enterprise Security, Privacy, and Legal teams. Build a strong governance model on Unity Catalog for the data as well as the agents and tools
Through this post, we hope you learned about our Databricks on Databrick's GenAI journey and how we leverage technology like this to help our sellers be more effective. Utilizing GenAI for this use case has helped to showcase how AI agents can significantly transform and assist every aspect of the seller journey, from prospecting and customer insights retrieval, driving better data hygiene by automating repetitive manual tasks and actioning those data insights to driving opportunities and improving sales velocity.
Stay tuned for our upcoming posts, where we’ll continue to share our experiences on how AI is reshaping the seller experience at Databricks.