On B2B websites, customers rarely ask about opening hours. They want to know whether a seal can withstand 250 bar, which material is approved for a specific medium, or whether a spare part is compatible with an older model series. An AI chatbot for technical information answers such questions using data sheets, manuals, and product data, cites the source, and asks clarifying questions if details are missing. Where custom engineering or a binding sign-off is required, it hands over to application engineering or sales—complete with all the details it has captured.
Which technical questions arrive on B2B websites
Technical values: Pressure, temperature, dimensions, tolerances, load limits.
Materials and resistance: Which material tolerates which medium, which surface finish suits which environment?
Standards and approvals: Does the product comply with a specific standard, is there a certificate for the application area?
Compatibility: Does the part fit the existing system, an older model series, or accessories from another manufacturer?
Installation and maintenance: Tightening torques, mounting positions, maintenance intervals.
Documents: Data sheet, assembly instructions, declaration of conformity, CAD data.
The answers to almost all of these questions already exist. They can be found in documents that the customer cannot locate, or in the minds of technical experts who are busy with other priorities.
Why search and FAQs fall short here
A search engine delivers documents, leaving the customer to extract the answer themselves. Anyone searching for "seal hydraulic oil 80 degrees" gets twenty data sheets and has to figure out which one fits. An FAQ answers the most frequent questions, but technical inquiries are rarely standard. They are specific, and the correct answer depends on parameters that the customer has to provide first.
A chatbot for technical information works differently: It understands the inquiry in the customer's own words, searches for the relevant section in the documentation, and asks follow-up questions before answering.
When the chatbot asks follow-up questions and when it hands over
It asks follow-up questions when the answer depends on a parameter that is missing: pressure, temperature, medium, series, year of manufacture. An answer without this information would be mere guesswork.
It answers when the details are complete and the answer is available in an approved source. It also cites the source so that the customer can verify it.
It hands over when a dimensioning or custom engineering calculation is needed that goes beyond the documentation, when the customer requires a binding approval, or when the knowledge is missing. Application engineering or sales can then see the previous conversation history and the captured parameters, eliminating the need to ask from scratch.
How you should prepare your data
You don't need perfect data to get started. However, three factors determine the quality of the answers:
Current versions. For every product, it should be clear which data sheet applies. Outdated versions should be archived or marked accordingly.
Variants and series. If values differ by variant, this must be clear in the data, for instance through tables or dedicated sections.
Access permissions. Define which documents can be used for public answers and which are reserved for partners or internal use only.
How scattered documents become a reliable knowledge base is explained in our article From Documents to AI.
How Mercury.ai answers technical information
Mercury.ai consolidates data sheets, manuals, websites, PIM, and other sources in the Knowledge Hub—including technical documentation with images. Mercury Intelligence identifies the inquiry, chooses the appropriate path, and asks follow-up questions if details are missing before generating the answer. Answers are derived from approved sources and are fully traceable back to the source reference. Seamless handover is managed by the Agent Desk, routed by department or language, complete with full context history.
This is in use, for example, at Böllhoff, a specialist in fastening technology with a complex B2B portfolio. Johanna Neumann, Head of Marketing Europe at the Böllhoff Group: "With our chatbot, our customers can find the right product quickly and easily and resolve open questions directly without having to switch channels." Learn more in the Böllhoff Case Study and on the page for Industry.
How you measure whether it works
After launch, analytics show which technical questions arise most frequently, for which products, and where the chatbot could not find an answer. These knowledge gaps serve as a valuable checklist for your product documentation: They show what customers want to know that isn't covered in any data sheet.
Would you like to automate answers to technical questions on your website? Talk to us.
About the Author
Mirco Schmidt
Chief Revenue Officer @Mercury.ai
Mirco Schmidt is Chief Revenue Officer at Mercury.ai. He has over ten years of experience in international and leadership positions, including at the Volkswagen Group, Club Med, and EQS Group, and holds a degree in Marketing Management from the FH des Mittelstands in Bielefeld. His core areas of expertise are project management, marketing and sales, and communications.
