AI Quotation Agent for Singapore Wholesale Distributors: Start With One Workflow

Wholesale sales in Singapore still lives in inboxes. A customer WhatsApps a photo of a parts list. Another emails a PDF with three codes that do not match your catalogue. A third pastes last year's prices into a spreadsheet and asks if you can do better by Friday.

The work is not "write a quote." The work is turn a messy RFQ into a priced draft someone can stand behind.

This is a use-case guide for that first workflow. It is not a second service page for AI agents Singapore or AI ERP Singapore. Those URLs own the commercial head terms. This article owns the inbound RFQ to approved quotation job for distributors.

If you want the broader ops-agent map (documents, tickets, inventory alerts), use AI agents for business operations. Come back here when the first ship is quoting.

SectionWhat a quotation agent is here

Not a chatbot that answers "what is the price of SKU X." Not an unsupervised mail-merge that emails customers.

A supervised quotation agent:

1. Reads the inbound RFQ (email, PDF, WhatsApp export, web form)
2. Matches lines to your catalogue, customer price tier, and live stock
3. Drafts a quotation object in the system you already use
4. Stops. A named person reviews, edits, and sends.

That last step is the product. The model proposes. The salesperson or sales coordinator still says yes. Design that gate with the human-in-the-loop AI approval checklist.

If the work never leaves a chat window, you wanted a lookup tool. If the work has to land as a quotation in SAP Business One, a custom ERP, or your current quoting app, you are in agent territory. That split is on AI agents vs chatbots.

SectionWhy wholesale RFQs are a good first workflow

Distributors are a strong fit when:

• You already have a price list, even if it lives in Excel plus "specials"
• Volume is high enough that retyping is the tax, not the thinking
• A wrong draft quote is recoverable (you did not send it)
• Someone already signs off today, even if that sign-off is a WhatsApp "ok send"

Weak fit, for now:

• Every quote is a unique project negotiation with no catalogue
• You cannot name the system of record for stock or customer pricing
• There is no owner who will review drafts for the first month

A quotation agent that is wrong is an email you did not send. That is why this is a better first ship than a payment, credit, or bank-detail agent. The wrap pattern is the same one in how to add AI agents to an existing ERP.

SectionStart with one workflow, not a quoting platform

The failure mode we see is "AI for sales": a site chatbot, auto-follow-ups, a forecast dashboard, and a quotation writer, all in the same kickoff.

Pick one loop:

Inbound RFQ → structured lines → priced draft → human send.

Leave for later:

• Auto-chasing cold quotations
• Customer portal "instant quote"
• Agent-written emails to the customer
• Reorder suggestions
• Multi-warehouse allocation games

You can add those after the draft is trustworthy. The commercial build for a production agent still lives on AI agents Singapore. If quoting, stock, and documents are the core of the operating system, that conversation belongs on AI ERP Singapore, after this one workflow is real.

SectionThe inbound RFQ to draft quote loop

Capture

Typical Singapore wholesale intake:

• Email with a PDF or Excel attached
• WhatsApp photos of handwritten lists or competitor quotes
• A portal form that still needs a human to price
• A sales coordinator forwarding "pls quote" from a group chat

The agent should accept the channel you already use. Do not invent a new intake app in week one unless the current channel cannot be read.

Match

This is the hard part. The model is not "smart pricing." It is retrieval plus rules:

• SKU, alias, or customer part number against your catalogue
• That customer's price tier, contract, or last-paid
• Stock that is sellable (not just on-hand somewhere)
• Lead time or indent flags when you cannot promise from warehouse
• Minimum order, pack size, and cut-length rules you already enforce

If match confidence is low, the draft must show the unmatched line, not invent a close SKU. Guessing a substitute into a customer quote is how you lose the account.

Draft

The agent writes a quotation record, not an email:

• Header: customer, validity, currency, collection vs delivery if you already store that
• Lines: SKU, description, qty, unit, unit price, notes
• Flags: unmatched items, below-cost, credit hold, stock short
• Source: which RFQ file or email the lines came from

Re-running the same RFQ should update the same draft, not spawn three quotations. That is an integration rule, not a prompt.

Approve

The review screen is the product. The human needs:

• The source snippet next to each line
• The exact payload that will be saved
• Edit, reject (with reason), or approve-and-I-will-send
• A visible "this will not email the customer until you send"

Version one does not auto-send. Encode that. The checklist for who may approve, timeouts, and logs is human-in-the-loop approvals.

SectionWhat you need before you start

Do this on a whiteboard before you pick a model.

| Need | Good enough | Not ready | |---|---|---| | Catalogue | SKUs with aliases and pack sizes | Descriptions only, no codes | | Pricing | Customer group or account price you can query | "Ah Seng knows the price" | | Stock | A system you trust for available-to-sell | Three spreadsheets that disagree | | Customer | Account code the quote will attach to | Quote to a WhatsApp nickname | | Approver | Named role plus a deputy | "Whoever is in the group" |

If three of those rows are "not ready," fix the data or the process first. An agent will only retype the chaos faster.

Wholesale teams that already need pricing tiers, reservation, and a B2B portal should read wholesale distribution software Singapore for the system of record. The agent sits on top. It does not replace that design.

SectionWhat the agent must not do in version one

Print this. Then encode it.

The agent must not:

• Email a price or promise to a customer
• Invent a substitute SKU without a human flag
• Override a credit hold
• Change customer or supplier bank details (it should not even see them)
• Post the quotation as an order
• Apply a discount above the named threshold

Drafting those things for review is fine. Sending them is not. That is the same split as AI agents Singapore.

SectionData in the RFQ: keep the path explainable

RFQs carry customer names, delivery addresses, sometimes identity details on trade accounts, and supplier or competitor pricing you do not want in a public model log.

Before you send files to a hosted model:

• Strip fields you do not need for matching
• Decide whether attachments are stored, and for how long
• Know whether the vendor may retain prompts

That is a data-handling design, not a later surprise. Use the PDPA checklist for LLM features. This article does not replace counsel.

SectionProve it on thirty real RFQs

Do not widen access because a demo looked clean.

• Pull thirty recent RFQs the current team already quoted
• Score each draft: accept, edit, or dangerous
• Include at least one that must fail closed (missing pack size, conflicting price, unknown customer)
• Write down which reject reasons you will fix in data vs in the prompt

If you cannot score it, you cannot operate it. Capability work (retrieval, models) sits on the AI/ML development hub. The approval-first agent product is AI agents Singapore.

SectionWhen this is an agent, and when it is AI ERP

Stay on a single agent when:

• The ledger and stock system are fine
• Quoting is the painful adjacent workflow
• You can wrap SAP B1, Xero-plus-ops, or a custom app

Move the conversation to AI ERP Singapore when quoting is one of several knowledge-work loops you want inside the operating system (RFQ reader, quotation writer, document filing, follow-up, inventory exceptions) with the same approval model.

Do not publish a second page that tries to rank for AI ERP Singapore or AI agents Singapore. Those terms stay on the money pages. This URL is the wholesale quotation workflow.

SectionWhat we will not do in a first sprint

• Replace your ERP so the agent has "one database"
• Let the model email customers on day one
• Train a custom model when catalogue retrieval plus price rules will do
• Promise a cycle-time number we have not measured on your queue

If the first job is deciding wrap vs rebuild, start with add AI to an existing ERP or AI consulting Singapore. Then come back to this workflow.

SectionFAQ

Is this just ChatGPT on our price list?

No. A chat window that answers "how much is this SKU" is a lookup. A quotation agent has to create a draft record, attach it to a customer, and wait for a named approver. Different tools, different risk.

Can we start if stock is only in Excel?

Sometimes, if one sheet is the agreed available-to-sell and someone owns it. If three people each have a different book, fix that first.

Do we need a customer portal first?

No. Portals help repeat orders. They do not parse this week's messy RFQ. You can add a portal later. See wholesale distribution software for that module.

Who should approve the first drafts?

The person who already sends quotes today, plus a deputy. Not "the AI team." Not the MD on every line unless the amount is above a written threshold.

Where do we go if we already know we want this built?

AI agents Singapore for one gated workflow. AI ERP Singapore if quoting sits inside a wider agent layer on the books you already run. Bring one RFQ sample pack, your price-tier rules, and who approves today.

Next step: Scope one supervised RFQ-to-quote loop on AI agents Singapore. If quotations, inventory, and documents are the operating system, continue on AI ERP Singapore.