Dhimath’s Agentic AI turns RFQs into ready-to-use automotive quotes
Dec 11, 2025
5 min read
0
35
0
Why RFQs are so hard on sales and engineering teams?
Automotive and manufacturing RFQs are long, dense, and unforgiving. A single RFQ can run dozens of pages of instructions, terms, price sheets, and technical specs—for example, detailed part lists for Ford automotive components or multi-vehicle rental LTAs for agencies like IOM. Teams spend hours just to:
Decode what is actually being asked (parts, quantities, delivery terms, eligibility).
Fill mandatory forms (supplier commitment, price sheets, non-debarment).
Draft compliant, professional responses under tight deadlines.
This is exactly the kind of repetitive, structured work that agentic AI should be able to automate—if the agent can truly “read” RFQs like a human and draft bid-ready outputs, not just generic text.
Dhimath’s agentic approach to RFQs
Dhimath lets you configure domain-aware AI agents that act on your documents and policies rather than being generic chatbots. For this experiment, a specialised “Automotive Quote Drafter” agent was created with:
Private mode:
Reads only uploaded RFQs (e.g., Ford automotive parts RFQ 2023‑078, UN and IOM vehicle-related RFQs).
Parses technical specs, price sheets, eligibility criteria, and forms.
Generates responses strictly grounded in those documents.
Public mode (hybrid):
Adds strategy and market-flavoured guidance (pricing ranges, positioning as compatible supplier).
Never fabricates RFQ clauses; instead, it complements document-based reasoning.
On top of that, prompts were tuned so the agent must:
Identify where the supplier can bid directly vs only as a component/sub-component supplier.
Produce fully numeric financial tables (no placeholders) with unit prices, extensions, subtotals, tax, and totals.
Explicitly avoid misrepresenting OEM dealer status.
The synthetic use case: Tara Engineering (Fictional Supplier)
To avoid exposing any real customer data, a fictional company—Tara Engineering—was created as the protagonist of this experiment.
Tara Engineering Company (fictional):
Precision manufacturer of sprockets, gear shifter forks, levers, and propeller shafts for 2‑wheelers and 4‑wheelers.
Based in India, positioned as a Tier‑2/Tier‑3 precision components supplier in automotive supply chains.
Three public RFQ documents were used as test inputs:
A Ford Automotive Parts RFQ from the City of Everett, listing hundreds of part numbers, categories, and yearly quantities.
A UNDP RFQ for a pick-up vehicle (technical and commercial structure, forms, conditions).
An IOM RFQ for vehicle rental services under an LTA (vehicle specs, driver requirements, pricing tables).
The goal: see if Dhimath’s agent can read these RFQs, and then:
Map them to Tara’s realistic playing field (where can a sprocket/fork/lever/shaft manufacturer actually participate?).
Draft compliance matrices, numeric quote tables, and cover letters, without hallucinating OEM status.
What the Agent actually did with the RFQs
1. Found Realistic Opportunities (Without Over-Claiming)
Given the Ford parts RFQ, the agent:
Scanned the engine parts section for areas compatible with precision machining (e.g., camshaft followers, engine mounts, seals).
Identified parts such as:
3L3Z‑6564‑A – Roller, Camshaft Follower
GB5Z‑6038‑A – Mount, RH Upper Engine
XW4Z‑6700‑AA – Seal, Crank Front
DG1Z‑8501‑D – Water Pump with Gasket
Framed Tara’s role as component supplier (rollers, brackets, seals, gaskets) rather than claiming to supply OEM Ford assemblies.
It also flagged that Tara can:
Play in engine/drivetrain-related assemblies where sprockets, forks, levers, or shafts are used.
Collaborate with OEM dealers or integrators as a precision sub-component supplier, not as the primary RFQ bidder.
2. Produced a Clean Compliance Matrix
A dedicated matrix was generated for RFQ 2023‑078 that finally gets the critical truth right:
“Supply of compatible sprockets, forks, levers, and shafts” – Compliance: Yes (with explanation).
“Authorized Ford Dealer” – Compliance: No (Tara is not an authorised Ford dealer; it is a compatible component supplier).
Other rows cover updated part number handling, RFQ form formats (Form 3.02), and non-debarment certification (Form 3.03), all tied to correct page references.
Numeric Quotes: From RFQ structure to sample pricing
The agent was required to move beyond templates and placeholders—to fill full numeric quote tables for both RFQ parts (for demonstration) and Tara’s proprietary parts.
RFQ-Based Sample Financials
For the Ford RFQ 2023‑078, the agent generated tables such as:
Brakes (sample):
Rear rotor (EU2Z‑2V026‑A): unit price 50.00, yearly quantity 36, extended 1,800.00.
Front rotor (DG1Z1125C / EU2Z‑1V125‑A): unit price 45.00, yearly quantity 28, extended 1,260.00.
Oil and fluids (sample):
Transmission fluid XT‑10‑QLVC: 20.00 × 156 units = 3,120.00.
Anti-freeze VC‑3‑B: 15.00 × 13 units = 195.00.
Wheel parts (sample):
Valve stem kit 9L3Z‑1700‑A: 10.00 × 18 = 180.00.
Clip W720211‑S300: 5.00 × 12 = 60.00.
With automatic calculation of:
Category subtotals (Brakes, Oil and Fluids, Wheel Parts).
Total across categories.
Sales tax at 9.9%.
Final Quotation Total for the demo.
These numbers are illustrative rather than true market prices, but they show Dhimath’s ability to respect RFQ structure and compute full quote math.
Tara’s Own Product Quote
In a second scenario, the agent focused only on Tara’s proprietary parts and built a concise financial offer for:
Sprockets
Gear shifter forks
Levers
Propeller shafts
For example, one summarised table looked like:
Sprockets: 28.00 × 100 = 2,800.00
Gear shifter forks: 42.00 × 75 = 3,150.00
Levers: 18.00 × 120 = 2,160.00
Propeller shafts: 55.00 × 50 = 2,750.00
Total (for those four lines): 10,860.00.
The agent also wrapped this with delivery (e.g., 30 days), a 12‑month warranty, and net‑30 payment terms as a coherent mini-quote.
Submission-ready artefacts and cover letter drafting
Beyond tables, Dhimath’s agent produced the “boring but crucial” content that RFQ responses need:
Checklists and notes referencing Form 3.01 (Supplier Commitment and Information), Form 3.02 (Price Sheet), Form 3.03 (Certificate of Non‑Debarment/Suspension) from the RFQ.
A structured cover letter for “Tara Engineering” addressed to the City of Everett buyer, positioning Tara as a precision parts supplier and referencing the enclosed compliance matrix and price sheets.
“Next steps” guidance: how Tara should review specs, finalise prices, and ensure eligibility docs and licenses align with RFQ demands.
These pieces are exactly what your sales and bid teams normally craft manually after reading through long RFQ PDFs.
Why this synthetic use case matters for real manufacturers
Although Tara Engineering is fictional, the behaviour demonstrated is very real and enterprise-relevant.
With Dhimath’s agentic AI:
Pre‑sales and sales engineering teams can go from “RFQ PDF” to drafted opportunity map + compliance matrix + quote skeleton in minutes, not hours.
Manufacturers and component suppliers can see exactly where they should bid directly and where they must partner (e.g., compatible supplier vs OEM dealer)—reducing both risk and wasted effort.
Leaders can safely prototype this on synthetic companies and public RFQs first, then move to real data once governance, access control, and legal review are in place.
Looking ahead
The Tara Engineering scenario is synthetic, but the RFQs, workflows, and pain points are very real for manufacturers, OEMs, and suppliers. Dhimath’s agentic approach shows how teams can move from manual RFQ reading and quote drafting to AI‑assisted workflows, where humans review and finalize outputs instead of starting from a blank page.
If you are exploring how to bring similar RFQ, sales, or data‑driven agents into your own organization, Dhimath provides a safe way to start with public or synthetic data, then scale to production.
To see Dhimath agents working on your RFQs or internal documents, reach out to us at info@greywiz.com or visit www.greywiz.com.
