How Our Client Overcame Sales Challenges with Antematter's AI Agents and LLM Solutions
Client Background
In this case study, we explore a significant project in which Antematter partnered with a leading organization in the Software Development & Information Services sector to address a critical challenge in modernizing their sales strategy. Facing outdated processes and growing inefficiencies, the client sought a way to integrate AI-driven solutions into their sales operations.
Through the implementation of Antematter’s AI-based real-time sales and RFP agents, our client overcame these barriers, transforming their approach to sales engagement and client interaction. This study outlines the key obstacles, innovative solutions, and lasting impact of local, offline, and open-source LLMs-as-assistants, which not only revitalized their sales approach but reshaped broader industry practices.
The Challenge:
Key Pain Points:
- Information Gaps during Sales Calls:
Sales teams struggled to access real-time information during client calls, particularly when navigating complex regulatory environments such as PFAS, OSHA, and EH&S. The inability to quickly reference key data led to delays in communication, often requiring follow-up calls and missed opportunities to close deals efficiently.
- Manual RFP Completion
The client’s sales department faced inefficiencies in handling RFPs, a process that required sifting through extensive documentation. Completing a single RFP could take up to five hours, reducing the time available for engaging with new clients and stifling productivity.
Business Impact
The cumulative effect of these challenges led to significant setbacks for the company, including:
- A 35% drop in new client acquisition.
- A 13% decrease in existing client engagement.
- Growing frustration within the sales department, leading to a noticeable decline in overall morale and efficiency.
The Solution
Overview
Antematter introduced Project K, a comprehensive suite of AI-driven tools designed to address the client’s unique challenges. Leveraging local infrastructure to self-host large language models (LLMs), this solution empowered the sales team with real-time support tools, including automated RFP processing and a multilingual sales assistant.
This integrated approach enhanced the team’s ability to respond swiftly and accurately during client interactions, while significantly reducing the time and effort required for RFP completion.
Key Features
#1 Real-time Transcription, Translation, Refinement, Prediction, and Text-to-Speech (TTS)
Antematter’s AI-enabled real-time transcription and translation capabilities ensured that sales teams could seamlessly handle multilingual conversations. The system provided predictive suggestions during sales calls, allowing representatives to present the most relevant information based on the conversation’s context.
#2 Live Infolink Module
This feature addressed the core issue of information gaps by offering real-time access to critical data during client calls. The Infolink module fetched relevant product, service, and regulatory information from the company’s databases, enabling sales representatives to provide instant responses to client queries.
#3 Automated RFP Processing
By automating the RFP completion process, Antematter’s solution drastically reduced the time required to fill out RFPs. The AI model parsed and completed the forms based on previous submissions and internal knowledge, alerting the sales team only when additional input was required.
Our Process:
Results
- Enhanced Customer Satisfaction
With faster response times and more accurate information during client interactions, customer satisfaction improved significantly.
- Improved Employee Experience
The automation of time-consuming tasks like RFP completion freed up the sales team to focus on high-value activities, boosting morale and overall efficiency.
Lessons Learned and Best Practices
- Adopt a Prompt-First Mindset
Structuring your prompt and requirements for the LLM upfront is crucial. Start by crafting a detailed first draft, then hand it over to the LLM for refinement. Providing clear structure and requirements alongside the prompt enables the LLM to effectively enhance the content, proving to be an excellent judge of what is necessary for accuracy and completeness.
- Plan for VRAM, Speed, Quality, and Compatibility
Implementing large language models required careful consideration of VRAM, speed, and quality trade-offs. The project demonstrated the need for proactive planning to optimize performance without compromising on efficiency.
About Antematter:
Antematter delivers AI and blockchain solutions that help SMBs automate workflows and open new revenue streams. In under three years, we’ve achieved significant results for multi-million dollar US enterprises across Finance, Healthcare, E-commerce, and Information Services. As a full-stack partner, we manage everything from problem identification to solution deployment and maintenance.
Learn more here.