RFQR | Devpost

RFQR

Inspiration

Industrial suppliers receive RFQs through messy emails, PDFs, spreadsheets, Gmail, Slack, and Outlook. People must manually decode every line, search catalogs, check certifications, and calculate prices. This process is slow and easy to get wrong.

We wanted to make quoting faster, clearer, and more reliable.

What it does

RFQR turns messy buyer requests into ready-to-send quotes.

It:

Extracts line items from RFQs
Standardizes material, grade, dimensions, quantity, and specifications
Matches items to the supplier catalog
Calculates competitive pricing
Explains the pricing
Flags certifications and compliance requirements
Asks clarifying questions when information is missing
Produces a final quote after everything checks out

RFQR does not guess. If an RFQ says “stainless steel” but does not specify 304 or 316, it asks the buyer.

How we built it

We built RFQR as an automated workflow that connects incoming request channels to a structured quoting process.

The system receives the RFQ, extracts the important details, converts them into a standard format, checks the supplier catalog, evaluates pricing, and generates a quote. It also tracks missing information and sends clarification questions back to the buyer.

Challenges we ran into

The hardest part was handling the many ways people describe the same product.

For example, “304 SS sheet 16 gauge 4x8” and “304 stainless, 0.060 inches, 48 by 96” describe nearly the same item. We needed to normalize these descriptions without losing important details.

We also had to make sure the system did not guess when material grade, dimensions, standards, or certifications were unclear.

Accomplishments that we're proud of

We are proud that RFQR can turn unstructured RFQs into clear, standardized quote data.

We are also proud of the clarification workflow. Instead of silently making a risky assumption, RFQR identifies uncertainty and asks the buyer the right question.

The result is a faster quoting process with fewer errors and better compliance visibility.

What we learned

We learned that industrial quoting is not only a pricing problem. It is also a data-cleaning, product-matching, compliance, and communication problem.

We learned that reliable automation must know when it does not have enough information. Asking one good clarification question is better than sending one bad quote.