Caixun's 2026 semi-annual report disclosed that its AI intelligent inquiry platform will be launched for commercial use in 2025, and introduced the delivery model consisting of Bootcamp co-creation, platform, forward-deployed engineers and continuous operations, clarifying that FDE is Forward Deployed Engineer. The two disclosures provide clues to corporate service research, but are not enough to confirm that all projects for an undisclosed customer are completed by this model.
A business problem may contain multiple reasonable algorithms
The following is ChinaOPC’s independent business analysis and is not Caixun’s undisclosed implementation rules. Customers ask "How many new customers are there this month?" New customers may refer to registration, first order placement or first payment completion. Whether repeated purchases are counted and how canceled orders are handled will also affect the results. The model can understand sentences, but it cannot replace the company in deciding which statistical method it always uses.
When co-creating, you can first ask the questioner to clarify what he is going to do with the answer. Arranging sales follow-up requires looking at potential leads, while analyzing actual deals may require another set of records. Both questions are worth answering, but they should be named separately rather than trying to satisfy all departments with one "new customers" number. Only after the business purpose is clear can we determine what information is needed.
It is best to use a small number of samples to explain the caliber.
A set of specially constructed or approved samples can be prepared for business personnel to judge whether to include them item by item. For example, if two orders are generated by the same customer, should one customer or two transactions be counted? Orders completed across months should be attributed according to the creation or completion time. These examples are only used to explain statistical meanings and cannot be disguised as corporate operating data, nor do they represent the actual use of these algorithms in the original cases.
If two business people give different judgments, they should first reserve their differences and find out the reasons. Some differences stem from different departmental purposes, and some stem from unclear historical rules. Giving the conflict directly to the AI to choose an answer may just cause the question to be obscured by smooth text. One of the values of co-creation is allowing business differences to be seen and discussed.
For computable problems, it is necessary to explain what is not covered at the same time.
"View sales by city" also requires confirming whether the city refers to the place of registration, place of delivery, or place of business ownership. When a certain data sheet only contains online orders, the answer cannot automatically represent all businesses. A small team can list the observation period, statistical objects, scope and non-covered parts in the problem description at the same time to prevent the query results from being regarded as complete facts after leaving the original data range.
After the question is turned into a query, business personnel should be able to understand how the numbers are composed from a small amount of details. If the summary result is different from the original operating report, you can first check the time, objects, repeated records and update rhythm without rushing to determine which party is wrong. AI is suitable for helping locate differential clues, and the final statistical basis used should still be confirmed by the corresponding business person in charge.
Answering the numbers and explaining the reasons are two steps.
The fact that the quantity is lower than last month cannot be explained by inquiries alone as a problem with the product, personnel or channels. The answer can first present the caliber used and the data covered, and then list the possible reasons as questions that require further examination. When there is no corresponding evidence, we do not speculate on the reasons for model generation as business conclusions, nor do we automatically promise to adjust the effect based on a short-term number.
When a small team tries out a question-and-answer service, they can first complete the problem definition, sample judgment, and detailed checks around an indicator that has been asked repeatedly, so that actual users can judge whether it helps the work. The previously unagreed caliber should continue to remain as a matter to be confirmed; when the data is insufficient, clearly stating that the gap itself is also a useful delivery, and the answer cannot be filled in for the sake of demonstrating intelligence.
The Caixun report shows the company's own delivery mechanism and commercial disclosure, and does not disclose enough material for ChinaOPC to audit a certain customer's income. This article does not refer to large enterprises as OPC. What is suitable for readers to learn from is to break down a daily business question into issues that can be understood by both parties, rather than copying the manufacturer's scale or service commitments.
Extended reading: Understanding AI startup numbers and How to determine fields when AI organizes product data, to understand the relationship between statistical objects and the caliber of original data.