Choco Automated Product Distribution With OpenAI, Doubling Sales Productivity

Choco demonstrated how AI-agents transition from supporting tools to critical infrastructure for real business. The product distribution platform integrated OpenAI API into order processing and, according to its own data, now processes over 8.8 million orders annually, reduces manual data entry by 50%, and enables sales teams to work twice as productively without expanding headcount. Choco operates in the food & beverage distribution market and unites restaurants, suppliers, and distributors in a single system. The company reports serving over 21,000 distributors and 100,000 customers across the US, UK, Europe, and the Persian Gulf countries.

The main problem as the business scaled wasn't in interfaces or CRM itself, but in the chaotic incoming stream of orders: they arrived via email, SMS, voice messages, photos of documents, and even handwritten notes. Employees then manually converted all of this into structured orders for ERP systems, which slowed scaling and created constant risk of errors.

As the company explains, the trickiest part wasn't even text or speech recognition. The critical layer was hidden context: individual SKU mappings for each client, familiar units of measure, delivery patterns, and other details typically held in the heads of order desk employees. That's why Choco bet not just on automating routine steps, but on an inference system that could resolve ambiguity at the moment the order arrives. Essentially, it's about transferring operational knowledge from human memory to a software layer accessible 24/7 and independent of any particular shift or employee.

On this foundation, the company launched OrderAgent—an agent for processing multimodal inputs that accepts emails, SMS, images, and documents and transforms them into ERP-ready orders. Soon came VoiceAgent, built on the Realtime API: it allows customers to place orders by phone with latency under a second, including outside business hours.

OpenAI was chosen, according to Choco, for model quality, multimodality, structured responses, and production reliability. Technically, the implementation included speech-to-text, embeddings, and function calling, and on top the team built its own quality evaluation loop: benchmark datasets, continuous monitoring, and A/B testing. This is an important detail: in such systems, it's not enough to "plug in an LLM"—you need to constantly measure where the model fails and how quality changes on real orders.

The results look like a case of not an experiment anymore, but industrial-scale operation. Choco claims that in production, over 200 billion tokens pass through OpenAI, order intake operates around the clock, and the error rate stays low with configurable automation thresholds. Reducing manual data entry to 50% frees employees from mechanical processing and shifts them toward higher-value tasks, and the twofold increase in sales productivity without hiring shows the effect extends beyond operational costs to commercial effectiveness. The UX aspect also matters: customers didn't have to change their habits. They still submit orders in whatever way is convenient for them, and the system adapts.

Choco separately highlights management lessons. First—quality assessment needs to start on day one, even if the team has only 10–20 labeled examples. Second—AI systems need separate observability: regular logs aren't enough if you can't see model inputs, outputs, and reasons for failures. Third—business needs to accept upfront the probabilistic nature of LLMs. These systems don't behave like deterministic code, so user expectations, SLAs, and escalation scenarios need to be designed differently.

This is a fairly mature signal for the market: winners won't be those who added a chatbot fastest, but those who learned to build managed agent processes around complex business data. The main takeaway from Choco's case is simple: AI-agents become a new operational layer in vertical B2B, where value is created not by beautiful interfaces but by the ability to parse messy real-world data streams and turn them into action.

If this model scales across other supply chain segments, the market will see not point automation, but gradual replacement of entire manual functions with systems that can listen, read, clarify context, and execute work independently.