Trustworthy AI for high-stakes engineering
An AI engine for industrial decarbonisation — that proves its own numbers before they reach your board.
Munta models a site's heat and cooling hour by hour, ranks the costed decarbonisation pathways — heat pumps, heat recovery, electrode boilers, thermal storage — and holds every figure to an audit standard before it ships. The result is a review-ready study a capital committee can act on, in about three weeks.
The engine
An AI system built to be trusted with engineering decisions
Most AI gives you an answer. In engineering, an unchecked answer is a liability. Munta pairs frontier language models with a physics-based simulation engine — and a verification discipline that treats the AI's own output as a draft to be audited, not a result to be believed.
Physics + AI, not prose
An hour-by-hour thermodynamic model of the site — steam, hot water, process cooling — drives the analysis. The AI reasons over real engineering, from heat-pump cycle design to whole-site energy dispatch.
Costed, decision-ready pathways
Every viable route is costed and ranked against your payback gate — capital cost, carbon saving, NPV, IRR and simple payback — with Monte-Carlo uncertainty on the headline case.
It surfaces its own errors instead of hiding them
Before a figure reaches you, it is held to an audit standard: every number traces to its derivation, and unverified or unsupported values are stopped, not shipped. On one benchmark the engine under-recommended a real project — and its own checks caught it, before a human did. That is the difference between an AI you can put in front of a board and one you cannot.
The deliverable
The Stage-1 Scoped Pathway Study
One site, one fixed fee. The deliverable a capital committee can act on — and hand to its ESOS Phase 4 assessor.
An 8,760-hour model of your site
Steam, hot-water and process-cooling loads built from your own 12 months of energy and process data — not a spreadsheet average, an hour-by-hour dispatch model.
Ranked, costed pathways
Every viable route — waste-heat heat pumps, heat recovery, electrode boilers, thermal storage — with capital cost, carbon saving, NPV, IRR and simple payback, ranked against your gate.
Cash vs appraisal-carbon, split out
Monte-Carlo uncertainty on the headline pathway, and an honest split showing where a case rests on fuel-bill cash versus a carbon-appraisal value — so no one is surprised at the capital gate.
Board- and ESOS-ready
A review-ready draft with full calculation provenance — every figure traceable to its derivation — suitable as direct input to your ESOS Phase 4 action plan (due 5 December 2027).
Fixed scope. Fixed fee. And a warranty: if a suitably qualified engineer on your side finds a material error in a calculation derivation, we correct it at no charge — and if it changes the study's conclusion, you don't pay for that site.
Pricing scoped per engagement · discussed on the call
Proof, not promises
Measured against real, completed projects
Before it models your site, the engine was tested against three completed, publicly documented industrial heat-pump projects — each rebuilt from the public figures alone, then run unmodified.
Rebuilt the completed heat-pump decision from GOV.UK figures. Matched the technology and the capital-cost scale, and landed a carbon saving inside the published band. On the first run it under-recommended the project — the engine's own validation caught that against the real outcome, fixed it, and re-ran to confirm.
Reconstructed the site's carbon arithmetic from public data and reproduced the published saving to within about 16%. The selected pathway stood up on fuel-bill cash and grant alone — before any carbon-appraisal value was counted.
The like-for-like reconstruction reached 62.6% gas displacement against a published >80% aspiration — inside the acceptance band, and honestly short. Rather than tune the inputs to match, the engine diagnosed exactly where and why the gap sat.
The honesty is the point. A study you can put in front of a board has to show where it is wrong, not only where it is right — the same assurance you would demand of a junior engineer's draft before it goes near a client.
The process
How it works
Three steps, about three weeks from complete inputs.
You share the data
Twelve months of gas and electricity use, a load description, and boiler and chiller schedules. One nominated technical contact.
The engine models, ranks & verifies
An hour-by-hour dispatch model, a ranked set of costed pathways with Monte-Carlo uncertainty, and the cash-versus-carbon split — each figure audited before it lands in the draft.
You get a decision
A review-ready study — board-ready and ESOS Phase 4 input — with every figure traceable to its derivation, plus a walk-through call.
Who's behind it
Engineering judgement, built into the software
Munta is built by a mechanical engineer working in industrial energy and high-voltage utility software — the engine, the methodology and the study are one person's work, deliberately kept rigorous.
The background is thermofluids and simulation — 100+ industrial CFD and energy studies for operators across power, oil & gas and offshore wind — now applied to the harder problem: making an AI system trustworthy enough to sign off. Its defining discipline is provenance: every number in a study traces to its derivation, and the engine's own checks are built to surface its errors rather than hide them — which is exactly what the benchmarks above demonstrate.
Have a published IETF project, or a site facing ESOS Phase 4?
Tell me the site and I'll show you what the engine reconstructs for it — a 20-minute call, no obligation. If a benchmark of your own published project would be more useful, I'll run that too.