Selling solar energy is a mission-driven business, but converting leads into loyal customers isn’t always straightforward. Neighborhood Sun, a fast-growing solar energy provider, was struggling with long response times, manual workflows, and missed sales opportunities. Potential customers often abandoned their journey midway, frustrated by a lack of real-time support and personalization. To solve this, the company needed an AI sales assistant, one that could offer instant, intelligent engagement and guide users seamlessly through the solar enrollment journey.

The problem: drop-offs, delays, and lost opportunities

Neighborhood Sun had a growing pipeline of inbound leads, but converting them efficiently was a major hurdle. The company’s sales process was manual and reactive; inside sales teams followed up with leads via email or phone, but often too late to capture intent. Customers had no way to get instant answers about solar options, eligibility, or local policies. The lack of personalization, especially based on location and energy usage, further eroded trust and engagement.

High drop-off rates, inefficient query handling, and missed follow-ups meant the business was losing both time and revenue. They needed a solution that could offer real-time, location-aware, AI-driven conversations and scale to handle thousands of potential customers without increasing headcount.

The solution: an AI sales assistant to scale sales engagement

GoML helped Neighborhood Sun implement a state-aware, vector-powered chatbot that acts as an intelligent, always-on sales assistant, providing personalized, contextual support at scale.

Real-time, location-based personalization

The chatbot tailors recommendations based on the user’s state, energy needs, and company info, extracted directly from contact links and customer data.

Vector-powered query understanding

Using Titan Text Embeddings v2, the system generates embeddings for user queries and retrieves relevant responses from documents stored in Amazon S3 via OpenSearch Serverless.

Contextual AI conversations

Powered by Claude Sonnet 3.5 on AWS Bedrock, the chatbot generates human-like, coherent responses, from explaining solar credits to handling enrollment queries.

Seamless, serverless architecture

AWS-native services power the experience end-to-end:

• PostgreSQL (RDS) for user credentials and tracking

• ECR and Lambda for deployment and API efficiency

• CloudWatch for monitoring and debugging

The impact: faster conversions, happier customers, and scalable automation

• 35% increase in lead conversion, driven by AI-powered, real-time customer engagement

• 50% drop in lead drop-off rates, as users completed their journey with instant support

• 40% boost in sales team productivity, by automating repetitive queries and freeing up time for high-value follow-ups

Lessons for high-volume consumer platforms

Common pitfalls to avoid

• Relying solely on human follow-ups for inbound lead conversion

• Ignoring local context (like state-level solar incentives)

• Failing to guide customers through complex journeys with clarity

Tips for sales teams

• Use embedding models to power relevant responses from documentation

• Deploy state-aware logic to improve personalization and trust

• Combine chat-based UX with real-time API integrations for speed and context

Ready to build an AI sales assistant for your team?

Let GoML help you design an AI sales assistant that converts leads faster, boosts team productivity, and delivers support that feels personal, even at scale.