At Databricks, our automation imaginative and prescient is to automate all elements of the enterprise, making it higher, quicker, and cheaper. For the gross sales groups, we’re digitally reworking our vendor expertise by offering genAI brokers that help the vendor throughout the gross sales lifecycle. Our purpose is to reinforce the vendor expertise with AI capabilities by seamlessly integrating them into their day-to-day duties and offering a less complicated, more practical manner for sellers to retrieve info belongings in addition to orchestrate actions by automating repetitive handbook administrative duties.
Our “Area AI Assistant” is constructed on the Databricks Mosaic AI agentic framework and offers a manner for sellers to question and work together with knowledge throughout a number of knowledge sources. It integrates with a number of key platforms together with:
- Our Inner Databricks Lakehouse for account intelligence, gross sales enablement content material, and gross sales playbooks
- Our Buyer Relationship Administration Platform (CRM) system
- Our Collaboration Platform collates and indexes most of our nonstructured knowledge
The AI software is used to:
- Conversationally work together with knowledge throughout a number of knowledge sources utilizing pure language (beginning with English)
- Means to obtain and create paperwork based mostly on the knowledge gathered
- Take actions based mostly on the information insights (replace fields in our CRM, draft a customized outbound prospecting e-mail, create a tailored buyer proposal, prep for a buyer assembly, and so on.
The sphere assistant responds to seeded prompts based mostly on person and web page context and in addition offers a chat-like interface for open-ended queries on the above-mentioned datasets.
Enterprise Affect
Sellers are usually overwhelmed with the amount of knowledge thrown at them. They want entry to knowledge residing in numerous siloed functions, as a part of their regular day-to-day routine. They require quick access to account, alternative, and use case knowledge that resides in our CRM, in addition to buyer market insights and account intelligence, together with account consumption knowledge that resides in our lakehouse. As well as, additionally they want entry to gross sales content material – enablement playbooks, aggressive gross sales collateral in addition to product data base articles and product roadmap paperwork. It isn’t simply restricted to knowledge retrieval, however the true effectivity positive factors happen when the repetitive handbook duties they carry out each day based mostly on the information insights they retrieve could be totally automated. That’s precisely what the position of the sector AI assistant is – assist the sellers within the day-to-day duties together with info retrieval, distilling the insights from the knowledge, and performing actions based mostly on these insights.
Answer Overview
Utilizing the Databricks Mosaic AI agent framework, we constructed a area AI assistant by integrating each structured and unstructured knowledge from a number of knowledge sources. The answer offers a complete strategy personalised and tailor-made for our sellers, accessible on-demand in our CRM. A few of the capabilities provided embody:
Buyer insights present a 360-degree buyer account view with:
- Monetary information/insights in regards to the account
- Aggressive knowledge panorama
- Product consumption by product line and cloud
- Buyer assist circumstances
- High Use Circumstances Driving Income
- Vendor Suggestions on different use circumstances which can be provided to comparable prospects
Information hygiene alerts
- Use circumstances which can be going stay within the subsequent week/month/quarter
- High use case blockers
- Use circumstances that lack key info (ie exec enterprise sponsor and so on.)
Gross sales collateral
- Gross sales playbooks
- Aggressive collateral
- Assembly summarization
- Pitch decks
Orchestrate motion
- Replace CRM with the subsequent steps on particular alternatives or use circumstances
- Draft a prospecting e-mail to a brand new buyer contact
- Create a customer-facing proposal
Our area AI assistant resolution is constructed solely on our Databricks tech stack. It permits integration into a number of and numerous knowledge sources and offers a scalable infrastructure framework for knowledge retrieval, prompting, and LLM administration. It’s constructed utilizing the compound AI agentic framework and helps the addition of a number of instruments (SQL queries, Python features) which can be all ruled by way of our Unity Catalog governance layer.
Agent / Instrument Framework
Human inputs are inherently ambiguous; LLMs have now given us the power to make use of context to interpret the intent of a request and convert this into one thing extra deterministic. To service the request, it may be essential to retrieve particular information, execute code, and apply a reasoning framework based mostly on beforehand discovered transformation. All of this info have to be reassembled right into a coherent output that’s formatted accurately for whomever (or no matter) will devour it.
That’s precisely what the sector AI assistant does to reply to the queries from the sellers. The sphere AI assistant has 1 driver agent and a number of instruments and features that carry out the deterministic processing.
- Information basis: That is the set of information sources that the agent interacts with. In our resolution, this knowledge basis contains knowledge in our Lakehouse, gross sales collateral, Google docs in addition to knowledge that resides in our CRM (Salesforce).
- Deterministic processing: The set of features and instruments required to provide appropriate, high-quality responses. The LLM can extract fields from a question and cross these to a normal operate name to do deterministic processing. Inside the Databricks Platform, the Mosaic AI Instruments and Capabilities capabilities allow this and user-defined features can carry out most actions inside Databricks. These could be usually Python features or easy SQL queries or APIs that combine with exterior apps akin to Glean, Perplexity, Aha and so on. and these could be invoked utilizing pure language.
- LLM fashions: We leverage Azure OpenAI, GPT 4 because the foundational mannequin for the sector AI assistant resolution. That stated, the framework helps a multi-model strategy the place the precise capabilities of every mannequin is evaluated with respect to the way it offers with particular use circumstances. For e.g. we now have evaluated our resolution with numerous open supply fashions and we selected Azure Open AI – GPT 4 because the mannequin for our resolution based mostly on the groundedness of the mannequin, its capability to generate factual and related content material, its capability to choose the suitable user-defined operate / software for processing every immediate, and its capability to stick to the content material output formatting prompting offered to the mannequin.
That stated, our resolution structure is designed to permit for flexibility in adopting new fashions as they develop into accessible in our Mosaic AI agent framework.
At Databricks, we now have leveraged the Mosaic AI Agent Framework which makes it straightforward to construct a genAI software like the sector AI assistant. Utilizing this framework, we now have outlined analysis standards and we leverage LLM-as-a-judge functionality to attain the appliance responses. The Mosaic AI Gateway offers entry controls, fee limiting, payload logging, and guardrails (filtering for system inputs and outputs). The gateway provides the person fixed monitoring of operating programs to observe for security, bias, and high quality.
The parts that we leveraged for our area AI assistant are:
Answer Structure
Our Learnings
Information is messy – Leveraged Lakehouse, iterative growth of datasets, targeted on data-engineered pipelines and constructing clear, GOLD Single Supply of Reality datasets
Measuring ROI is troublesome – Be ready to experiment with small focus teams within the pilot. Constructing analysis datasets for measuring mannequin effectiveness is tough and requires targeted effort and a technique that helps speedy experimentation
Information and AI Governance is a MUST – Have interaction early with Enterprise Safety, Privateness, and Authorized groups. Construct a robust governance mannequin on Unity Catalog for the information in addition to the brokers and instruments
Conclusion
By means of this put up, we hope you discovered about our Databricks on Databrick’s GenAI journey and the way we leverage know-how like this to assist our sellers be more practical. Using GenAI for this use case has helped to showcase how AI brokers can considerably rework and help each side of the vendor journey, from prospecting and buyer insights retrieval, driving higher knowledge hygiene by automating repetitive handbook duties and actioning these knowledge insights to driving alternatives and bettering gross sales velocity.
Keep tuned for our upcoming posts, the place we’ll proceed to share our experiences on how AI is reshaping the vendor expertise at Databricks.