Scalestack unlocks GTM productivity and growth with AWS and MongoDB

INTRODUCTION

Unlocking sales productivity and growth with AI

Sales productivity startup Scalestack is driven by a lofty mission. The company — which was founded in 2021 by veteran startup entrepreneurs Elio Narciso and Alex Prioni — builds products that “make computers work for humans, so they can focus on what really matters,” according to its website.

Scalestack seeks to bring order to often chaotic go-to-market (GTM) data. Specifically, Scalestack helps customers manage both structured and unstructured data, from first-party CRM data to third-party data from sources like LinkedIn. The company’s AI-powered, all-in-one GTM data orchestration and activation platform helps a growing list of customers — including MongoDB’s own sales team — spend less time manually researching and organizing data, and more time executing.

To make its mission a reality, Scalestack turned to MongoDB and Amazon Web Services (AWS). With the industry’s most popular developer data platform and the world’s most comprehensive and broadly adopted cloud services provider, respectively, the choice of MongoDB Atlas on AWS was simple.

“Scalestack’s mission is to help organizations unlock sales productivity, and our relationship with MongoDB and AWS has been integral to achieving that,” said Narciso.

THEIR CHALLENGE

Big data, small team

Research by Salesforce backs up Scalestack’s mission. A survey of nearly 8,000 sales professionals showed that they “spend just 28% of their week actually selling, with the majority of their time consumed by other tasks,” like manually researching and prioritizing prospects, and data entry.

The issue, Narciso said, is that there is no tech to easily connect GTM data to customers’ Ideal Customer Profiles (ICP) and sales engines, hence sales professionals waste time reconciling data sets that don't talk to each other. To solve this, Scalestack developed an AI solution that would enable customers to aggregate and understand a variety of data from disparate sources, helping reps execute sales plays (repeatable, scenario-specific strategies to help sales reps succeed) with context.

Indeed companies — particularly enterprises — usually have three big GTM data “buckets,” said Narciso. The first is their customer relationship management (CRM) system, which contains lots of “first party” data about target companies and prospects. The second GTM data bucket is “third party data” from external sources, which is often purchased. Examples include Linkedin data, news data, and information from Crunchbase, Zoominfo, job postings and more. And the last bucket is “zero party” data that comes from marketing motions like lead generation forms, or when people share data as part of the process of registering to use a product. As a result, GTM teams spend a lot of their time organizing all of this data, sometimes even doing so manually.

Scalestack's AI platform brings the GTM data “buckets” together in an easy and intuitive automated “workflow,” suggests prioritized actions, and helps sales representatives execute their sales plays. It unlocks a new level of sales productivity and growth for customers, Narciso said.

OUR SOLUTION

AI powered by AWS and MongoDB

To make all of that magic happen, Scalestack needed a solution that would be easy to deploy and that would allow their developers to focus on the core components of their innovation.

Enter AWS and MongoDB Vector Search on Atlas. By choosing AWS as its cloud provider, Scalestack has been able to keep its cloud costs low and its data usage elastic. And Scalestack uses MongoDB’s “flexible schema and powerful querying abilities to efficiently manage a diverse range of data types,” said Scalestack’s Alex Prioni.

AWS also provides the infrastructure for Scalestack’s Spotlight AI copilot — which helps sales representatives draft emails and scripts based on GTM data — ensuring scalability and reliability.

Meanwhile, Spotlight uses MongoDB Vector Search’s retrieval-augmented generation (RAG) abilities to perform quick searches over large datasets using vector similarity. With Spotlight, users can have “conversational interactions” and ask the copilot complex questions about company data, leading to dynamic, informative responses, Prioni said.

Spotlight draws on a huge amount of disparate types of data—such as customer information, company facts, news, and job openings. By using MongoDB Vector Search on Atlas to find connections and patterns across all of those data types, Spotlight gives its users answers that are both relevant to and useful.

“By leveraging MongoDB Vector Search on Atlas, our AI copilot can understand the context of user queries more effectively, drawing upon a rich database of indexed information,” said Prioni. “This leads to more accurate and contextually relevant responses, a key factor in providing an engaging and helpful user experience.”

THE RESULTS

Increased productivity and revenue

Though Scalestack is still in its early days, the company has already seen impressive results thanks to its work with AWS and MongoDB.

“Working with MongoDB and AWS has notably improved Scalestack's efficiency, particularly by streamlining data management and enhancing query performance,” said Prioni. “MongoDB's real-time processing capabilities have also been crucial in providing up-to-date information across our services.”

And by using Scalestack’s AI, customers have seen drastically reduced research times and significant increases in productivity. Customers saw a 40% increase in rep productivity, and GTM teams saw a 53X ROI on average on the Scalestack platform, measured as delta revenues influenced by Scalestack.

“Our sellers get hundreds of sales leads coming in weekly from a large variety of sources like events, job postings, and via social networks. Leveraging AI, Scalestack has been key to helping us to easily aggregate, manage, and automate disparate GTM data sets in a matter of minutes and identify true leads,” said Meghan Gill, SVP of Sales Operations at MongoDB.

Going forward, Scalestack looks forward to leveraging the integration of MongoDB Vector Search on Atlas and Amazon Bedrock, which was announced during AWS re:Invent 2023.

The planned integration between MongoDB Vector Search on Atlas and Amazon Bedrock will make it easier for developers to create applications on AWS that use generative AI to complete complex tasks for a wide range of use cases and deliver up-to-date responses based on proprietary data processed by MongoDB Vector Search on Atlas.

Prioni noted that Scalestack will be using Amazon Bedrock starting in Q2 of 2024. The ability to customize Amazon Bedrock models will allow the company to fine-tune AI models to specific use cases, enhancing the Spotlight copilot’s relevance and performance, he said.

“We’re really excited about the integration between MongoDB Vector Search on Atlas and Amazon Bedrock — this fully managed system will let our developers focus on innovating on behalf of customers. We look forward to working with both MongoDB and AWS to further the development of Scalestack’s AI-powered GTM orchestration and activation platform,” said Narciso.

If you’d like to learn more about how MongoDB helps startups build faster and scale further, check out MongoDB for Startups. And for more information about how AWS and MongoDB work together to help organizations accelerate the development of modern applications, see the AWS MongoDB Partner page.

Next Steps

To learn more about how others are innovating with AI, check out the Building AI with MongoDB case study series. You can also register for MongoDB Atlas and visit the MongoDB Vector Search on Atlas Quick Start guide to start building smarter searches or get started on gen AI in your next project.