AI Agents Can Now Request Cold Storage Quotes and Run Our Calculators — Here's How It Works

Buyers no longer start a cold storage project with a search engine. Increasingly they start it with an AI assistant: "how big a freezer do I need for 25,000 kg a day of frozen fish in Vietnam, and what will it cost?" The assistant answers from whatever it can reach. Until now, that meant scraped brochures, forum posts and outdated price lists — and no path from the answer to an actual supplier.

ColdMatch Group has changed that. Our platform is now machine-accessible: AI agents can query our cold chain data, run our engineering calculators and submit a genuine request for quotation on behalf of a buyer, through open Model Context Protocol (MCP) endpoints. To our knowledge, we are the first industrial cold chain sourcing platform to expose procurement itself — not just content — to AI assistants.

Why this matters for cold chain buyers

Sourcing industrial refrigeration is slow because the early stage is unstructured. A buyer spends weeks converting a commercial idea — tonnage, product, temperature, country — into something suppliers can price. Capacity has to be translated into pallet positions and cubic metres, cooling load estimated, refrigerant strategy chosen, budget bracketed, and only then can an RFQ be written.

That translation work is exactly what a good AI assistant does well, provided it has real data and real tools. When the agent can call a sizing calculator instead of guessing, and read published regional benchmarks instead of hallucinating a price, the buyer arrives at the supplier conversation with a defensible brief on day one instead of week six.

What an AI agent can do on ColdMatch today

The public, read-only endpoint at /api/public/mcp requires no login and is open to any MCP-capable assistant — ChatGPT, Claude, Copilot-style agents or a custom internal tool. It currently exposes four tools:

The authenticated endpoint at /mcp, secured with OAuth, goes further: an agent acting for a signed-in buyer can list that buyer's projects, read a project's detail and create a new RFQ. That last tool is the important one. The agent is not sending an email into a void — it is opening a record inside the same procurement pipeline our specialists work in every day.

Calculators an agent can actually run

ColdMatch publishes more than thirty engineering and commercial calculators: cold room sizing, cooling load, blast freezer capacity, energy consumption, refrigerant comparison, pallet position planning, ROI and total cost of ownership. Humans use them in the browser. Agents now reach the same logic through the estimator tool, and receive structured numbers rather than prose.

That distinction matters. A number an assistant invents cannot be audited. A number returned by a documented calculator, with its assumptions attached, can be checked, challenged and carried into a specification. When an agent tells a buyer that 25,000 kg/day of frozen product at −25 °C with 90 days of cover needs roughly 8,900 m³ and 1,250 pallet positions, that figure came from the same engine our team uses.

From answer to RFQ in one conversation

The commercial breakthrough is continuity. A typical agent-driven session now runs: the buyer describes the project in plain language; the agent calls the estimator to size it; it pulls regional benchmarks to bracket the budget; it reads the relevant country guide for lead times, duties and grid reliability; it drafts the technical brief; and — once the buyer approves — it submits the RFQ through the authenticated endpoint.

From there the process is human again. A ColdMatch specialist reviews the brief, confirms scope and qualifies the project, then approaches vetted manufacturers, EPC contractors and integrators. Comparable quotes come back to the buyer, typically within a few business days. Buyers pay nothing.

Built for GEO, not just SEO

There is a strategic reason we built this. Discovery is shifting from search results to generated answers. Being mentioned in an AI answer is worth little if the assistant cannot act on it. Being *usable* by the assistant — as a tool it can call, with structured data it can cite — turns a mention into a project.

Everything on the platform is built accordingly: published datasets with explicit sourcing, direct-answer blocks on every guide, FAQ and Dataset structured data, and now callable tools. Our cost benchmark pages, country hubs and calculators are all designed to be quoted accurately by a machine and verified by a human.

Trust, limits and human control

Three safeguards are non-negotiable. First, benchmark figures are planning-grade ranges for feasibility, never binding quotations — binding pricing only comes from suppliers through an RFQ. Second, anything that creates or reads a buyer record requires OAuth sign-in, so no agent can open requests anonymously. Third, fair-use rate limits (60 requests/hour public, 300/hour authenticated) keep the service fast and prevent scraping abuse.

And the final safeguard is people. AI compresses the research phase; it does not sign contracts, inspect factories, negotiate warranty terms or arrange financing. Our specialists still do that, and every project passes through them.

How to connect

Point any MCP-compatible client at https://coldmatchgroup.com/api/public/mcp for read-only tools, or https://coldmatchgroup.com/mcp for the authenticated buyer tools. No key is needed for public access. Prefer to work the traditional way? Every one of these tools has a human interface: request a quote, plan a project, check 2026 cost benchmarks or browse country guides.

Whether your next cold storage project starts with an engineer, a procurement director or an AI agent, it ends in the same place: comparable quotes from vetted suppliers, reviewed by people who build cold chains for a living.