The AI quoting market is filling fast. GoAutonomous, Arzana, Broadn, Graip AI, and Kwantflow all approach RFQ intake differently. Here is how estimator-led, local-first quoting compares for complex manufacturing work.
Market landscape
New category: The RFQ intake software market for manufacturing barely existed two years ago. Today, at least five vendors compete for the same problem: inbound RFQs arrive unstructured, and someone needs to turn them into priced quotes. The way each vendor approaches the problem reveals their assumptions about who the customer is and what kind of quoting they do.
The how 18.8 billion in AI funding validates the RFQ intake market article explains why this category emerged. Venture capital flooded manufacturing AI in 2026, but the intake layer was overlooked, creating room for a new generation of tools.
Understanding the differences between these tools matters because the wrong choice means your estimators still spend half their week re-keying data. The right choice eliminates the bottleneck.
GoAutonomous
B2B commerce focus: GoAutonomous positions itself as an RFQ and quote automation platform for B2B commerce. It reads incoming RFQs in any format, identifies products, retrieves customer-specific pricing from the ERP, and generates quotes automatically. Their research on the RFQ backlog , describing it as "the most underreported sales metric in B2B manufacturing" , is some of the best independent analysis of the intake problem.
GoAutonomous is strongest for standard product catalogs where the RFQ asks for known SKUs with variant configurations. It is a commerce platform, not an estimating tool. The assumption is that the product is already defined in a catalog. For job shops and fabricators quoting custom fabrication, the RFQ arrives as a CAD file of a part that has never been made before. GoAutonomous cannot extract geometry to estimate a custom cycle time.
Arzana
Custom AI agents: Arzana, a Y Combinator company from the Spring 2026 batch, takes a different approach. They train custom AI models on a factory's historical data and deploy agents that automate quoting, order entry, invoicing, and other repetitive office work. Their quoting agent receives RFQ emails, matches parts against the customer's catalog, validates pricing, and sends professional quotes automatically.
Arzana's strength is that the AI is trained on the shop's own historical data, which means the quoting reflects the shop's actual pricing patterns. The limitation is the same: it works best for repeat parts and catalog items where the AI can learn from past quotes. For first-of-kind custom fabrication, the historical data does not exist yet. Arzana positions itself as an "autonomous ERP" , it aims to replace the ERP, not complement it.
Broadn
AI-native CPQ: Broadn describes itself as an AI agent for manufacturers that understands how they sell. It reads RFQs from email and contact forms, applies the manufacturer's pricing and product logic, drafts quotes, follows up, and produces ERP-ready orders. Broadn recently launched Agent Herbie for air-gapped environments , an AI agent that works offline.
Broadn is an AI-native CPQ. It operates on the assumption that the manufacturer has a defined product catalog with configurable options. The AI learns the configuration rules and pricing logic from past orders and applies them to new RFQs. For manufacturers with standard product lines who receive repeat inquiries, Broadn is a strong fit. For job shops quoting one-off custom fabrication from CAD files, the catalog assumption does not match the workflow.
Graip AI
Discrete manufacturing: Graip AI launched a Quote Management Agent in March 2026 that handles the full RFQ-to-quote workflow. It reads inbound RFQs across common formats, resolves variant configurations, checks ATP, and posts validated quotes into CPQ, CRM, or ERP. Graip targets discrete manufacturing , the same general industrial segment as GoAutonomous and Broadn.
Graip's agent handles the full sequence from reading an incoming request to creating a validated quote in the system. It ingests RFQs including PDFs, emails, and contact form submissions. The limitation is shared with the others: Graip assumes the product is defined in a catalog. It does not extract geometry from CAD files or calculate custom cycle times for fabricated parts.
Kwantflow approach
Estimator-led intake: Kwantflow is the only tool in this comparison that processes CAD geometry locally and keeps data on the estimator's desktop. It reads STEP, STP, DXF, DWG, PDF, and 3D PDF files directly from the email inbox. It extracts bounding box dimensions, volume, surface area, and feature complexity from the raw geometry. The estimator sees dimensional data before deciding whether to quote.
The local-first architecture means Kwantflow does not upload CAD files to a cloud server for processing. For defence shops, this is the CMMC compliance differentiator. For job shops quoting custom fabrication, this means the tool can estimate cycle time based on actual geometry rather than catalog matching. The estimator remains in control of pricing decisions. The tool handles the geometry extraction and data structuring.
The Paperless Parts vs Kwantflow CMMC comparison covers the detailed architectural differences. The ERP AI agents and the intake gap article explains why none of these tools replace the ERP AI intake layer , they complement it.
How they compare
Key differences: All five tools solve a real problem , inbound RFQs arriving unstructured. But they differ on the fundamental question: does the product already exist in a catalog? GoAutonomous, Broadn, and Graip AI assume yes. Arzana assumes the shop has historical data to train on. Kwantflow assumes the shop needs to estimate something new from raw geometry.
For a job shop or fabricator quoting custom parts from CAD files, the catalog-based tools will not help. The estimator still needs to open the CAD file, extract the geometry, and build a custom cost estimate. Kwantflow handles the geometry extraction and converts it into structured data that feeds the quoting workflow.
The RFQ automation guide for metal fabricators provides practical workflow advice for shops evaluating these options and deciding which approach fits their quoting profile.
Choosing the approach
Fit first: The best RFQ intake tool depends on what you quote. If your shop sells standard products from a catalog with configurable options, GoAutonomous, Broadn, or Graip AI can handle the majority of incoming RFQs automatically. If your shop quotes custom fabrication from customer-supplied CAD files, you need a tool that processes geometry first and catalog matching second.
The market is early enough that no single tool dominates. Each vendor is building for a specific assumption about the customer. The right question is not "which tool is best" , it is "which tool matches my quoting workflow."
<a href="/download">Try Kwantflow</a> and see why estimator-led RFQ intake beats general B2B quoting for complex manufacturing work.
Ways estimators can keep quote review clear:
- GoAutonomous focuses on B2B commerce RFQ automation from the buyer side, processing inbound requests against catalog pricing for standard products.
- Arzana trains custom AI models on a factory historical data and deploys agents for quoting, order entry, and invoicing across manufacturing offices.
- Broadn targets U.S. manufacturers with an AI-native CPQ that reads RFQs, applies pricing and product logic, and produces ERP-ready orders from email inquiries.
- Graip AI handles the full RFQ-to-quote workflow for discrete manufacturing, from reading an inbound request to posting a validated quote directly into CPQ or ERP.
- Kwantflow is the only estimator-led, local-first, manufacturing-specialist option , processing CAD geometry on-device and keeping data local for complex fabrication quoting.
