AI for B2B distributors: datasheet answers, equivalent parts and clean product data

What inside sales gets from an AI on datasheets

An internal AI for a technical distributor answers inside sales questions from the manufacturers' datasheets in your own files, with the datasheet and page each answer came from. It proposes equivalent parts when an item is discontinued or a customer asks for a second source, and a rule check compares the key parameters before anyone sees the suggestion. The same models read datasheets into structured attributes for your product database and the B2B shop.

This guide is for owners, managing directors, heads of inside sales and product data managers at distributors of electrical, electronic, pneumatic, hydraulic, fastening or bearing products, and at technical wholesalers with tens of thousands of articles from many manufacturers. It explains the three jobs in plain words, what the company has to prepare, where the AI must not decide on its own, and which server carries the work. A manufacturer that wants a selector for its own catalogue will find that case in our guide to an AI product selector for technical sales; a consumer shop with product texts and site search is covered in our guide to an AI server for e-commerce.

Three jobs with different risks

The three jobs use the same models and documents, but a wrong answer costs very different amounts in each.

Our summary of the three tasks described in this article.

A datasheet answer carries its own check: the colleague opens the cited page and sees the value. An equivalent part needs more than that, because the risk sits in what the answer leaves out, a parameter nobody compared. Product data is the slowest to go wrong and the hardest to clean up afterwards, so it needs a review step before anything is written to the ERP.

Datasheet search with a source for every answer

The method is called retrieval-augmented generation, RAG for short. The datasheets are split into passages, each passage is turned into a vector, a list of numbers that captures its meaning, and stored in an index. A question is turned into a vector as well, the nearest passages are retrieved, and a language model writes the answer only from those passages, with a reference to each. Our guide to RAG on company data explains the technique for the IT side.

Datasheets bring three difficulties of their own. The values sit in tables, often across two pages, with footnotes that change their meaning; a converter that turns the table into running text loses the row and column labels. Docling, an open-source document converter whose code is under the MIT licence, states "advanced PDF understanding incl. page layout, reading order, table structure" and "extensive OCR support for scanned PDFs and images", and runs locally. A vision-language model, one that reads page images as well as text, can work from the rendered page instead; Qwen3.8-27B, under the Apache 2.0 licence, is such a model. Our guide to vision-language models for document AI compares these approaches.

The second difficulty is part numbers. Vector search finds meaning and can miss an exact string such as an order code, so datasheet search combines it with keyword search, which our RAG guide calls hybrid retrieval. The third is versions: manufacturers revise datasheets, and an answer from a superseded revision is wrong in a way that looks right. Each passage therefore carries the manufacturer, the order code, the revision and its date, and the answer shows them.

Embedding models, the small models that turn text into vectors, cover many languages. Qwen3-Embedding-0.6B, under Apache 2.0, lists "100+ Languages" and "cross-lingual" retrieval on its model card, so a question in German can find a passage in an English datasheet. Our guide to embedding and reranker servers for RAG lists further models and their licences.

Cross-reference and equivalent parts: AI candidates and a rule check

An equivalent part is the classic inside sales question: the customer's contactor, valve or bearing is discontinued, on allocation or from a manufacturer the customer wants to avoid. A language model can find candidates quickly, because it understands that a "rain-proof enclosure" and an "IP65 housing" may describe the same requirement. It cannot be trusted to compare twelve parameters without missing one.

The working split is this. The AI searches the datasheets and proposes candidates. A rule check, a fixed comparison written by your product managers, then compares the key parameters from structured data: values that must match exactly, values that must be at least as high, values that may differ. For a three-phase contactor that might be coil voltage and mounting (must match), rated current and auxiliary contacts (at least as high) and the manufacturer's accessory range (may differ). For a deep-groove ball bearing it might be bore, outer diameter and width (must match) and seal type and clearance class (must match or be approved).

Our recommended workflow; the parameter lists come from your product managers.

The structured attributes come from your product database, and where they are missing, from the extraction described in the next section. Where a manufacturer publishes its own cross-reference list, that list takes precedence, and the AI should cite it. A replacement in a safety function, an approved component in a customer's drawing or a part with a certification behind it is a decision for the customer's engineer; the AI's table supports that conversation, and the approval stays with the engineer. Where the product database already holds clean attributes, a model that writes database queries can do the comparison step, as our guide to a text-to-SQL LLM server explains.

We supply AI servers built to order and set up datasheet search under our Private AI/ML service; send us a sample of your datasheets and five recent cross-reference requests, and our team will show how the search and the rule check would handle them.

Product data: attributes from datasheets into the shop

Most distributors receive product data in uneven quality: complete attribute files from some manufacturers, a PDF and a short text from others. The same reading model that answers questions can extract attributes from a datasheet into fixed fields: dimensions, ratings, materials, approvals, temperature range. It also normalises units, so that "0.37 kW" and "370 W" end up in the same field, both in kW.

Classification systems give those fields a common shape. ETIM, which describes itself as "the international classification standard for technical products", maintains such a classification model. Where your shop or your customers' purchasing systems use ETIM, the AI fills or checks the fields; it does not decide which class an article belongs to without review.

Two rules keep the data clean. First, every extracted value carries the datasheet and page it came from, so a reviewer can check it in seconds. Second, the AI writes into a review queue, not into the ERP. Where the extracted value contradicts the ERP, the queue shows both, and the product data manager decides. A sample check of a few hundred articles per manufacturer, before the extraction runs over the whole catalogue, shows whether the manufacturer's layout reads reliably. The same pattern serves supplier data for the digital product passport.

Rights, prices and customer enquiries

Three kinds of information need a decision before indexing. Purchase prices, customer-specific conditions and margins stay out of the datasheet index, or sit in a separate index that only permitted staff can search; the permissions are applied when passages are retrieved, as our RAG guide explains, so an instruction to the model is not the protection. Customer enquiries with project names and quantities are confidential business information of your customers, and on an own server they stay with you. And whether manufacturers' datasheets and data files may be stored and indexed in an internal system is a question of your supply and data agreements with each manufacturer; your legal counsel confirms what they allow.

On an own server, sign-in comes from your company directory, so field sales, inside sales and the product data team each see what they may see, and a query log records who asked what. Our guide to preparing company documents for an AI assistant covers folder structure, versions and owners.

The server for datasheet search and part comparison

The load has two parts: indexing the datasheets once, and answering questions during the working day. Indexing is short work. The performance page of NVIDIA's NeMo Retriever text embedding NIM, updated on 17 August 2026, gives 318.9 passages of 1,024 tokens per second for Nemotron 3 Embed 1B in NVFP4 on one RTX PRO 6000 Blackwell Server Edition, at 64 requests in parallel. A distributor with 40,000 datasheets, at an assumed eight passages of 1,024 tokens each, has 320,000 passages; at that rate they take about 17 minutes by our arithmetic. Other embedding models run at other rates, and converting the PDFs takes longer than embedding them, so a test with a few thousand of your own datasheets gives the real figure.

Answering questions is a moderate load. An inside sales team of thirty sends far fewer requests at the same moment than it has members, because each person reads the answer and talks to the customer before the next question. It pays to run this on the company AI server that also carries the general assistant, order entry and quotation work, as our guide to AI order entry from emails and PDFs shows for the order side.

Users-per-card rule (0.9 × driver-visible memory, less 3 GiB per card and less the weights): Qwen3.8-27B FP8 28.75 GiB with FP8 cache, 1.14 GiB per 32K conversation, and 4 GiB we reserve for the embedding and reranker models; gpt-oss-120b 60.8 GiB with 16-bit cache, 0.5625 GiB per 16K conversation. Checkpoint sizes from the Hugging Face file lists, read on 25 October 2026. Power by our estimate (cards, processors and 15 per cent for the rest), confirmed by a measurement on the delivered system.

By our rule one card has 0.9 × 95.6 − 3 = 83.04 GiB for weights and cache. Less 28.75 GiB for the reading model and 4 GiB for the embedding and reranker models, 50.29 GiB remain, which at 1.14 GiB per conversation of 32K tokens is 44 conversations in flight. Qwen3-Embedding-0.6B has 0.6 billion parameters, about 1.2 GB in BF16 by our arithmetic. The vectors are small too: 320,000 vectors of 1,024 dimensions in 32-bit floats take about 1.3 GB before any index, so the index fits in the server's memory with room to spare. Our comparison of vector databases for RAG covers the index itself.

The entry server has two NVIDIA RTX PRO 6000 Blackwell Server Edition cards, two AMD EPYC processors, 384 GB of DDR5 ECC memory and two NVMe drives, and runs from one ordinary 230 V 16 A feed in a server room. Four cards suit a distributor that adds a chatbot for its B2B customers or runs the extraction over the whole catalogue during the day; our guide to a server for a customer service chatbot sizes that part.

What we supply

You can buy this system from us: our team sizes the configuration to your workload and budget and replies within one business day. We build AI servers to order with two, four or eight cards, assembled and tested before delivery, delivered anywhere in the EU on one EU contract and invoice, with manufacturer warranty on every component handled through us; quantities and lead time are confirmed in writing with the quote. Our Private AI/ML service, delivered with our engineering partner Vixen.UNO, sets up the models, the search on your datasheets with citations, access control and the query log. The parameter rules for equivalent parts come from your product managers; we build them into the check.

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