Nowadays, the entire AI community's products are emphasizing how strong their big models are, but those who have actually used them should know an awkward reality - as @ Openledger HQ mentioned this morning, "86% of companies say that generic AI models cannot meet the needs of specific fields" For example, if you use GPT for financial data analysis, industrial equipment monitoring, and medical document classification, most of the output results either answer incorrectly or rely heavily on prompts to adjust, and in the end, you don't even know if it understands what you want. Of course, many people want to say, isn't an AI model just a process of continuous training. As long as you feed him enough data, he will definitely think what you want and give you what you need. In fact, the most ideal way for individuals is to create their own exclusive AI model that understands your data and adapts to your business. But in reality, problems arise one after another, and the prerequisite for achieving these is that you need massive amounts of data, people to train models, and most importantly, infrastructure to run models, which can basically discourage 90% of people. @Openledger HQ recently launched APIs for three core modules: prompt calling, cost tracking, and model management, which can efficiently solve the above problems. Make data traceable one ️⃣ Prompt to call the interface You can directly call any deployed SLM (Specialized Language Model) through the/v1/completeness interface. Send a prompt and you will receive the result immediately. You can also customize data, maximum token count, and choose which model to use. The scope of adaptation is very wide: AI bots, on chain inference, and even game scripts can be used. two ️⃣ Model management interface By using/v1/models, all existing models can be retrieved, including those trained by yourself and those shared by others. If you want to see detailed information? You can use/model/info to check configuration parameters such as price, inference mode, and access permission group. It even supports team_id management, is compatible with OpenAI tools, and can be grouped by access permissions, making it very suitable for team collaboration and permission control. three ️⃣ Cost and expenditure tracking Calling models on the chain is not free, but the key is where the money goes? OpenLedger provides a completely transparent tracking mechanism: The cost generated from each call will be automatically distributed to two types of people: one is the person providing the data, and the other is the person training and launching the model. You can also use/send/logs to check who used which model, when, and how much money was paid. It can be filtered by user ID, request ID, API key, and time period to achieve transparency throughout the entire process chain. I personally believe that this is the correct way to open up the "AI economy", and in the era of big models, relying solely on inference is useless. For the system, the core of the AI economy lies in the ability to split accounts, trace origins, start production, and leverage data productivity. At least for now, OpenLedger is moving from "model tuning" to "model building+revenue distribution+data property rights" as an on chain economic activity. Both users and developers can benefit from it.