Revolutionizing Sales with AI: Glyphic AI’s Journey with MongoDB

When connecting with customers, sales teams often struggle to understand and address the unique needs and preferences of each prospect, leading to ineffective pitches. Additionally, time-consuming admin tasks like data entry, sales tool updates, follow-up management, and maintaining personalized interactions across numerous leads can overwhelm teams, leaving less time for impactful selling.

Glyphic AI, a pioneering AI-powered sales co-pilot, addresses these challenges. By analyzing sales processes and calls, Glyphic AI helps teams streamline workflows and focus on building stronger customer relationships.

Founded by former engineers from Google DeepMind and Apple, Glyphic AI leverages expertise in large language models (LLMs) to work with private and dynamic data. "As LLM researchers, we discovered the true potential of these models lies in the sales domain, generating vast numbers of calls rich with untapped insights. Traditionally, these valuable insights were lost in digital archives, as extracting them required manually reviewing calls and making notes," says Devang Agrawal, co-Founder and Chief Technology Officer of Glyphic AI. "Our aim became to enhance customer centricity by harnessing AI to capture and utilize conversational and historical data, transforming it into actionable intelligence for ongoing and future deals."

Built on MongoDB, AWS, and Anthropic, Glyphic AI automatically breaks down sales calls using established methodologies like MEDDIC. It leverages ingested sales playbooks to provide tailored strategies for different customer personas and company types. By using data sources such as Crunchbase, LinkedIn, and internal CRM information, the tool proactively surfaces relevant insights before sales teams engage with customers.

Glyphic AI employs LLMs to offer complete visibility into sales deals by understanding the full context and intent of real-time conversations. The system captures information at various points, primarily focusing on sales calls and recordings. These data are analyzed by LLMs tailored for sales tasks, summarizing content based on sales frameworks and extracting specific information requested by teams. MongoDB records serve as the main database for customer records, sales call data, and related metadata, while large video files are stored in AWS S3. MongoDB Atlas Search and Vector Search features are integrated, providing the ability to index and query high-dimensional vectors efficiently.

Glyphic AI's Global Search feature uses Atlas Vector Search to allow users to ask strategic questions and retrieve data from numerous sales calls. It matches queries with vector embeddings in MongoDB, utilizing metadata, account details, and external sources like LinkedIn and Crunchbase to identify relevant content. This content is processed by the LLM model for detailed conversational responses. Additionally, MongoDB's Atlas Vector Search continuously updates records, building a dynamic knowledge base that provides quick insights and proactively generates summaries enriched with data from various sources, assisting with sales calls and customer analysis.

Figure 1: How Glyphic AI transforms sales call analysis

Why Glyphic AI relies on advanced cloud solutions for efficient data management and innovation

"I used MongoDB in the first app I ever built, and ever since it has consistently met our needs, no matter the project," says Agrawal.

For Glyphic AI, MongoDB has seamlessly integrated into the company's existing workflows. MongoDB Atlas has greatly simplified database management and analytics, initially involving the team implementing vector search from scratch. When MongoDB introduced Atlas Vector Search, Glyphic AI transitioned to this more streamlined and integrated solution.

"If MongoDB's Atlas Vector Search had been available back then, we would have adopted it immediately for its ease of testing and deployment," Agrawal reflects.

While Agrawal appreciates the benefits of building from scratch, he acknowledges that maintaining complex systems, like databases or developing LLM models, becomes increasingly challenging over time. The AI feature enabling natural language queries in MongoDB Compass has been particularly beneficial for Glyphic AI, especially when extracting insights not yet available in dashboards or analyzing specific database elements.

In the fast-paced AI industry, time to market is critical. MongoDB Atlas, as a cloud solution, offers Glyphic AI the flexibility and scalability needed to quickly test, deploy, and refine its applications. The integration of MongoDB Atlas with features like Atlas Vector Search has enabled the team to focus on innovation without being bogged down by infrastructure complexities, speeding up the development of AI-powered features.

As a small, agile team, Glyphic AI leverages MongoDB's document model, which aligns well with object-oriented programming principles. This allows for rapid development and iteration of product features, enabling the company to stay competitive in the evolving generative AI market. By simplifying data management and reducing friction, MongoDB's document model helps Glyphic AI maintain agility and focus on delivering impactful solutions.

With vector search embedded in MongoDB, the team found relief in using a unified language and system. Keeping all data-including production records and vectors-in one place has greatly simplified operations.

Before adopting MongoDB, the team struggled with synchronizing data across multiple systems and managing deletions to avoid inconsistencies. MongoDB's ACID compliance has made this process far more straightforward, ensuring reliable transactions and maintaining data integrity. By consolidating production records and vectors into MongoDB, the team achieved the simplicity they needed, eliminating the complexities of managing disparate systems.

Glyphic AI's next step: Refining LLMs for enhanced sales insights and strategic decision-making

"Over the next year, our goal is to refine our LLMs specifically for the sales context to deliver more strategic insights. We've built a strong conversational intelligence product that enhances efficiency for frontline sales reps and managers. Now, we're focused on aggregating conversation data to provide strategy teams and CROs with valuable insights into their teams' performance," says Agrawal.

As sales analysis evolves to become more strategic, significant technical challenges will arise, especially when scaling from summarizing a handful of calls to analyzing thousands in search of complex patterns. Current LLMs are often limited in their ability to process large amounts of sales call data, which means ongoing adjustments and improvements will be necessary to keep up with new developments. Additionally, curating effective datasets, including synthetic and openly available sales data, will be a key hurdle in training these models to deliver meaningful insights.

By using MongoDB, Glyphic AI will be able to accelerate innovation due to the reduced need for time-consuming maintenance and management of complex systems. This will allow the team to focus on essential tasks like hiring skilled talent, driving innovation, and improving the end-user experience. As a result, Glyphic AI will be able to prioritize core objectives and continue to develop and refine their products effectively.

As Glyphic AI fine-tunes its LLMs for the sales context, the team will embrace retrieval-augmented generation (RAG) to push the boundaries of AI-driven insights. Leveraging Atlas Vector Search will enable Glyphic AI to handle large datasets more efficiently, transforming raw data into actionable sales strategies. This will enhance its AI's ability to understand and predict sales trends with greater precision, setting the stage for a new level of sales intelligence and positioning Glyphic AI at the forefront of AI-driven sales solutions.

As part of the MongoDB AI Innovators Program, Glyphic AI's engineers gain direct access to MongoDB's product management team, facilitating feedback exchange and receiving the latest updates and best practices. This collaboration allows them to concentrate on developing their LLM models and accelerating application development. Additionally, the provision of MongoDB Atlas credits helps reduce costs associated with experimenting with new features.

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