Finding Similar RFQ Using AI for Custom Manufacturing

1. Why “find similar RFQ using AI for custom manufacturing” matters right now

Every custom manufacturer lives or dies by quotation speed and accuracy. You receive a drawing or a rough sketch, you run to historical folders, you open old spreadsheets, you ask the sales engineer who left two years ago, and you still miss the closest twin job that could save hours of estimating.

That waste shows up as lost deals, thin margins, and all-nighter shift work. The search problem is not that data is missing. The problem is that data is buried. Finding similar RFQ using AI for custom manufacturing attacks the burial ground directly. It liberates tribal knowledge sitting in drawings, emails, ERP logs, and half-finished CAD files.

I built Mavlon exactly for that moment when a new RFQ hits the inbox and the purchasing guy says, “Haven’t we made something that looks like this before?”

2. Anatomy of an RFQ search nightmare

Before AI, the hunt for similar RFQ followed a familiar spiral:

1. Sales opens the incoming request.

2. Engineering tries to decode GD&T, tolerances, and surface treatment.

3. Operations asks whether tooling exists.

4. Finance wants a cost baseline from a prior run.

5. Everyone dumps files in a shared drive named “Archive” and prays.
   

The cycle stalls for half a day. Multiply that by dozens of RFQ each month and the annual cost is shocking.

3. Breaking the spiral with AI similarity search

Core idea: Represent every past RFQ as a rich vector that captures geometry, material, tolerance, quantity, and client intent. Store those vectors in a high-speed database so you can query new RFQ instantly.

Steps at a glance:

1. Ingest drawings, BOM, and email context.

2. Extract entities such as profile type, alloy, radius count, hole count, finish, and contract terms.

3. Embed the structured plus unstructured data into a single vector. Think of a coordinate set where each axis measures one slice of the RFQ soul.

4. Store vectors in Qdrant or any dedicated similarity engine.

5. Query with a new RFQ vector and rank neighbours by cosine similarity.

6. Explain the match so estimators see why a certain prior job is relevant.
   

Mavlon automates every bullet above behind one upload button.

4. Inside the tech: how Mavlon sees shapes and sentences

Shape understanding - We use a Vision Transformer that reads contours and dimensions from JPG or PDF drawings. It converts line segments, fillets, chamfers, and call-outs into tokens similar to how a language model treats words.

Sentence understanding - OpenAI GPT family models extract context from email threads: urgency, delivery window, tier one automotive versus mid-size job shop, and risk flags.

Fusion layer - We fuse geometry tokens and text tokens, then fine-tune a SentenceTransformer so that similar RFQ land close together in vector space. This cross-domain embedding is the secret sauce because it respects both physical shape and commercial nuance.

5. Benefits quantified

1. Quote cycle drops from days to hours because estimators start from an existing cost rollup instead of a blank sheet.

2. Win rate climbs; speed plus accuracy wins hearts at procurement desks.

3. Margin improves because you spot underquoted legacy jobs before repeating the mistake.

4. Onboarding time shrinks; new hires lean on AI memory rather than tribal folklore.
   

A beta customer handling precision profiles saw forty percent faster quotation turnaround in the first month.

6. Overcoming typical objections

“Our data is messy.” Everyone’s data is messy. Mavlon’s ingestion layer cleans formats on the fly. Even smartphone photos of paper drawings get converted reliably.

“We already use PDM or ERP search.” Traditional systems depend on exact filenames and tags. AI similarity ignores file names. It learns shapes and language patterns so small typos or naming chaos no longer block discovery.

“We fear security leaks.” Mavlon runs on enterprise-grade encryption. Vectors hold numerical abstractions, not the full drawing. A competitor cannot reverse-engineer your IP from a vector index.

7. Implementation roadmap

Phase one: discovery Collect a sample set of past RFQ across your product families. Ten years of data is ideal, but even six months delivers quick wins.

Phase two: pilot Integrate Mavlon with shared drives or PLM. Configure Qdrant on-prem or choose our managed cloud. Index the sample data. Let estimators try live search on the next batch of quotes.

Phase three: production rollout Automate nightly ingest. Link results back to ERP cost sheets and quoting software. Train the team in two ninety-minute workshops.

Phase four: continuous learning The system re-embeds every closed job with actual cost and lead time. Your similarity search becomes smarter after every shipment.

8. Broader impact on workflow

Faster design for manufacturability reviewsEngineers pull near-twin models to spot machining bottlenecks quickly.

Strategic sourcingPurchasing sees which suppliers handled similar alloys and geometries, cutting vendor scouting time.

Accurate capacity planningOperations estimates run time by looking at prior cycle data from equivalent shapes, avoiding schedule chaos.

Sales narrative boostYou can quote “We built a comparable part for XYZ company last quarter” with proof ready.

11. Future horizon

Generative design feedback will soon merge with similarity search. Imagine uploading an RFQ and receiving not only closest matches but also AI-suggested alternate profiles that lower material waste. Mavlon’s roadmap heads toward that frontier.

12. Closing thoughts

Finding similar RFQ using AI for custom manufacturing no longer belongs in science fiction. It is a practical lever your estimators can pull today. Free your quoting team from endless folder dives and spreadsheet archaeology. Let them think, decide, and close deals while Mavlon digs through the past at silicon speed.

If any part of this guide resonates, reach out. We will help you turn dormant data into decisive wins.