To illustrate the process, we will focus on creating an AI Sales Copilot that is adapted to your domain and business needs. The AI Sales Copilot will automatically generate meeting notes, enrich CRM data on the go, evaluate sales reps’ performance, answer sales queries, and assist the sales team in real time during prospect calls.

Generate Meeting Notes

In the fast-paced world of sales, taking comprehensive meeting notes during client interactions can be a distracting chore. Moreover, the post-meeting process of converting scribbled notes into a format suitable for CRM entry compounds the workload. 

Having an AI Sales Copilot to automatically generate meeting notes addresses the above challenge. You have two methods to achieve this with Symbl.ai:

1. Out of the box: As a swift and efficient solution, you can employ Symbl.ai’s prebuilt Insights UI, allowing you to generate meeting notes for your audio and video calls with minimal API calls. Powered by Symbl.ai’s Nebula LLM, Insights UI provides an intuitive interface replete with key details such as meeting summaries, action items, Q&A, objections, and sentiment analysis. Learn more from our previous blog that goes into details of Insights UI.
   
2. Customization: For those who prefer a tailored approach, Nebula LLM offers flexibility. Using Nebula, just with a few prompts, you can craft meeting notes that precisely match your preferred format and style, ensuring that your notes align seamlessly with your sales process and workflows.

Enrich CRM data

Maintaining an accurate and up-to-date Sales CRM system is paramount for successful sales teams. Sales representatives are often burdened with the time-consuming task of manually updating and managing customer data resulting in incomplete and inaccurate CRM data.

Bring in your own AI Sales Copilot to automate CRM enrichment, effectively extracting vital information from sales calls about contacts within prospect companies and generate concise opportunity summaries. This automation significantly alleviates the manual effort and ensures that your CRM remains a reliable and current resource.

The following architecture diagram illustrates how you can use multiple components from Symbl.ai to automate CRM enrichment:

Evaluate Sales Performance

Evaluating the performance of sales representatives on an ongoing basis is challenging, as it involves manually tracking numerous variables, from communication skills to adherence to the sales process, which can be time-consuming and prone to biases. 

Automating the evaluation process streamlines the task, saving time and providing consistent, data-driven insights, enabling quicker identification of areas for improvement and more efficient coaching.

With Symbl.ai, you have two approaches to enable the AI Sales Copilot do automatic performance evaluations:

1. Out of the box: Symbl.ai’s Call Score API provides a pre-configured scoring system for assessing sales reps’ performance based on predefined criteria such as Communication & Engagement, Forward Progression, Sales Process, and Question Handling. The results are also available on the pre-built Insights UI. To learn more about Call Score API, refer to our previous blog on Call Score.
   
2. Customization: If the criteria or the scoring methodology of Call Scoe API does not work for you, use Nebula LLM to score your sales calls as per your custom criteria and sales process. For instance, if your organization adheres to a particular sales methodology like MEDDIC, you can craft a prompt tailored to the MEDDIC framework, resulting in a call score that aligns precisely with your unique criteria.

Sales Q/A bot

Sales representatives often grapple with delays and inefficiencies when responding to prospect inquiries. Such challenges can lead to missed opportunities, reduced customer satisfaction, and diminished chances of closing deals successfully. 

A Sales Q/A bot as part of your Copilot will serve as an everyday companion to answer all queries that sales representatives have on a day to day basis. To build a Q/A bot, the first step is to construct a knowledge base capable of efficiently addressing a myriad of prospect inquiries, spanning product information, customer interactions, internal meetings, and other pertinent data. Symbl.ai’s Embeddings API plays a pivotal role in converting this wealth of information into vectors, which in turn enables efficient semantic search as shown in the architecture diagram below: 

Once you have your domain knowledge available in an efficient retrieval system, employ Nebula LLM to utilize the domain knowledge when answering sales representatives’ questions. This technique is popularly known as Retrieval Augmented Generation (RAG):

Real-time Assistant

Building on the knowledge base that underpins the Sales Q/A bot above, the subsequent step is to infuse real-time conversations between sales representatives and prospects with this invaluable resource. 

Symbl.ai’s Trackers come into play here, capturing instances where sales representatives may require immediate assistance, such as mentions of competitors, objections, and new product questions. By querying the knowledge base in real time, the AI-driven assistant can supply instant answers, enabling sales representatives to capitalize on the crucial moment when the prospect’s attention is most engaged.

To dive deeper into how to build a real-time assistant as part of your AI Sales Copilot, refer to our detailed blog on Real Time Assist.

Conclusion

To sum it up, we’ve explored how to build an AI Sales Copilot that is tailored to suit the specific needs of your business. Each component of the Copilot can be developed independently, granting you the flexibility to begin where you find most compelling. The architectural frameworks we’ve discussed in this blog can be readily adapted to diverse use cases, spanning contact centers, support teams, and recruitment. If you’re eager to learn more about how to construct your very own AI Copilot, contact us.

The post How to Build an AI Copilot with Symbl.ai appeared first on Symbl.ai.

Overview

At Symbl.ai, we offer an on-prem deployment option for our Nebula large language model (LLM), enabling organizations to deploy and utilize our capabilities within their own infrastructure. This deployment includes the Summary feature, which provides four types of conversation summaries: short, long, list, and topic-based. By deploying Symbl.ai on-premise, organizations can have greater control over their data protection, ensure compliance with regulatory requirements, and customize the solution to fit their specific needs.

Key Advantages

Symbl.ai’s on-prem summary model stands out due to its unique strengths and advantages:

Optimized Performance

Our language model is based on the state-of-the-art transformer architecture, designed specifically for summarizing long, multi-party conversations across various domains. We have optimized our models to deliver LLM-level quality while utilizing 115 times fewer parameters than GPT-3. This optimization significantly enhances efficiency and processing speed, resulting in average latencies that are 5 times lower than comparable models.

Supports long conversations

Our custom transformer-based model architecture addresses the high variance problem inherent in human conversations. This design ensures consistent performance even at longer sequence lengths, allowing you to process multi-party conversation transcripts up to 3 hours in length whereas mainstream comparable models can only process up to 30 mins of conversations.

Cost-effective

Symbl.ai on-prem summary deployment enables cost-effective hardware utilization. You can deploy our summarization model on single instances of cheaper GPUs, costing as low as a few dollars per day, while still processing up to thousands of conversations daily. This results in an effective hardware cost of fraction of a cent per conversation, delivering both efficiency and cost savings.

Data Protection

Our on-prem deployment provides enhanced data protection control. By deploying Symbl.ai on-premises, you can manage sensitive information within your own infrastructure, ensuring data protection control. This level of control enables safeguard against unintended data access, data leaks or data breaches.

Secure, Resilient, and Scalable

Our solution follows security best practices, ensuring that it does not require root user access. We provide secure containerization, while you are responsible for securing your infrastructure. The Symbl.ai container includes built-in health checks and logging mechanisms, enabling you to monitor the solution’s health and resilience. This built-in resilience ensures that the system remains robust and stable even during high-demand scenarios. Additionally, our container-based deployment architecture enables horizontal scaling, allowing you to handle increased workloads efficiently while maintaining optimal performance.

Conversation Intelligence

The on-prem deployment of Symbl.ai provides organizations with four types of conversation summaries:

• Short Summary: Provides a concise overview of a conversation, capturing the key points and highlights. It is useful for quick reference and provides a snapshot of the conversation’s main themes.
• Long Summary: Offers a detailed and comprehensive overview of the conversation, including deeper analysis and nuanced insights. It enables a thorough understanding of the conversation.
• List Summary: Presents the conversation’s key points in a bullet-point format, making it easy to scan and extract important information. It provides a structured summary that allows for quick reference and review.
• Topic-Based Summary: Organizes conversation insights based on the main topics or subjects discussed. It helps identify the primary themes covered in the conversation, making it easier to navigate and focus on specific areas of interest.

Getting Started

To get started with the on-prem deployment of Symbl.ai, organizations should ensure the following:

• Host Environment: Set up a host environment with reliable internet access to facilitate communication with the Symbl server. This communication is necessary for access token generation, usage reporting, and downloading container updates.
• Container Orchestration Platform: Choose a container orchestration platform that aligns with your organization’s infrastructure and requirements. Popular choices include Kubernetes or Docker Swarm, which provide the necessary tools for managing and deploying containers.
• Hardware Requirements: Ensure that your infrastructure meets the hardware requirements for deploying and running Symbl.ai containers. This includes having sufficient CPU, memory, and storage resources to support the containerized deployment.

Next Steps

To explore the on-prem deployment options available with Symbl.ai, contact Sales.

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