It's like having a senior Amazon Expert working 24/7.

We offer out-of-the-box connections to Amazon's Advertising, Seller, and Vendor APIs. In the future, we plan to connect to more relevant sources based on our customers' needs. Our Agentic Network's architecture allows us to customize solutions for individual data sources. In our experience, combining various inputs improves the quality of insights. If you want to run our AI Analytics Agents on your own data warehouse (DWH) or connect other APIs, such as market insights or weather reports, we can easily do so and customize the analysis based on the new context and inputs. If you want to connect new marketing channels, such as Google or TikTok, or e-commerce platforms, such as Walmart or Target, we can leverage the existing solution, but we would need to create customized agents to analyze and understand the domain context.

The major advantage of using an agentic structure is that you can create agents for specific tasks and provide them with the necessary tools. This allows us to minimize the problem of hallucination and wrong calculations. We have various agents that do nothing but check and monitor the outputs of the data retrieval and analytics agents. These "critic agents" understand context, perform sanity checks, and act as gatekeepers. If an output fails the check, the network will rerun the tasks. Furthermore, we do not allow the LLMs to perform calculations or write code on their own. They must use tools such as code interpreters and Python libraries to perform specialized tasks, such as statistical models and SQL statements. The results are probably already more reliable than those of a human using an Excel spreadsheet. :D But if they fail, our critic agents will be there to help.

The onboarding process is straightforward and can be completed in a matter of days or weeks, depending on how many data sources we need to connect. If you want to start with the standard Amazon APIs, we only need to connect your accounts to our system, and then we're ready to go. The next step is updating the context and brand knowledge. Every brand, portfolio, and market environment is different, and this step sets us apart from generic summary tools. We feed as much information as possible into the machine and enrich it with regular updates on your goals, budget, and strategies. Then you're ready to use it as you would ChatGPT or Copilot. Alternatively, we can define unique analyses (e.g., ABC cluster, media mix model) and schedule reports. For the onboarding process with your own DWH or integration into your software please read the FAQ point behind this link

Would you like to use your own database? No problem! First, we'll hold a design workshop to map out your requirements and preferences for the end result. We'll also determine what data is available and its quality. Poor data means poor agents. We'll also discuss privacy and usage restrictions and take care of all the formal documents. Once we've agreed on the solution, we'll consult with your data or tech team to determine how we can access the data and establish the connection. Then, we will explain the agents your data structure and update the knowledge bases for your specific case. After a testing and piloting phase, we will roll out the system to your company. Would you like to integrate the solution into your own software? Great! We designed our solution so that you can easily integrate it with your existing software via API. Do you already have dashboards and want a text box next to them explaining everything? It's just one API call. The onboarding process is similar to using your own data, except we'll focus on the jobs-to-be-done and the cases in which you want to implement our agents. In general, the onboarding process is also faster because we don't have to do much on our end if you're happy with our standard connectors. In addition to our standard API, we can customize the feedback and endpoints according to your needs. For example, if you want to trigger a unique, fixed analysis, such as media budget recommendations across ten markets in a certain format, we can do that. In this case we would also create one agent specialized for that request.

At the core of our Agent Network, we analyze your performance data and supplement the process with unstructured information about your brand, business, portfolio, goals, and strategies. Our agents will access a knowledge base that includes important information and can easily be updated by you, e.g., with meeting minutes or budget tables. At the start of a project, we will guide you through setting the foundation, after which you can update it regularly on your own.