How Choco uses AI agents to fix food distribution’s messy middle

Food distribution is one of those industries that looks modern on the surface but is held together by phone calls, spreadsheets, and fax machines. Yes, fax machines. Choco, a company that’s been trying to drag this space into the 21st century, recently shared how they’re using OpenAI APIs to build AI agents that actually do something useful.

Choco’s platform connects restaurants with food suppliers. Think of it as a marketplace, but the real value is in the backend: automating the endless back-and-forth of orders, invoices, and inventory updates. The problem is that suppliers still rely heavily on manual processes. A restaurant sends an order, a supplier’s staff types it into their system, and somewhere along the line, mistakes happen. Wrong quantities, missed items, delayed deliveries.

Choco’s team decided to tackle this with AI agents. Not the flashy, chatbot-replacing-customer-service kind, but agents that sit between systems and handle the grunt work. They used OpenAI’s GPT-4 and the Assistants API to build agents that can parse incoming orders, match them against supplier catalogs, and generate the right outputs for each supplier’s legacy system. This is higher than I expected in terms of real-world complexity, because supplier systems aren’t standardized. Each one has its own format, quirks, and missing data.

The key insight here is that Choco didn’t try to build a monolithic AI that understands everything. Instead, they created specialized agents for specific tasks: one agent handles order parsing, another manages inventory lookups, a third deals with exception handling when something doesn’t match. This approach has been tried before in other industries, but applying it to food distribution feels like a solid fit because the domain is narrow enough that each agent can be trained on a focused dataset.

What’s the actual impact? Choco reported that their AI agents reduced manual order processing time by roughly 40%. That’s not a hypothetical number from a press release; it’s based on comparing workflows before and after deployment. Error rates also dropped significantly, which matters because a single wrong order can ruin a restaurant’s evening service. Suppliers saw fewer phone calls asking “where’s my shipment?” because the system caught mismatches early.

But let’s not pretend this is magic. The agents still need human oversight for edge cases. A supplier might send an order with a handwritten note scribbled in the margin, or a restaurant might request a substitution that isn’t in the catalog. Choco’s system flags those for human review rather than trying to guess. That’s the right call. Too many AI projects try to automate everything and end up creating more chaos than they solve.

I also appreciate that Choco didn’t go with a custom model or some expensive enterprise AI platform. They built this on OpenAI’s existing APIs, which means the barrier to entry for similar projects is lower than you’d think. If you’re a developer looking at this and wondering “could I do something similar for my industry?”, the answer is probably yes, assuming you have clean training data and realistic expectations about edge cases.

The food distribution industry isn’t going to change overnight, but Choco’s approach shows that targeted AI agents can chip away at the inefficiencies that have been accepted as normal for decades. It’s not about replacing workers; it’s about removing the soul-crushing repetition that makes people dread their jobs. That’s a win in my book.