Search Past RFQ Using AI

Why every custom steel manufacturer needs instant access to old quotes specs and emails

If you sell custom steel parts you already know the real bottleneck. The customer asks for a quote and your team starts hunting. Which grade did we supply last year for a similar shape. What tolerance did the customer finally agree to. Which supplier gave the best lead time for that rare dimension. The information exists somewhere across old emails shared drives ERP notes and scattered PDFs. Finding it fast is the hard part.

This is where search past RFQ becomes a mission critical workflow. The ability to search past RFQ with AI and get a precise answer in seconds is the difference between a same day quote and a delayed response. It is the difference between confident pricing and guesswork. It is the difference between winning the order and losing it.

Mavlon is an AI sales engineer for custom steel manufacturers. It connects to your company knowledge across email ERP and product sources and lets any seller or sales engineer ask a question in natural language. The goal is simple. Search past RFQ across your history and get the single best answer with the exact lines evidence and context you need to quote now.

This guide explains why search past RFQ matters what makes it hard how AI changes the game and how to roll it out with zero drama. No fluff. Just practical advice from real build work in custom steel.

What does search past RFQ mean in practice

Search past RFQ means a seller can type or paste a new request and ask questions like these

• Do we have a record of U channel in austenitic grade supplied to this customer

• What mill tolerance did we accept for 60 by 15 flat in 304

• Which previous RFQ from this region had similar thickness and finish

• What lead time did we quote for the last order with tight straightness

• Which supplier responded fastest for a comparable profile

Under the hood the AI reads the request understands shape grade dimension finish tolerance quantity destination and promised date. Then it searches all past RFQs emails and specs. The output is a compact answer with direct citations to the original messages or files. That is the core of search past RFQ.

Why search past RFQ is hard without AI

Finding answers in old data sounds simple but the reality is messy. Here are the common blockers

• Naming chaos

Customers write 1.4307 while your catalog shows 304L. Another email says AISI 304L. Someone else wrote 1 comma 4307. All of these point to the same grade but exact text matching fails.

• Shape and size variants

A request may ask for UPE 100 but your previous work was UPE 80 or UPN 100 or a welded equivalent. Humans can spot families of shapes. Simple search rarely does.

• Attachments everywhere

Critical details sit inside PDFs Word files and images from messaging apps. Many tools ignore these. Humans have to open each file and read it.

• Thread sprawl

One RFQ might span ten emails over eight weeks. Replies contain corrections exceptions or negotiated tolerances. The useful answer is often in the seventh message.

• Tribal knowledge

Experienced sellers remember that a particular supplier is good for a certain grade at a certain month. New team members do not know this yet. Search past rfq should surface this context automatically.

How AI makes search past RFQ reliable and fast

An AI sales engineer like Mavlon does three big jobs for search past rfq

One it turns messy text into structured meaning

When someone writes Can we quote 1.4307 flat bar 60 by 15 with HRC around mid range the AI parses it into grade shape width thickness hardness target and other constraints. The same happens for your old emails and attachments. Once both sides are structured the AI can compare new intent to old reality.

Two it normalises the language

One grade becomes a single identifier across all its names. Units are unified. Common steel terms in English German Italian and Spanish are mapped into one frame of reference. Now search past rfq works even if the original request mixed languages and units.

Three it ranks past work by similarity and outcome

The AI finds the closest past orders and quotes and shows the ones that led to a win or a shipment. It does not stop at keyword overlap. It looks at dimensions tolerances finish region supplier timing and seasonality. Then it explains why a result is relevant so you can trust the answer.

What a good search past RFQ answer looks like

A strong answer should include

• A direct conclusion you can copy into your reply or quote

• The three most relevant past examples with short reasoning

• Key conditions like tolerance and surface finish and heat treatment if relevant

• Any red flags such as stock gaps minimum order quantity change or special certification

• Clear citations you can click to see the original email or file

This structure gives you speed with the right level of proof. No more guesswork. No endless hunting.

Where the data comes from for search past RFQ

To make search past rfq work Mavlon connects to the systems you already use

• Company email and shared mailboxes for customer threads

• ERP for product and order history

• File storage for drawings and PDFs and Word files

• Website and catalog content for public specs

• Supplier quotes or broker responses when available

All ingestion is read only. Sensitive fields can be masked. Access can be limited by role. Every answer shows sources so auditors and managers can verify.

That is how search past RFQ stays both fast and trustworthy.

The most common questions teams ask

Can we supply this grade in this dimension

The AI checks past supply and feasibility notes then flags availability risks and suggests nearest alternatives. This is the classic search past rfq case.

What price band did we use last season for a similar spec

The AI finds the closest wins and quotes and shows a realistic range. It will call out when underlying costs or lead times have shifted.

Which supplier should we ping first for this request

The AI looks at response speed historic acceptance and fulfilment rate for comparable items. It also notes special certifications and paperwork patterns.

What was the tolerance we used last time for this thickness and shape

The AI reads past threads where tolerance was negotiated and pulls the final accepted value with a pointer to the exact email line.

The day one rollout plan for search past RFQ with Mavlon

You can make search past rfq live without disturbance to daily work. Here is a simple plan

Week one connect sources

IT grants read access to email ERP and file storage. We ingest the last few years with privacy rules. No changes to user workflows.

Week two tune the shape and grade dictionary

We align your internal naming with public standards. We map synonyms and local language. We confirm the unit rules. Now search past rfq will connect the right dots.

Week three pilot with a small group

Pick three sellers and one sales engineer. We measure time to first answer and the number of back and forth messages to build a quote.

Week four enable for the full team

Open access to everyone in sales and pre sales. Add managers in view mode. Create a simple set of saved queries so every new joiner can benefit.

Concrete metrics to watch for search past RFQ

If search past RFQ is working you should see these moves

• Quote cycle time cuts by a big margin

• First response quality improves because hidden constraints surface earlier

• Fewer escalations to senior engineers for routine questions

• Higher win rate when a customer repeats an old request

• Onboarding time for a new seller drops

Track these across thirty days and ninety days. You will build a baseline and a proof of ROI for search past rfq without complex dashboards.

How search past RFQ fits the life of a seller

Picture a normal morning. The inbox has nine new threads. One subject line mentions a profile and a grade you handled six months ago. With search past RFQ you paste the new text into Mavlon and ask three things

• Where have we supplied something close

• What was the winning tolerance and finish

• Which supplier is my fastest path for this region

The AI returns a short answer with three citations. You click to open the exact email where the customer accepted a specific tolerance. You adjust price based on the most recent band. You forward the supplier names to purchasing. Quote goes out before lunch. That is the promise of search past RFQ when it feels like a natural extension of how you already work.

Tactical tips to get the most from search past RFQ

• Keep subject lines meaningful. Include grade shape and key dimension. Future you will thank present you.

• Nudge customers to send drawings as vector or clear PDF. The AI can read images but cleaner inputs give faster certainty.

• Save final agreement lines. The last message in a thread often has the important number.

• Add quick tags in ERP for special approvals and exceptions. The AI will use them to warn future sellers.

• Encourage new sellers to run a search past rfq for every new thread even when they think they know the answer. It builds habit and reduces risk.

Security privacy and control for search past RFQ

Mavlon treats company data with strong security and access control. The platform keeps only what is needed to answer questions. Access is scoped by role. Sensitive emails can be excluded. Every answer carries traceable citations so you can spot and fix a wrong rule quickly. That is essential for search past RFQ in regulated and export sensitive environments.

If you prefer data residency in a specific region Mavlon supports deployment choices that match this need. Talk to us about your legal and compliance requirements and we will keep search past RFQ aligned with them.

What about accuracy and trust

No AI is perfect. The key is accountability. For search past RFQ each answer includes

• Sources you can click

• A short explanation of why those sources match

• A confidence level scaled by similarity and recency

• A way to mark the answer as correct or incorrect

When someone marks an answer the model learns the right pattern for your business. Over time search past RFQ gets sharper for your product mix and customer base.

Ten powerful prompts to try today inside Mavlon

These are simple copy paste starters to show the range of search past RFQ. Use your own grades shapes and sizes.

1. Show past RFQ where we quoted the same grade and a similar thickness with surface finish comparable to this request

2. For this customer name list our last three quotes that shipped and the final tolerance used

3. What was the typical lead time we promised for this grade and shape from this region in the last two quarters

4. Which suppliers responded within two days for comparable requests

5. Provide past order numbers that match this drawing dimension within five percent

6. Give me the most common reasons we declined similar requests in the last year

7. For welded beams of size near this one show the closest match and the accepted straightness requirement

8. List RFQ that mention hardness targets for this grade and thickness

9. Summarize emails where the customer accepted a substitute grade for this shape

10. Find past quotes with the same standard and show the final price band and notes

Every one of these leans on search past rfq and brings you straight to the line of text that matters.

Frequently asked questions about search past RFQ

Will this replace my ERP search

No. ERP remains the system of record. Search past rfq is a fast intelligence layer that reads across systems especially unstructured emails where most context lives.

Can the AI read technical drawings

Yes. Mavlon extracts dimensions and notes from common file types. When a drawing is low quality you will still get a helpful summary plus a flag to review.

What about multilingual customers

Search past rfq handles common European languages and maps technical terms into a shared internal vocabulary. You can search in English and still find an Italian or German thread from last year.

How long does it take to start

Most teams begin with a small data set and get value inside the first month. The core value of search past RFQ arrives as soon as email and key folders are connected.

How do we control access

You decide who can ask questions and which mailboxes and folders are in scope. Sensitive groups can run in separate spaces if needed.

A simple mental model for change management

Think about your people in three groups. Experts who know the old threads by heart. Mid level sellers who can quote common parts quickly. New joiners who are keen but still learning. Search past RFQ respects this reality.

Experts get a memory partner that never sleeps. Mid level sellers stop losing time chasing the right email. New joiners get the confidence to answer with proof. This is not about replacing judgment. It is about putting proof in front of judgment.

The compounding effect of search past RFQ

Every search and every feedback click improves the next answer. Pricing notes become easier to find. Exceptions do not get buried. Supplier performance patterns surface without meetings.

Over a quarter the speed and clarity compound. Customers feel it. They reply faster. Trust grows. Win rate improves. That is the flywheel from search past RFQ.

In the end..

If you want to try search past RFQ inside Outlook with your own data visit the Mavlon site and request a demo.

We will show you your own past quotes and emails linked in a single view and you will see how it feels to answer in seconds.

Search past RFQ should not be a dream. It should be a daily habit. Let us help you make it real.