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Explore solutionSolution Example · Manufacturing
Turn incoming RFQs into review-ready estimating packets.
Manufacturing RFQ & job packet agent
Estimators spend hours reading drawings, PDFs, and emails before they can start a quote. An agent collects incoming RFQs, extracts materials, quantities, tolerances, deadlines, and specifications, checks approved reference information, and prepares structured estimating packets for human review.
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The business challenge
Manufacturers receive RFQs containing emails, technical drawings, PDFs, specifications, and quantity requirements. Estimators manually review documents and prepare quote packets, creating delays and increasing the risk of missing important requirements.
- Reading every attachment to find materials, tolerances, finishes, and quantities
- Re-keying requirements into spreadsheets or the ERP
- Chasing customers for missing information late in the process
- Quotes waiting in a queue while estimators handle preparation instead of pricing
How the AI agent works
An agent collects incoming RFQs, extracts materials, quantities, tolerances, deadlines, and specifications, checks approved reference information, and prepares structured estimating packets for human review.
- 01
RFQ arrives
Email, drawings, and spec PDFs land in a monitored inbox.
- 02
Requirements extracted
Materials, quantities, tolerances, and due dates are pulled into a structured record.
- 03
Checked against references
Approved materials, past jobs, and capability rules are checked; gaps are listed.
- 04
Estimator reviews
The estimator confirms the packet, resolves flagged gaps, and sets pricing.
Human approval
- 05
Packet and draft quote ready
An approved packet and draft quote are saved to the ERP or CRM.
What AgentSpring delivers
- Email and RFQ document intake
- AI-powered specification extraction
- Missing-requirements identification
- Estimator review workspace
- Draft quote preparation
- ERP/CRM integration where supported
Potential business impact
Reduce manual RFQ preparation work and accelerate customer quote turnaround.
50–70% reduction in preparation time
Illustrative pilot target. Actual results depend on your workflow and will be established during discovery.
How we would measure it
During discovery we'd record your current baseline for each measure, then track it through the pilot.
- Time from RFQ received to estimator-ready packet
- Quote turnaround time
- Requirements missed and caught after quoting
Systems & integrations
Typical categories of systems this kind of agent connects to. Which specific products can be connected is confirmed during discovery, based on what you use and what access it allows.
- ERP
- CRM
- Document repositories
- CAD/drawing file storage
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Could this work for your business?
Tell us about your current workflow. We'll help identify where an AI agent could reduce manual work and what it would take to build a solution.