How our AI co-pilot sizes a cold room — real engines, not a chatbot's guess
By DigiEntropy Engineering · 2026-03-29 · 6 min read
Watch our AI co-pilot size a cold room and shortlist compressors from one plain-English request — and see why the numbers come from real engines, not a language model's guess.
About this post — Authored by an AI assistant using DigiEntropy's polynomial corpus, the universal compressor predictor, and the project's chart-generation tooling. Charts are produced by Python scripts that read the same database the live site queries; tables and formulas are pulled from the same engineering modules. Findings reflect the project's current dataset and methodology — send corrections or deeper questions to admin@digientropy.com. Ask a general-purpose AI chatbot to "size a cold room and pick a compressor" and it will answer with confident, plausible-looking numbers. The problem: it made them up. For engineering work, a plausible-but-wrong number is worse than no number — it looks right until it costs you a compressor. Our AI co-pilot is built the other way around. It does not invent the figures. It calls the same engines the rest of the platform runs on, and shows you the result as interactive cards you can read and edit. This post walks through one real request — sizing a cold room, then shortlisting a compressor — to show exactly how that works, and where every number comes from. The request You type it in plain English: Size a cold room: 6×4×3 m freezer at −18 °C, 30 °C ambient, 500 kg/day of beef in at −5 °C, on R449A. Then shortlist the 3 most efficient Bitzer compressors. Step 1 — it makes a plan and gathers the inputs The co-pilot does not answer straight away. It lays out a short plan, then turns your sentence into a form. It pulled the numbers you gave into the right fields. Anything you left out, it fills with a sensible default and tags it assumed — 100 mm PIR insulation, a 20% safety factor, 18 hours a day of runtime. You can change any field before it runs. Nothing is hidden, and nothing is computed yet. Step 2 — it calls the real cold-room engine Confirm the inputs and the co-pilot calls the platform's cold-room load model — the same one behind the Cold Room tool. The load comes back as a card, not a paragraph. The total is 3.782 kW, and it shows you the heat balance: transmission 1.468 kW (39%), infiltration 1.114 kW (29%), safety margin 0.631 kW (17%), internal 0.43 kW (11%), and product 0.138 kW (4%). It also derives a sensible design point from the room — −28 °C evaporating, 42 °C condensing. Step 3 — it shortlists compressors from the polynomial engine Without you asking twice, it chains into compressor selection. It scans the 177 Bitzer models indexed for R449A, evaluates each one's capacity and power at −28/42 °C from its EN 12900 polynomial, keeps the 22 that cover the 3.782 kW load with sensible headroom, and ranks…