Single large models have inherent blind spots, and AI hallucinations may cause potential losses: Laohuir adopts the "multi-AI cross error correction" mechanism to make all divergences explicit.

With the penetration rate of generative AI exceeding 36.5%, 515 million AI users in China have completed cognitive education, and market demand is shifting from "whether it can hold conversations" to "whether it can solve complex problems". Data from the State Administration for Market Regulation of China in 2025 shows that there are more than 120 million individual industrial and commercial households nationwide. Together with micro, small and medium-sized enterprises, the total number of self-employed and MSME operators exceeds 190 million. This group generally faces the dilemma of "lonely decision-making": when making decisions alone, there is no one to discuss with, professional consultation comes with high costs and long cycles, traditional single large models have capability blind spots, and wrong suggestions brought by AI hallucinations may cause actual losses. Against this backdrop, "Laohuir", a multi-AI collaboration App tailored for self-employed and MSME operators, emerges as the times require, targeting the incremental market of AI tool upgrading.

From the conception stage, "Laohuir" has been anchored to the real pain points of small and medium operators, and has completed the whole-process development from product design to engineering implementation. At present, the mobile App, multi-model collaboration engine, cost settlement engine, operation backend and official website agreement system have all been fully developed, and the project is now in the preparation stage for angel round financing. Targeting the core pain points of single large models and ordinary multi-model aggregation tools, the project has built three innovative core mechanisms. Different from ordinary tools that only display the answers of multiple models side by side, "Laohuir" creates a multi-AI role collaboration workbench. Users only need to select a scenario, and the system will automatically complete the dynamic mapping of roles and models, match professional roles in corresponding fields with one click to schedule large models, without requiring users to switch Apps and sort out contexts by themselves. On this basis, "Laohuir" has innovatively launched the @model cross-error-correction mechanism, where users can directly @ a specified model to raise doubts about the answers of other models, and fundamentally reduce the risk of AI hallucinations through multi-model cross-validation to make differences directly explicit. Finally, the system will automatically generate a comprehensive conclusion covering four dimensions: consensus, divergence, suggestions and boundaries, output a "consultation report" that can be directly used for decision-making, and form a complete product closed loop.

Compared with single large models, the differentiated advantages of "Laohuir" are clearly visible: a single large model outputs answers relying on a single general capability, which has clear blind spots, requires users to manually design prompts to play professional roles, and only has a single perspective that easily misses key information. If users want to use multiple models, they have to purchase multiple memberships, leading to high switching costs. In contrast, "Laohuir" presets multiple professional expert roles such as legal, fiscal and taxation, and cardiology, and automatically schedules multiple models to draw on each other's strengths. A membership of 38.8 yuan per month enables access to multiple models in multiple scenarios, lowers the usage threshold through scenario-based guidance, and greatly improves the credibility of answers relying on multi-model cross-validation. Its core competitive barriers are built around scenario understanding, role design, multi-model scheduling and cross-validation. At present, the project has been implemented in two core scenarios: the first is the startup operation decision-making scenario. For issues such as equity allocation, it can realize independent output by multiple roles including legal, fiscal and taxation, and marketing experts, cross-remind risks, and output structured decision-making conclusions. The second is the health report interpretation scenario, which solves the pain points of ordinary users that "the outpatient visit only lasts 5 minutes, and multi-disciplinary consultation is only open to inpatients". After users upload the physical examination report, the system will automatically match experts from corresponding departments for interpretation, support cross-department cross-questioning, and finally output comprehensive medical advice with clearly marked risk boundaries. It does not replace doctor's diagnosis, and high-risk indicators will automatically trigger emergency medical reminders.

In terms of market space, the demands of the 190 million self-employed and MSME operators targeted by "Laohuir" have not been fully met, and the health scenario can further cover a wide range of urban people aged 20 to 45, with clear incremental market space. The project plans to promote growth in three stages: focus on completing product verification in three core scenarios within 0-3 months, promote growth in vertical scenarios within 3-9 months, realize fission through distribution of real cases and sharing of user achievements, launch a light team space within 9-18 months, connect with offline channels such as incubators, fiscal and taxation SaaS, and physical examination institutions, and explore B-end cooperation space. Among them, the health scenario is naturally endowed with social fission attributes, which can realize the communication effect of "one person uses it, the whole family pays attention to it".

In terms of business model, "Laohuir" adopts a membership subscription plus points-based pay-per-use model. New users will be given 5000 points after registration, which can be used to experience 10 to 20 complete collaboration sessions for free. The membership is priced at 38.8 yuan per month, covering all collaboration functions, custom roles, document and report analysis, conclusion export and monthly fixed points reward. Users can purchase extra points after their points are used up. The project strictly controls costs through mechanisms such as tiered cost settlement and unit cost guardrails, so as to avoid heavy users dragging down the unit economy. At present, the basic mechanism for cost control has been fully established.

The project has launched angel round financing, with a planned financing of 5 million yuan, selling no more than 10% of the equity at a post-money valuation of 50 million yuan. 50% to 60% of the raised funds will be used for market promotion, 30% for product development, and 10% to 20% for team operation. The goal is to achieve a monthly revenue of over 3 million yuan within 12 months, complete product-market fit verification, and lay a foundation for subsequent large-scale growth.

Mr. Lin, a self-employed entrepreneur who participated in the early test, said that he used to figure out equity allocation on his own, and the single large model he asked only gave a general answer, which made him always worried about missing tax pitfalls. After completing the consultation with Laohuir, the fiscal and taxation expert directly pointed out the risks he had not noticed before, making him feel much more reassured. Ms. Chen, a user who has experienced the health consultation function, mentioned that she could not understand a lot of abnormal indicators after the physical examination, and queuing at the outpatient clinic only allowed her to consult one department. Using Laohuir, she got interpretations from three departments at one time, and it also helped her clarify which department to register with priority, which is extremely practical.

The founder of "Laohuir" said that the original intention of launching this project is that even the 190 million self-employed operators, even if they run a business alone, should have an expert team that can help them check their decisions. Starting a business is already lonely enough. We hope to use the method of multi-AI collaboration to ensure that every small operator does not have to make decisions alone. This is a gift from technology to ordinary people, and it is also our most simple original intention to build this product.