AI Quote Assistant: From RFQ to Quotation in UAE Trading

An AI quote assistant reads a customer’s request for quotation, whatever form it arrives in, matches each requested line to the seller’s real catalogue, prices it within the company’s margin rules and hands a draft quotation to a salesperson for review. For a UAE trading company that receives bills of quantities from contractors, Excel lists from facilities firms and one-line messages from dealers, it removes the slowest and most error-prone part of selling: reading and retyping the request. This article explains how the assistant works, where it is reliable, where a human stays in charge and what has to be true of the underlying system for it to be useful.

The problem is reading, not pricing

Ask a sales manager in Dubai where quoting time goes and the answer is rarely “deciding the price”. It goes into interpretation. A contractor’s BoQ describes a product in the consultant’s words, not the manufacturer’s. A facilities company’s spreadsheet lists part numbers from a competitor’s catalogue. A dealer writes “same as March but add the big ones”. Each line has to be understood, found in the catalogue, checked for stock and placed on the quote with the right unit, pack size and specification.

A thirty-line RFQ can take an afternoon. A two-hundred-line tender takes days, which is why tenders are often declined or answered late. The pricing step at the end, with a price list and a margin floor, is comparatively quick.

What the assistant does, step by step

Intake in any format

The RFQ arrives as a PDF attachment, an Excel or CSV file, the body of an email, a WhatsApp message forwarded to the shared inbox, or text pasted by the salesperson. The assistant extracts the lines: description, quantity, unit and any stated specification, brand or model. Scanned PDFs are read as images first.

Matching lines to the catalogue

Each extracted line is matched to catalogue items. The matching tolerates misspellings, abbreviations, different word orders and Arabic or English descriptions, and it uses the company’s own history: what was supplied the last time this customer, or any customer, asked for a similar description. Where the match is confident, the item is placed. Where there are candidates, the assistant lists them with a confidence indicator and the salesperson chooses. Where nothing matches, the line is flagged as a possible new item or a substitution opportunity.

Pricing inside the rules

With the lines matched, prices come from the customer’s price list and tier, volume breaks and current promotions. The margin guard compares each proposed price against the live FIFO cost and the floor for that product group. The assistant does not price below the floor; it marks the line for approval and shows the recent selling history so the approver can decide.

Checks before sending

The draft is then reviewed for the things that embarrass a seller later: a quantity that looks like a typo against the customer’s usual order, a unit mismatch between the RFQ and the catalogue, an item that is out of stock with no lead time stated, terms that do not match the customer type, and a missing TRN or validity date. Each is a flag, not a silent correction.

The cover note and the handover

Finally the assistant drafts the cover email in English and Arabic, in the company’s tone, summarising what is quoted, what was substituted and what needs clarification from the customer. The salesperson edits and sends. Acceptance online then reserves stock and continues the normal quote-to-cash chain.

Where the human stays in charge

The assistant proposes; it does not commit. That line is deliberate.

Decision	Who makes it	Why
Which catalogue item satisfies an ambiguous line	Salesperson	Product knowledge and customer relationship
Pricing below the margin floor	Named approver	Policy and accountability
Substituting a brand or model	Salesperson, with customer confirmation	Specification risk on projects
Sending the quotation	Salesperson	The customer is dealing with a person
Accepting a revised specification	Customer	The RFQ is theirs

A review that once took an afternoon takes a fraction of it, because the salesperson is checking a structured draft rather than building one.

What has to be true of the system underneath

An AI assistant is only as good as the data it works from. Four conditions matter.

• A clean catalogue. Items with proper descriptions, units, pack sizes, brands, models and alternates. The assistant cannot match to a catalogue that is itself a mess.
• Live cost and stock. The margin guard needs the current landed FIFO cost and availability by warehouse, which means purchasing, receipts and inventory in the same system as quoting.
• Customer records with history. Price tier, terms, past quotes and what was supplied before, so the assistant can learn what “the usual” means for each account.
• Rules written down. Margin floors by product group, discount authority by role and terms by customer type have to exist as settings, not habits.

This is the practical reason an AI quote assistant inside a trading ERP outperforms a standalone tool: it has the catalogue, cost, stock and customer in reach without integration work.

Privacy, cost control and working without an AI key

Trading businesses are rightly cautious about where their price lists and customer data go. Three design choices address that.

First, data masking: customer names, TRNs and contact details can be masked before any text reaches an external language model, and the matching and pricing logic runs on the company’s own data. Second, cost controls: usage limits per user and per month, so an enthusiastic team does not run up an unexpected bill with the AI provider. Third, a rule-based fallback: if no AI key is configured, or the provider is unavailable, the same intake still works through structured parsing, catalogue search and the margin guard. The assistant becomes faster with a model behind it, but the quoting process does not stop without one.

Beyond the single quote

The same reading capability has uses on either side of the quote. Supplier invoices can be read the same way, with the vendor identified by TRN, lines matched to the purchase order and the three-way match run before a bill is drafted. On the demand side, lead scoring from enquiry behaviour and a pipeline forecast help the sales manager see which RFQs are worth the effort. And Ask e-tijariX, a conversational layer over the business data, answers questions such as “which quotes over a certain value are still open with this customer” in English or Arabic, with the source records shown.

Where Next Orbit fits

Next Orbit Technologies, a software company based in Dubai serving clients across the UAE, the GCC and internationally, offers, implements and supports e-tijariX, whose sales and quoting module includes the AI Quote Assistant described here, reading RFQs and BoQs from PDF, Excel, email and text, building the quote within the margin guard, running pre-send checks and drafting bilingual cover notes, with data masking, cost controls and a rule-based fallback. For the full document chain after the quote is accepted, read quote-to-cash for UAE trading companies. For AI capabilities built to your own specification, see generative AI development in Dubai.

Frequently asked questions

What is an AI quote assistant?

An AI quote assistant is a feature inside quotation software that reads a customer’s request for quotation in its original format, extracts the requested lines, matches them to the seller’s catalogue, applies price lists and margin rules, checks the draft for errors and prepares a cover note, leaving a salesperson to review and send. It shortens quoting from hours to minutes for multi-line requests.

Can an AI quote assistant read a scanned bill of quantities?

Yes, provided the scan is legible. The document is read as an image, the table structure is recovered and each line is extracted with its description, quantity and unit. Lines the assistant is unsure about are flagged for the salesperson rather than guessed, and the original document stays attached to the quotation for reference.

Will the assistant quote below our minimum margin?

No. The margin guard applies to AI-built quotes exactly as it applies to manual ones. A line that cannot reach the floor at the proposed price is marked for approval, with the item’s recent selling history shown, and only a user with the right authority can release it. The assistant never bypasses the approval matrix.

What happens to our customer data when AI is used?

Sensitive fields can be masked before any text leaves the system, usage is capped per user and per period, and every AI action is recorded in the audit log. If you prefer not to use an external model at all, the quoting flow still runs on rule-based parsing and catalogue search, so no data is sent anywhere.

Does the assistant work in Arabic?

Yes. RFQs written in Arabic, English or a mixture are read and matched against a bilingual catalogue, and the cover note is drafted in both languages. The quotation itself prints in English, Arabic or both, with right-to-left layout handled by the document templates.

Key takeaways

• The slow part of quoting is interpreting the RFQ, and that is what an AI quote assistant removes.
• Lines are matched to the real catalogue with confidence indicators; ambiguous matches go to the salesperson.
• Pricing stays inside the margin guard and the approval matrix; the assistant never commits a price.
• Pre-send checks catch quantity, unit, stock, terms and TRN problems before the customer sees them.
• The assistant depends on a clean catalogue, live cost and stock, customer history and written rules, which is why it belongs inside the ERP.
• Data masking, cost caps and a rule-based fallback mean the process works with or without an AI key.

Next step

Next Orbit’s engineers in Dubai can run a sample RFQ through the assistant on Next Orbit’s sample data and show the draft, the flags and the cover note, then discuss how your catalogue and margin rules would be set up. Request a demo or contact us to arrange a session.