ROI & Results
AI Agent ROI for Manufacturing
A 60-person contract manufacturer was spending 50 hours/month on RFQ responses -- pulling costs, calculating lead times, and formatting proposals. They won 18% of quoted jobs. We deployed an AI quoting agent. RFQ response time dropped from 72 hours to 8 hours. Win rate jumped to 27%. That's $340K in additional annual revenue from the same pipeline.
The Story
What Changed and Why It Matters
In contract manufacturing, speed wins contracts. When a buyer sends an RFQ to 5 shops, the first 2-3 to respond with competitive quotes get the job. Shops that take a week to respond are already eliminated before the buyer evaluates pricing. But putting together a manufacturing quote isn't simple -- you need to calculate material costs, machine time, labor, tooling, finishing, packaging, and shipping. Each RFQ takes 2-4 hours of engineering and estimating time.
This manufacturer received 60-70 RFQs per month. Their 2-person estimating team could handle about 50 thoroughly. The remaining 20 either got rushed (and less accurate) quotes or were deprioritized. Average response time was 72 hours -- which in manufacturing means you're third or fourth in the buyer's evaluation.
The AI quoting agent reads the RFQ (including attached drawings and spec sheets), identifies the required processes (CNC machining, sheet metal, welding, finishing), pulls material costs from their supplier price lists, calculates machine time based on historical job data, and generates a draft quote. The estimator reviews and adjusts in 45 minutes instead of building from scratch in 3 hours. Response time dropped to 8 hours -- first or second in almost every competitive situation.
Transformation
Before vs After
Before AI Agents
60-70 RFQs/month. Response time: 72 hours average. 20+ RFQs under-served each month. Win rate: 18%. Estimating team: 2 people at 100% capacity. $1.9M annual revenue from quoted work.
After AI Agents
All RFQs quoted within 8-12 hours. Zero RFQs deprioritized. Win rate: 27% (+50%). Estimating team at 70% capacity (handling complex quotes and engineering changes). Projected annual revenue from quoted work: $2.24M.
The Numbers
ROI Metrics
72hrs → 8hrs
RFQ response time
18% → 27%
Win rate
20+/mo → 0
RFQs under-served
+$340K
Additional annual revenue
3hrs → 45min
Estimator time per quote
9 days
Payback period
The System
What We Built
Agent Configuration
Single agent: RFQ & Quoting Agent connected to the company's ERP (Epicor), supplier price lists, and historical job cost database. Reads RFQs and drawings, identifies required processes, calculates material + labor + machine time costs based on historical data, and generates draft quotes with markup. Estimator reviews and submits. LLM cost: ~$130/month.
Details
Timeline & Investment
Timeline
Week 1-2: ERP integration, historical job cost ingestion (3 years of data), supplier price list import. Week 3: Agent testing with 30 historical RFQs (compared to actual quotes). Week 4: Live deployment with estimator review. Month 2: Win rate improvement visible in monthly metrics. Month 3: Steady state at 27% win rate.
Investment
Department Build package: $2,500 one-time. Monthly LLM costs: ~$130. At $340K in additional annual revenue, the ROI is 130:1 in year one. Even a 5% win rate improvement would have justified the investment many times over.
FAQ
AI Agent ROI for Manufacturing — Common Questions
Can the AI read engineering drawings?
The agent can extract text, dimensions, and notes from PDF drawings. For complex geometry requiring visual interpretation, it flags the drawing for human review and focuses on the parts it can read -- material specs, tolerances, surface finish requirements, quantity. The estimator handles the geometry-dependent calculations.
How accurate are the AI-generated quotes?
In calibration testing against 30 historical quotes, the agent's estimates were within 8% of the actual quoted price on average. That's close enough for the estimator to adjust quickly. The agent is particularly accurate on repeat work and common processes where historical data is rich.
Does it work with our ERP system?
Epicor, SAP, and NetSuite are fully supported. For other ERPs with database or API access, we build custom integrations. The key data the agent needs: material costs, machine rates, labor rates, and historical job costs. If your ERP stores that, we can connect to it.
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