How metal fabricators can reduce RFQ duplication using AI?
- Atishay Jain
- Aug 11
- 8 min read
A quick note before you read
I am writing this like a builder who spends time inside inboxes and shop floor conversations. Simple words. Real problems. Zero fluff. No case studies. Just what works.
In this guide I explain how metal fabricators can reduce rfq duplication using ai with practical steps that start from email only and grow steadily.
What RFQ duplication actually is?
RFQ duplication is when the same or a near identical request shows up again and again across teams, sites, or time. It can be the same part with a slightly different name. It can be a repeat inquiry from a distributor that forgot they asked last month. It can be a drawing that looks new but matches a past job with tiny differences like hole distance or length tolerance.
Why this is a problem
Engineers and estimators keep reworking the same thing, so lead time stretches for everyone.
Sales gives inconsistent answers that create price leakage and awkward follow ups.
Procurement and inventory feel the clutter from extra part records and messy vendor threads.
Management cannot see true demand because duplicates inflate the funnel.
You do not need a giant program to feel the drag. A few repeated items inside a busy month can eat many hours and choke the team during peak season.
Why duplication happens in the first place?
Inbox chaos. RFQs arrive by email, phone notes, and portals. Many arrive in languages other than English. Some have a single line in the subject and the real detail sits in a drawing.
Naming confusion. The same section can be called by different standards. The same grade can be written in many ways. Colleagues remember different nicknames from old jobs.
Attachment sprawl. Drawings live in long threads with many forwards. Links expire. Local file names do not match the item name.
New joiners. A new hire does not know the tribal history of what was quoted last season.
Pressure to respond. When an inbox is full, the safest move is to work the one in front, not to search for a match.
Most of these triggers are small on their own. Together they form a slow bleeding wound. That makes this a perfect target for a narrow AI system.
How AI helps without any heavy integration
You can reduce duplication with a light footprint. Start with email only and a small knowledge pack. Then climb to deeper systems later. Here is the ladder in simple steps.
Step one
Turn every RFQ email into structured context
Detect the language of the email and the attachments
Pull key facts from the body and the subject such as quantity, section family, alloy, length, tolerance, delivery city, and any must haves
Extract tokens from attachments such as standard name, cross section, diameter, hole count, weld length if it is visible in text
Show these facts as chips inside the Outlook sidebar so the rep does not need to scroll the thread again
Step two
Retrieve the best past answers from sent mail and shared folders
Mine previous threads where someone already answered a similar question or sent a similar quote
Rank by closeness to the current context rather than by date
Let the user insert the prior answer into a reply with one click and edit the numbers before sending
Step three
Find near matches across drawings and descriptions
Use text signals first such as part names, section families, and grade synonyms
Add simple geometry fingerprints later such as section dimensions and obvious features read from text on the drawing
Show the top three close matches with a short label like looks similar to job 9147 with weld length close to the current request
Step four
Protect policy and tone
Apply a rule set that blocks hard promises on price and lead time if you do not have a system of record in the loop
Insert a polite disclaimer line when the reply is a quick first response
Keep the language and units from the original email
Step five
Learn from usage
When a user accepts a match or edits a draft, store those signals
Show team leaders a weekly view of duplicate RFQs caught, time saved, and most reused answers
This ladder works even if you start with only email ingestion and a small library of policy documents. No need to touch ERP on day one.
The simple economics behind duplication
You feel duplication as time lost. The business feels it as margin loss and uncertainty. Keep the math clear for a manager.
Every repeated answer wastes a slice of scarce engineering time. Ten repeats a week at fifteen minutes each becomes many hours each month.
Every duplicate RFQ that sneaks into a quote inflates demand and makes the forecast noisy. Planning gets harder and material pricing suffers.
Extra part records and extra replies create confusion in customer service later when the client comes back with a change.
You do not need perfect numbers to start. Track a few obvious counters. Duplicates caught. Minutes saved. Replies inserted. That is enough to make a decision about the next step.
A practical playbook you can run this quarter
Week one and two
Choose one product family where duplicates are common.
Seed a tiny knowledge base with policy PDFs, spec sheets, and standard MOQs.
Turn on email extraction for that family and build the chip header that shows the key fields.
Set up the prior answer search across sent mail and the shared estimate folder.
Week three
Add the one click insert to reply action and a language aware draft reply.
Add a simple rules check that flags risky phrases such as firm price or firm delivery when you do not mean it.
Week four
Launch a pilot with three estimators and one sales lead.
Measure time to first reply, number of replies inserted, and number of duplicates caught.
Review real threads weekly and fix the extraction misses.
Month two
Expand to one more product family.
Add basic geometry fingerprints taken from text in drawings such as section size and hole data.
Start a simple list of near duplicate part names to catch spelling and naming quirks that your team uses.
This plan is narrow on purpose. You want a fast proof that you can reuse knowledge without heavy change management.
The product checklist for a real world tool
If you plan to use or build a tool like Mavlon for this job, hold the product to this list.
It must extract context from the current email and show it as chips at the top of the panel.
It must find similar past threads by meaning, not just keyword.
It must draft a reply in the same language as the sender and preserve units.
It must give you Insert to reply and View sources as first class actions,
It must say I do not know when confidence is low and offer a quick Ask for details template.
It must record basic metrics on time saved and duplicates caught.
It must allow an admin to upload a small library of policy and spec files with version stamps.
It must support a path to deeper integrations when you are ready, not before.
If a tool cannot pass this list, it is a demo, not a solution.
How to keep the knowledge clean without big process changes?
You do not need a multi year governance program. A few simple habits keep the system useful.
Tag sent replies with a simple label such as Answered RFQ so the model can learn from the right data.
Store final quotes and key attachments in a single shared folder with readable names.
Keep a one page policy for MOQs and approval paths and update it monthly.
Appoint a single owner for the tiny knowledge base and give that person ten minutes per day to review new items.
A light touch beats a complicated process here. Your goal is steady reuse, not perfection.
Risks, limits, and how to handle them
Source quality risk. If your historic replies are messy, retrieval will be messy. Cure this by seeding a tiny set of clean source files and improve from there.
Overconfidence risk. Do not let the assistant claim exact price or lead time without a source of record. Keep those as ranges until the right system is in the loop.
Language risk. Automatically translate but allow a human to glance at the draft before sending. Keep technical words simple.
Privacy risk. Respect data boundaries. Keep tenant data separate and avoid sending sensitive attachments to external services unless you have a clear policy.
Drift risk. Product names and policy evolve. Set a monthly reminder to review and refresh the knowledge pack.
These are normal for any small tool in a live sales process. Owning them openly keeps trust high.
How metal fabricators can reduce RFQ duplication using AI?
Here is the core recipe that any team can follow.
Install a sidebar that lives in Outlook and activates on RFQ like subject lines or attachments.
Detect the language and summarise the request in a short human voice.
Extract the key technical facts and show them as chips.
Retrieve the top three prior answers and show a quick reason for each match,
Offer Insert to reply and Draft reply in the sender language as primary actions,
Add a simple rule set that stops risky claims and inserts a standard disclaimer when needed.
Track duplicates caught and time saved automatically in the footer of the panel.
Review the weekly dashboard and tune the extraction patterns and the knowledge pack.
Do not wait for perfect data. Start with email only, then add deeper signals when the first wins are in hand.
Where AI fits inside a quote team?
Think of the assistant as a fast scout. It does three things well.
It skims a long thread and pulls the signal to the top.
It brings you the most likely prior answer and the nearest past job.
It writes the first draft in the right language and tone.
Humans still make the final call on price, feasibility, and special terms. That is fine. The time you save on the first reply and on duplication checks gives you more energy for the parts that really need expert thought.
Why this is not just a search tool?
Search brings back pages and leaves you to read. A narrow assistant for RFQ duplication brings back actions. It gives Insert to reply. It gives Draft reply in the right language. It points at the exact source that supports a policy line. That is what makes it sticky and worth paying for.
How Mavlon approaches this problem?
Mavlon is the assistant I have been building for custom and engineered to order work. The focus is simple. Live inside Outlook. Pull signal from the current thread. Retrieve the best prior answer.
Draft a safe reply you can send. Show the source. Measure time saved. Nothing heavy on day one. The path to deeper integration is there when the pilot proves value.
If you already have tools that hold price and routing data, Mavlon can connect later. If you are not ready for that, it still gives you reuse and duplication control with only email and a tiny library of policy files.
What success looks like in the first month?
First reply time drops by a large percent on the chosen family.
The team inserts suggested replies more than half the time.
The system flags and stops obvious duplicates multiple times per week.
Team leads see clear proof of time saved and fewer repeated questions in stand ups.
When you see those four changes, you have a real result. From there you can decide if you want to add drawing signals or connect price and capacity systems.
Final word
When teams ask for a short answer the short answer is this metal fabricators can reduce rfq duplication using ai by combining email extraction prior answer reuse and light policy checks. RFQ duplication is a boring problem that burns time every day.
The fix is not a giant platform. The fix is a narrow assistant that lives where people already work and helps them reuse what they have already done. Start with email and a tiny knowledge pack. Prove the drop in first reply time and the drop in repeated work. Then climb from there. If you keep the scope honest and the actions tight, you will feel the win inside one quarter.
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