Revolutionizing a Manufacturing Business on Agentforce

The largest producer of structural composites in the United States, the client supplies essential products for bridges, buildings, utilities, and infrastructure across the country.

Forecasting business performance is nothing new for their VPs—they’ve been doing it for years, relying on a range of operational and market indicators beyond just sales figures to anticipate whether revenue goals will be met. They had access to Salesforce’s advanced tools. But there was still one critical element missing-exactly what they needed most.

The Challenge

The Client implemented Salesforce's AI Forecasting and Revenue Intelligence. Though these tools were powerful, they pulled historical data, generated polished dashboards, and spotted trends that were limited to the current month or quarter.

For a large Company like this, it was more like driving with a GPS that only showed the next turn helpful, but not enough to plan the entire journey. They were unsure about meeting their year-end target because they couldn't project further out. This inability meant they were constantly doing manual number crunching, piecing together spreadsheets, and guessing at the bigger picture. However, guessing shouldn't be a strategy in a high-stakes industry like Manufacturing.

The Solution

That's when the Client approached Datamatics.

The Datamatics team came in to implement Agentforce, as the Client wanted a clear, annual forecast, instead of more dashboards to understand if they'd land where they needed to by December.

Here's how Datamatics delivered it:

• Built a Custom Forecasting Model- The team created a new Salesforce object that captured every metric that actually mattered to the Client, such as closed-won opportunities, close ratios, shipments, backlog, and the deals sitting just one step away from closing.
• The Datamatics team pulled in three years of monthly snapshots for each metric, so the AI had rich context to learn from.
• Using Salesforce's Prompt Builder, the Datamatics team crafted a highly specific prompt that explained how all those numbers worked together. Here, the team wasn't just fetching data—they were giving a story the AI could understand and respond to.
• Each month, the AI now produces Conservative, Realistic, and Stretch forecasts. Alongside each, it explains why pinpointing the factors helps or hurts their chances.
• Currently, our solution enables the Client to receive a monthly forecast with visual indicators that show their exact standing against the year-end goal.