Gorilla Launches AI Margin Intelligence Tools for Energy Retailers

What Gorilla Just Shipped

Gorilla has expanded its Energy Margin Intelligence platform with a new set of AI-powered features aimed at B2B energy retailers. The tools are designed to help commercial teams configure pricing, trace margin movements, and catch calculation errors during implementation—without handing over control of the underlying logic.

The AI functions sit inside Gorilla’s existing commercial data and calculation engine. That’s a deliberate choice: outputs can be audited against the same logic already driving pricing, forecasting, and margin calculations. Users can access the tools directly within Gorilla’s platform or connect via MCP to external tools including Claude and Copilot.

Predefined prompts are included to reduce the learning curve for staff working with complex commercial data structures.

The Problem This Is Solving

Gorilla’s own Energy Margin Intelligence Index 2026—an independently commissioned survey of 600 senior B2B energy executives in Europe—surfaced a telling contradiction. More than 90 percent of respondents said they were confident in their margin reporting accuracy. Yet many also reported delays and gaps when reconciling reported sales with actual financial margins.

Three quarters said siloed teams were a barrier to margin optimisation. Most estimated that between six and ten percent of annual revenue could be at risk due to poor margin visibility and slow decision-making.

That’s not a small number. For a mid-sized energy retailer, that range represents a material commercial exposure—one that better tooling could directly reduce.

Why Auditability Is the Core Differentiator

In energy retail, trust in the numbers isn’t optional. Contracts can include blended rates, pass-through adjustments, Take-or-Pay structures, and highly specific pricing terms. A tool that produces outputs you can’t trace back to source logic is a liability, not an asset.

Gorilla’s approach is to ground its AI in the structured commercial data and calculation logic already running the business. The goal, as the company frames it, is to remove cumbersome analytical work while leaving commercial judgement with the retailer.

That framing matters. It positions the AI as an accelerator for existing workflows rather than a replacement for commercial expertise—a more credible pitch for enterprise energy teams than broad automation promises. For teams evaluating what makes AI outputs trustworthy, that distinction is central.

Recent Traction Worth Noting

The launch comes alongside visible customer momentum:

• CleanChoice Energy, a US renewable energy supplier, selected Gorilla’s matrix-based pricing system to extend acquisition pricing across competitive retail markets including PJM and NYISO.
• Luminus renewed its agreement with Gorilla for another three years, continuing use of its B2B pricing and forecasting tools.
• Gorilla has previously named Engie and Scottish Power among its clients.

The company also won Best Product for Energy & Utilities at the 2026 SaaS Awards, with recognition for connecting pricing, trading, risk, consumption, settlements, and finance data in a single commercial view.

The Broader Context

This release fits a wider shift in the energy sector. Retailers are moving away from standalone reporting tools toward integrated systems that combine pricing, forecasting, contract management, and finance data in one place.

The pressure is real: volatile wholesale prices, increasingly complex product structures, and thin margins mean that leakage between a frontline sales decision and the realised margin can be costly. Gorilla’s July update—a pre-billing calculation layer for industrial and commercial contracts—addressed the same underlying problem from a different angle, automating contract structures before data reaches billing systems.

The AI tools announced now extend that logic into the analytical and decision-support layer.

Who This Is Actually For

This isn’t a general-purpose AI tool. It’s built for commercial teams at B2B energy retailers who are already managing complex pricing structures and need faster, more traceable insight into what’s driving margin outcomes.

If your team is still reconciling margin in spreadsheets, or if pricing, trading, and finance data live in separate systems, the value proposition here is straightforward: connected data with AI on top, grounded in the same logic your business already runs on.

The practical takeaway is this—margin intelligence only works if the underlying data is structured and consistent. Gorilla’s bet is that AI built on top of that foundation is more useful, and more trustworthy, than AI applied to fragmented reporting outputs. For energy retailers evaluating tools in this space, that architectural distinction is worth examining closely.