Use case · Applied AI for the Industrial Business Layer

A Support Assistant That Knows Your Manuals

An AI assistant grounded in your own manuals, datasheets and support history: it answers technical questions with its sources attached — and says so when it doesn't know.

This use case describes how INDNOV approaches this problem. It is a representative architecture, not a case study of a specific client.

Signs you need this

  • The same technical questions reach support every week, and only a handful of experts can answer them.
  • Answering means finding the right page in the right version of a manual, datasheet or old ticket.
  • New staff take months to become useful on technical questions.
  • Sales waits for support before replying to a customer’s specification question.

The problem

Industrial products come with a lot of documentation: manuals, datasheets, installation guides, certificates, application notes — and years of support tickets. The knowledge exists, but it’s scattered across shared drives, PDFs and help-desk systems, in several languages and versions. Finding the right answer depends on a few experienced people who know where to look. They become a bottleneck, and their knowledge leaves with them.

Generic AI chatbots don’t solve this. They answer fluently from general knowledge, not from your documents, and they can be confidently wrong about the one detail that matters: a torque value, a temperature rating, a compatibility constraint.

The approach: answers grounded in your own documents

The assistant doesn’t rely on what an AI model “knows”. It first retrieves the relevant passages from your own documentation, then has the model draft an answer using only those passages — with a link to each source.

AI support assistant architecture Figure 1. Your documentation is indexed once and kept current. Each question retrieves the relevant passages first; the AI model drafts an answer only from them, with citations, under guardrails and governance. 1. Your knowledge Manuals and datasheets Support tickets and emails Technical notes and FAQs Product data from ERP / PIM Ingestion and indexing split · tagged by product, version, language Knowledge index searchable by meaning · refreshed on change 2. Answering a question Question from a support agent (customers later, if wanted) Retrieve the relevant passages, filtered by access rights AI drafts the answer only from those passages, with citations 3. Output and control Guardrails no reliable source → says so, hands over to an expert Answer with sources checked by the agent before sending Governance access rights respected · EU or private hosting · usage logged for review ratings and corrections improve the docs
Figure 1. Your documentation is indexed once and kept current. Each question retrieves the relevant passages first; the AI model drafts an answer only from them, with citations, under guardrails and governance.

Your knowledge, indexed. Manuals, datasheets, technical notes, support tickets and product data from the ERP or PIM are ingested, split into passages and tagged by product, version and language. The index is refreshed when documents change, so answers follow the current documentation.

Retrieval before generation. Each question first triggers a search — by meaning, not just keywords — filtered by product, version and the user’s access rights. Only the relevant passages reach the AI model.

Answers with sources. The model drafts the answer from those passages and cites them, so the reader can check the original page in one click. A question asked in Spanish can be answered from an English manual, and the other way round.

Guardrails. When no reliable source is found, the assistant says so and hands the question to an expert instead of guessing. Every answer can be rated or corrected, and recurring gaps point to documentation that needs to be written.

How it’s implemented

  1. Knowledge audit and pilot scope. Inventory the documentation and ticket history, agree which product lines and languages go first, and collect a set of real questions with known correct answers to measure quality objectively.
  2. Indexing and the internal assistant. Connect the sources, build the index and deliver the assistant to the support team inside the tools they already use — the help desk, Teams or the intranet. Answer quality is measured against the question set before going live.
  3. Feedback, expansion and optional customer access. Ratings and corrections improve the documentation and the index; more product lines are added; and — only once quality is proven — a version can be opened to customers or distributors, for example in a B2B portal.

Expected outcomes

  • Faster, more consistent answers to technical questions, with the source attached.
  • Experts freed from repetitive questions to focus on the difficult ones.
  • New staff productive sooner, with the company’s knowledge at hand.
  • Visibility into what customers actually ask, and where the documentation falls short.

Risks and trade-offs

  • Answers are only as good as the documentation. Outdated or contradictory documents produce outdated answers; the audit and the feedback loop exist to bring them to the surface.
  • AI can still be wrong. Citations, the “I don’t know” rule and human review for anything customer-facing keep errors visible and contained.
  • Data privacy and confidentiality. Documents and tickets can contain customer data. Access rights are enforced, personal data can be excluded from the index, and EU-hosted or private deployments are available.
  • It’s a product to look after, not a one-off project. Sources change, models improve, and answer quality needs regular measurement.

The core idea

The value isn’t the AI model — it’s your company’s accumulated technical knowledge, made findable. A good support assistant answers from your documents, shows where each answer comes from, and knows when to hand over to a person.

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