Kaluza C&I is a platform built for commercial and industrial (C&I) energy retailers, which sell electricity and gas to business customers instead of residential households. Its pricing and quoting capability, Kaluza Pricing & Quoting (KPQ), handles the full C&I pricing and quoting cycle from lead to signed contract, with specialised analytics at its core: Cost-of-supply calculation, scenario modelling, and margin and rate building.
The question energy retailers are asking
The conversation about AI in commercial and industrial energy pricing has shifted. It is no longer a question of whether AI will play a role. For many teams, it already does. The entry points are practical and unglamorous: Cleaning data, processing documents, normalising formats. The harder question is what comes next. Specifically:
How do we use AI to move faster without handing a multi-million-pound pricing decision to a model that could get it wrong?
C&I energy is not a commodity that lends itself to full automation in the way that residential tariffs might. Every large commercial or industrial customer has a consumption profile that is unique to them. The wholesale cost to serve a customer depends on:
Getting it wrong is not an inconvenience – it is a profit and loss event. Whether the deal arrives through a broker or directly from the customer, and whether the rates come from the latest AI model or an abacus, the retailer holds the liability.
Different tools, same humans
C&I energy pricing is, at its core, a trading activity. At the Computer History Museum in California, a Bloomberg terminal sits next to a set of ancient clay trading tokens.
The tools change, the activity does not. The Bloomberg terminal, launched in 1982, was one of the most significant tool changes in financial trading. For the first time, analysts had real-time price data, news, and analytics integrated into a single accessible system. It did not replace the trading desk. It made the trading desk faster and better-informed. The risk decisions stayed human. What changed was the quality of information available at the moment of decision, and the time spent gathering it. AI is the next step in the same progression for energy pricing. It removes the low-value, time-consuming work from the job, so that the people making high-stakes decisions have more capacity to make better ones.
In practice, for a C&I pricing or trading team, that could look like this:
- Load-profile matching: A broker’s site list arrives with no interval meter data, the consumption records used to model a customer’s usage patterns. Without it, pricing is guesswork. AI matches each site to a candidate from a reference library in seconds, scored by quality so the analyst knows what to trust.
- Portfolio risk monitoring: An AI agent watches the live futures curve and shows a risk indicator against every open quote in the pipeline, covering how far the market has moved since the quote was priced and what that means for the margin. Material exposure is flagged for analyst review before the offer reaches the customer.
- Scenario analysis: To compare scenarios and assess risk, a pricing analyst asks a question in plain language. What used to mean hours across spreadsheets comes back as a chart in seconds, drawing on thousands of historical data points, without leaving the platform.
None of that replaces the pricing analyst’s judgment. It accelerates the part of the job that was never judgment in the first place.
Trading teams will embrace AI to move faster, but on large contracts the sign-off stays human. Lower-stakes, reversible tasks are different, and agents will increasingly act on those unattended. What matters is that the platform lets you choose where that line sits.
Built in, not bolted on
For AI to work well inside a C&I pricing workflow, the platform underneath it matters. It needs addressable data, traceable operations, and auditable outputs. KPQ was built that way, not bolted on afterwards. While the rest of the market races to catch up, KPQ has not had to move at all.
What this means for a C&I retailer evaluating AI
As AI tooling becomes more common in the energy industry, the market will increasingly offer two types of AI-assisted pricing products.
- The first sits in front of a pricing platform designed for humans only, replicating its logic at the interface layer.
- The second calls a clean, well-documented API that exposes the full pricing surface.
That second type is the one a trading team will actually be prepared to use, because it keeps the human in the loop at the decision point and gives them the evidence trail that makes accountability possible. Kaluza C&I is built as the second type.
A competitor trying to retrofit a clean API onto a pricing monolith today is starting from a long way behind. For a retailer asking how to adopt AI without betting the business on it, the answer is a platform that gives AI the access it needs and your analysts the control they need. The data library has been compounding for years, and the audit trail is already in place. The Bloomberg moment for C&I pricing is arriving, and Kaluza is ready for it.
Beyond the quote
Pricing is where the commercial risk is set, but it is not where the work ends. Kaluza C&I takes the same approach beyond the quote: Once a contract is signed, billing and contract operations run on the same foundation, so what was priced and approved is exactly what gets billed.
For the wider argument on why retailers should buy the foundation and build what differentiates them, read "The case for an open, AI-first foundation".
