How Agentforce Is Building Driver Support For Greenlane With 98% AI Response Capability

Supporting drivers today while preparing to scale tomorrow

With logistics and transport rapidly electrifying across North America, Greenlane is building the charging infrastructure to promote a sustainable future and keep commercial vehicle fleets moving.

Greenlane’s goal was to build an intelligent charging ecosystem to simplify charging for drivers and streamline fleet electrification for businesses. But transitioning to operating electric fleets and figuring out where and how to charge them raises plenty of questions. When a call comes in about a charging session or a reservation hold request, answers need to be fast and precise.

However, answering those urgent questions wasn’t as simple as hiring more support representatives. With its first site open and several more planned, Greenlane needed a support model that could deliver reliable service from day one and grow with its charging network. With a tight launch schedule and complex new technology to deploy, the team had limited time to train new service representatives on all the nuances of the business.

Initially, they turned to third-party call centers for support, but the options weren’t quite right. A dedicated call center gave agents the necessary focus, but it came at a high cost. A shared model was more economical, yet representatives were split between many businesses, making it harder to retain the detailed and unique context behind Greenlane's services.

That loss of context had an impact on customers: It could mean a long wait while an agent searched for reference material, followed by an answer that lacked detail. For drivers on tight delivery schedules, those minutes matter and can lead to delays for their customers and loss in revenue. Greenlane realized they needed a way to make specialized knowledge available immediately to existing customers, without building a large support operation before demand required it.

Greenlane was already using the Salesforce ecosystem to power smarter ways of working across almost every department. Sales teams use the platform to manage opportunities, leads, and quotes. Marketing uses it for customer communications and campaigns, while operations use Salesforce and SiteTracker to manage case work, assets, construction progress, and site development.

Slack is also the primary communication layer for Greenlane’s Product and Engineering teams, where nearly all day-to-day collaboration takes place. Dedicated channels for each charging depot bring Product, Operations, Engineering and other internal stakeholders together around live site activity, while shared channels with customers and partners speed up technical integrations, implementation questions and product feedback.

With its Salesforce foundation already in place, Greenlane saw Agentforce as the natural platform for building an agentic customer experience that could scale as its charging network expands.

Turning complex operational knowledge into faster answers

Greenlane chose Agentforce because it could work with the data and workflows already central to their business. Instead of taking engineering resources away from product development to build and maintain a separate AI tool, the team could configure the agent through Salesforce's intuitive interface, update its knowledge base, and refine prompts quickly.

The core build took around two months, and since then, the team has focused on testing the experience with the rigor required for a customer-facing service. It created over 100 scenarios covering the questions drivers are most likely to ask, from how to begin a charge to billing queries and details around onsite access. The team also tested the messy reality of spoken requests: Busy people paraphrase, speak in incomplete phrases, or use different names for hardware. All those minor variations might be obvious to an experienced employee but not necessarily to an AI agent.

To make sure the agent met Greenlane’s standards during the testing phase, each scenario was assessed against three measures: the accuracy of the answers, average handling time, and whether the issue could be resolved without escalation. The early results have been impressive, reinforcing the strength of the new strategy. Agentforce delivered answers 58% faster than Greenlane's shared call center model and was 15% more accurate. What’s more, it could handle up to 98% of calls without the need for escalation.

This testing process continues to strengthen the AI agent’s knowledge base. When a test exposes a gap, the team can add alternative terminology to an article, tighten retrieval logic, or update an instruction. Data 360 helps Greenlane control all of this information, so the agent is guided toward the relevant answer rather than being distracted by unrelated operational data. And if the answer isn’t in the approved sources, the agent knows that it should never invent one.

That combination of curated knowledge, specific guardrails like AI harnesses, and continuous testing matters in a new and technically complex charging environment. It gives Greenlane a scalable foundation for growing and improving the experience before and after its Agentforce launch, while maintaining a clear route to human support for the edge cases that need it. Further, it gives their growing team direct control over how the experience evolves, without a long or expensive hand-off to a third-party developer.

Creating the connected customer experience behind the network

The Agentforce call center is designed to be Greenlane's first responder, with human support available when it’s needed. But the longer-term ambition goes beyond simply answering questions. Because Agentforce sits within a connected Salesforce environment, it can bring together relevant information, including case, asset, and site data, to help customers understand the current state of a charging station.

With that connected environment in place, the team is exploring other use cases, including connecting Agentforce directly to its charge management platform so the agent can help a driver make a reservation or clear a fault remotely.

Looking ahead, Greenlane also sees potential to combine this operational data with live feeds from on-site cameras. These feeds could help the business identify a driver who needs guidance, understand whether a charging bay is genuinely available, or recognize a problem such as a damaged cable.

Agentforce could also bring support insights directly into Slack, helping product and engineering teams spot issues and turn driver feedback into improvements for their services. Internal users could ask operational questions, from the status of a specific charger to an issue at a depot, and receive a contextual answer based on the same data available to customer service. The aim is to build a more responsive service that keeps drivers informed and helps Greenlane learn from recurring issues.

For a growing company, Salesforce gives Greenlane the flexibility to start with a focused Agentforce use case and extend it as its charging network scales. The result is a customer experience foundation that helps its lean team support drivers with greater confidence now, with room to evolve its services and insights.

The connected platform also gives Greenlane a practical way to connect support with the rest of the business. An issue reported by a driver can be understood alongside case information, equipment status, and site activity, while the same data can highlight patterns worth addressing in product development. As the network adds sites and services, Greenlane can add agents as the network grows without rebuilding the customer experience.

Ready to turn customer questions into a deeper relationship? Get started for free with Salesforce Suites or activate Foundations to try out Agentforce today.