AI bot for nomenclature matching
45 USD Looking for a developer to create an AI bot that will process customer requests for fasteners and automatically generate an Excel file (with the prospect of integration with the ERP Delopro).
Project Task
It is necessary to develop a system in which the manager uploads the customer request (text, photo, Word/Excel), and the AI bot:
recognizes items, sizes, strength class, coating, quantity, and units of measurement from the customer's "raw" text;
matches these items with our nomenclature (company product catalog);
if necessary, uses a correspondence table with supplier nomenclature;
converts quantities from customer units (pcs, kg, m, packages, etc.) to our base units (for example, "100 pcs.");
forms a final table (Excel) in a format that can be imported into ERP Delopro as an order.
In the next stage — instead of Excel, a request/order should be created directly in Delopro via API.
Final Table Format
It is necessary to create an Excel file with the following fields (the structure is ready to be clarified together):
Customer data: organization, contact person, phone, EDRPOU.
Original request:
"Product in the request" — as written by the customer (for example, "bolt 1050", "bolt M1050 class 8").
"Unit of measurement in the request" — as specified by the customer (pcs, kg, m, pack, etc.).
"Quantity in the request".
Our nomenclature:
"Our product" — name according to our catalog.
"Product ID" — internal identifier from the catalog/ERP.
"Unit of measurement (ours)" — base unit (for example, "100 pcs.").
"Quantity (ours)" — converted quantity in our units.
Example:
Customer: "bolt 1050" → Our product: "Bolt 1050 DIN 933 cb", ID 2451, unit "100 pcs.", quantity 10.
Customer: "Bolt 1050 class 8", unit "kg", quantity 5 → Our product: "Bolt 1050 DIN 933 cb 8.8", ID 6652, unit "100 pcs.", quantity 1.27 (after conversion from kg).
Nomenclature Matching Features
It is important to consider the specifics:
The same product may appear in requests under different names, in different formats of size notation (M10×50, M10*50, 10х50, etc.).
It is necessary to be able to normalize these variants and find the same product in our catalog.
There is a correspondence database of our nomenclature and the nomenclature of individual suppliers — it needs to be used for matching.
There is a database of order history for specific counterparties.
After identifying the company, it is desirable to suggest options based on what it has ordered before.
The system should "learn" from requests: remember stable formulations and the style of specific clients to improve accuracy over time.
Clarification and Correction Cycle
A mechanism for interaction with the manager is needed:
If the bot does not find an item or makes a mistake, the manager indicates corrections (for example, selects a product from the list or changes the item manually).
After corrections, the bot rebuilds the table taking the changes into account.
After the table is approved, there should be an option to:
download/get the Excel file in the required format,
(in the next stage) automatically create an order in Delopro.
Technologies and Integrations
I am open to suggestions for the stack (implementation based on 8n8 or similar solutions, use of LLM, vector search, etc. is possible).
It is necessary to provide for:
working with our catalog and correspondence database (format: Excel/CSV/DB — we will discuss);
preparing the structure for further integration with ERP Delopro (through Excel import at the first stage, through API — at the next).
What I Expect from the Performer
Experience in developing systems based on AI/LLM (processing text requests, entity extraction, matching with the catalog).
Understanding of b2b wholesale trade tasks, preferably experience with nomenclature (warehouse, spare parts, fasteners, etc.).
Ability to design architecture: modules, API, databases, integration with ERP.
Willingness to briefly describe:
the proposed technology stack,
approach to training/tuning the model,
approximate data schema.
In your response, please indicate:
Examples of similar projects (if any).
Technologies you plan to work with.
Estimated timelines for MVP implementation (provided that I provide the catalog, correspondence databases, and examples of requests).
Client's review of cooperation with freelancer
AI bot for nomenclature matchingWorks quickly! I hope that this quality remains in future projects.
Freelancer's review of cooperation with Evgen Kramarenko
AI bot for nomenclature matchingEvgeny is an excellent client with a clear understanding of technical tasks. He provided comprehensive documentation (tender specifications, lists of fasteners, catalog photos), which allowed me to quickly set up the architecture of the AI parser to meet his needs. We successfully completed the first stage of data digitization and normalization. All processes are transparent, and communication is top-notch. I am happy to continue working on the second stage of creating a Matching Engine for 20,000 items of nomenclature. I recommend collaborating with him.
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7 days602 USD
7 days602 USD
Hello, Yevhen!
Do you have ERP Delopro - do you not have the BAS accounting system? Most likely, you need something like our ePrices+ module for working with supplier prices...
In general, if you do have the BAS accounting system - we can be of help. Contact us, we will consult you. Thank you.
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5 days89 USD
5 days89 USD
Hello
I understood the task. It is necessary to develop an AI system that accepts requests in text, photo, Word, Excel, recognizes positions and parameters, matches them with your nomenclature, uses a database of correspondences, converts units of measurement, generates an Excel file for import into Delopro, and provides for further integration via API.
For the AI, you can use Ollama locally without separate training; it is enough to correctly set up the logic and prompts.
I am ready to start.
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1 day178 USD
148 1 1 1 day178 USD
Good day. I am ready to complete this project and have extensive experience in developing various applications.
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1 day223 USD
296 1 day223 USD
Good day. I am interested in your project. I can implement it, I have relevant experience. Write to me in private.
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7 days267 USD
327 14 0 7 days267 USD
Hello
Recently, I have been specializing in AI - chatbots. I have created several AI assistants for the service order sector (lawyer assistant, dentist assistant...).
Your project can be fully implemented on n8n. This reduces development costs and makes it completely transparent for you. We can complete it in about a week.
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1 day22 USD
220 1 day22 USD
Hello! I can design and implement an AI system for matching fastener nomenclature “client request → your product → Excel for Delopro”, with a cycle of clarifications through the manager and knowledge accumulation about clients.
1) Architecture (modules)
A. Ingest:
Text from email/messenger
Excel/Word (tables)
…
PDF/photo/scan (OCR)
Result: a unified “raw” text + attachments linked to the request
B. Extraction:
Highlighting line-items
Parsing attributes: type (bolt/nut/washers…), size (M10×50), standard (DIN/ISO/GOST), strength class (8.8/10.9), coating (zinc/galvanized/stainless), quantity, units of measurement
Output: JSON with item structure (strictly according to the schema)
C. Normalization:
Standardizing formats: M10×50 == M10*50 == 10x50
Normalization of coatings/classes/standards
Synonym dictionaries + rules
D. Matching with the catalog:
1st pass: exact/rule-based match (if there is ID/article/model template)
2nd pass: similarity search in the catalog (vector-based)
3rd pass: supplier correspondence table (if the request is in “their” terms)
4th pass: counterparty hints (order history, “frequent products”)
Output: top-N candidates + confidence + reasons for selection
E. Unit Conversion:
Coefficient table: kg→100 pcs for specific items (via weight/norm)
Support: pcs, kg, m, packs, boxes, etc.
Conversion logs (so the manager can see how it was derived)
F. Human-in-the-loop (manager edits):
If not found/uncertain: show a list of candidates + filters
The manager selects “the correct product” / adjusts attributes / quantity / units.
After “Approve” — save the matching rule (learning from edits)
G. Export:
Excel strictly according to your Delopro import template
(Stage 2) the same payload → Delopro API
2) Proposed stack
Backend/API: Python + FastAPI
Storage: PostgreSQL (requests, clients, rules, history), S3-compatible storage for files
Search: Qdrant (or FAISS) for the catalog and “similar items”
LLM: structured output + schema validation (Pydantic)
OCR: PaddleOCR/Tesseract (depending on scan quality)
Excel: openpyxl (exact structure)
Manager interface: web (minimal) or Telegram bot (quick and convenient)
3) How the system “learns”
Not “fine-tuning the model” immediately (it’s expensive/long), but making it more practical:
Memory of matches: “how this client usually writes” → “which of our products”
Auto-rules: if the same phrasing occurs N times and the manager selects one and...
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3 days134 USD
2937 73 4 2 3 days134 USD
Hello! I can create such a bot based on Google Sheets with script writing, which will process all the necessary functions!!!
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10 days178 USD
312 10 days178 USD
👋 Yevhen, hello!
We are developing chatbots for businesses that not only respond to messages but also become part of your system.
The bots integrate with:
✅ CRM — to save contacts and customer history,
✅ payment systems — for instant payments,
✅ analytics — so you can see the effectiveness of each dialogue.
📊 This allows you to fully control the process and automate routine tasks.
💬 We are ready to discuss your project and offer a solution that will save time and deliver results.
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