AgroCore AI Advisor™ - an AI advisor for companies in the agricultural sector
The customer does not always ask a question during the trader’s business hours. They may compare varieties in the evening, check the label, look for product applications, analyze machine parameters, or consider which solution fits their situation. AgroCore AI Advisor™ provides verified company knowledge in the form of a conversation available 24/7.
It is a specialized advisory system developed by Adagri, combining a generative AI model with a closed knowledge base of the client. AgroCore is a technological extension agricultural marketing - it should help the user to obtain the answer, and the company should retain control over what knowledge this answer is based on.
Why is a regular chatbot not enough?
The general AI model can generate smooth responses, but it does not know all the current documents, products, procedures, and recommendations of a specific company. In the agricultural industry, this gap is particularly important, because the response may relate to technical parameters, labels, deadlines, compatibility, or how to use the product.
AgroCore AI Advisor™ mitigates this problem by working on a controlled knowledge base. The system is supposed to respond based on materials provided and accepted by the client, instead of freely guessing based on the model’s general knowledge.
Digital Safe - a closed knowledge base of the company
The basis of AgroCore is a vectorized knowledge base defined in the current offering as 'Digital Safe'. Documents, catalogs, labels, instructions, FAQs, technical materials, product knowledge, and other sources indicated by the company are included in it.
The system searches for the appropriate fragments and uses them to generate the response. If the question goes beyond the available range, the advisor should interrupt the automatic response and direct the user to the appropriate contact on the company's side, instead of creating the information without a source.
What problems does AgroCore solve?
- repetitive product questions that reach retailers and advisors every day,
- the absence of an expert available outside of working hours,
- difficulty in quickly finding information in extensive catalogs and documentation,
- differences in the way different people in the organization respond,
- loss of a user who did not find the answer on the page and returned to Google or its competitors,
- the need to scale up expert knowledge without proportionally increasing the number of employees.
What might a conversation with an AI advisor look like?
Instead of browsing through dozens of subpages, the user can ask a question in natural language. Depending on the knowledge base, the system can answer, for example, questions about the differences between product variants, available parameters, documentation, usage instructions, compatibility, scope of application, or the next contact step.
It is important that the system does not fool people or make decisions that the company does not want to automate. Its role is to make knowledge more accessible faster and more conveniently, and in situations requiring individual assessment, to pass the conversation to an expert.
Table 1. The boundary between AgroCore’s automatic response and the human role
| Type of situation | The role of AgroCore | The role of man |
| Finding information from approved materials | It responds based on a knowledge base | A person is not needed for a typical informational question. |
| Comparison of parameters available in the database | Organizes and compares information from the client's sources | A person enters when an individual recommendation is needed |
| Question outside the scope of the base | He should not create answers without a source. | Passes the user to the appropriate contact |
| Individual diagnosis or commercial decision | He should not make decisions on his own that go beyond the material | Responsibility is taken over by an expert or a trader |
AgroCore in various categories of agriculture
Production means and agrotechnical products
The advisor can organize information from labels, catalogs, and product materials, making it easier to find the appropriate documents and answers to basic questions.
Machines and technology
The system can help in comparing the parameters of models, equipment, applications, technical materials, and information available in the company's database.
Seeds and varieties
It can facilitate the finding of the characteristics of the variety, traits, recommendations and data contained in the client's accepted materials.
Products for breeding
It can organize the knowledge base about solutions, product variants, and documentation, as long as the answers remain within the limits of information approved by the company.
Control over the source of the answer
The most important advantage of the solution lies not in the fact that AI 'knows everything'. On the contrary - it should only know what the company has consciously made available in the system. This allows you to update the database as the portfolio, documentation, labels, or recommendations change.
This is especially important in an industry where product and regulatory information can change. Updating the source should be simpler than manually correcting dozens of answers that are rigidly stored in a traditional FAQ.
Technology: Gemini and Google Cloud
The current version of AgroCore AI Advisor™ uses the Google Gemini model and the Google Cloud infrastructure. The system is deployed on a Google Cloud account belonging to the client, allowing the company to maintain control over its own environment and knowledge base.
Adagri accounts for the implementation process, while the infrastructure and operational costs of the model are borne directly by the technology provider. This approach limits dependence on a closed subscription to the tool, which is maintained solely by the agency.
15-business-day implementation protocol
- Strategic analysisWe define business priorities, types of questions, scope of knowledge, sources, and tone of communication. We also determine what the system should not do and when it should direct the human being.
- Engine construction and knowledge baseWe configure the environment, the backend, and the vector document database. The materials are organized so that the system can find the correct information during a conversation.
- Integration and testsWe test typical questions, edge scenarios, out-of-range responses, behavior with ambiguous questions, and the stability of the environment.
- Operational launchWe integrate the advisor with the site, provide documentation, and train the personnel responsible for maintaining knowledge and controlling costs.
Human-in-the-loop - AI knows when a human should enter
Product knowledge can only be automated to a certain level. If a question requires an individual diagnosis, a commercial decision, or advice beyond the scope of materials or the responsibility of an expert, the system should direct the user to a human being.
This model allows the team to avoid repetitive questions, but it does not attempt to replace the relationship, experience, and responsibility of the advisor.
AI advisor as a component of the customer journey
Generative tools are becoming another source of information used by customers. On the Adagri blog, we show that the farmer’s decision-making path is now multi-sourced: Google, industry media, social media, advisors, dealers, and AI can all participate in a single decision.
AgroCore allows the brand to maintain greater control over that part of the journey that takes place on its own website. The user does not have to go to an external model to get a response based on the company's materials.
What does the organization gain?
- availability of basic product advice 24/7,
- one source of knowledge for recurring customer questions,
- less burden for traders and experts with simple inquiries,
- control over the materials used for the response,
- the possibility of updating knowledge as the portfolio and documentation change,
- the infrastructure running on the client's Google Cloud account.
Why Adagri?
- The combination of technological expertise with more than 20 years of knowledge of the agricultural market.
- The project of the system from the beginning takes into account the specificities of product knowledge and farmers' decisions.
- The closed knowledge base model limits the free generation of responses without relying on the client's materials.
- The implementation includes analysis, configuration, testing, and integration, not just the installation of a ready-made widget.
- The system remains part of the client’s infrastructure, and operational costs are billed directly to the technology provider.
Most common questions
1. Can AgroCore provide a wrong answer?
Every generative system requires control, which is why AgroCore limits sources to a verified knowledge base. We also design behavior for questions outside the scope—the system then has to direct the user to a human being, instead of guessing.
2. Who is the owner of the infrastructure?
The current implementation model involves launching the system on the client's Google Cloud account. The company therefore retains control over the infrastructure and the accumulated knowledge base.
3. What materials need to be provided?
These can include PDFs, DOCX documents, catalogs, labels, instructions, FAQs, and other digital sources of knowledge. At the beginning, we jointly determine which materials are current and should power the system.
4. Can AgroCore be integrated with an existing website?
Yes. The solution was designed to integrate with the company's official website. We check the technical details during the analysis before implementation.
5. How long does the implementation take?
Adagri's current offer provides up to 15 working days for the efficient delivery of materials, access to the environment, and decision-making on the part of the client.
6. Can the system replace representatives and advisors?
That is not the goal. AgroCore is intended to handle repetitive questions and quickly share knowledge. Issues requiring individual diagnosis, reporting, or the responsibility of an expert should remain within the human sphere.
Share your experts' knowledge even when they are not at their computers.
