Spec sheets aren’t built for speed. They’re static, scattered, and slow to analyze—especially when you’re juggling prototypes, components, and deadlines. With Airtable, product ops teams can use AI to instantly extract line items, costs, and specs from PDFs—turning files and docs into actionable insights that accelerate decisions and reduce time-to-market.

Unify, analyze, and optimize material spend

 

When to use this play

Use this play to manage early-stage designs, analyze material costs, and collaborate across teams—all in one streamlined workflow.

  • Audit prototype materials: Extract specs and costs from new footwear prototypes automatically.

  • Identify cost-saving opportunities: Flag high-cost components to improve margins without compromising quality.

  • Collaborate on design in real time: Visualize materials in 3D to gather feedback and iterate instantly.

Pro tip

Upload multiple spec sheets at once and use AI to auto-tag materials by category (think: outsole, upper, and lining)—making it easier to compare costs across prototypes and spot high-impact savings opportunities.

Business impact

Turn static spec docs into strategic advantage—this play speeds up cost analysis, sharpens margin decisions, and aligns product and design teams.

  • Make faster decisions: Cut data extraction and analysis from days to minutes.

  • Improve margins: Surface line-item cost insights to optimize spend.

  • Unify workflows: Replace siloed docs with a single source of truth for cross-team collaboration.

6 steps to get you started

Follow these six steps to turn static spec documents into a dynamic, AI-powered workspace—and go from siloed data to real insights in minutes.

Ask targeted questions and get instant, cost-focused insights.

Sample Field Agent prompts

Start with these prompts for your AI fields in Airtable. Copy and paste into the open field, and customize them for your unique workflows.

Determine if the price paid for a material is typical compared to the market. Use market analysis to assess.

Output: 

State if we are on par, underpaying, or overpaying, e.g., "Overpaying: [2 sentences about why, citing key facts]"

Context and data: 

- Material Name: _<Insert field>_ 

- Material Price: _<Insert field>_ 

Note: You run this extraction AI field agent for any type of product metadata, including style number, category, factory, or materials used. 

Extract the style name from a document accurately.

Output: 

Return the style name exactly as it appears, e.g., "ZENFLOW STRIDE."

Context and data: 

- Document: _<Insert attachment field>_

About Omni and Field Agents 

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Unify, analyze, and optimize material spend

These plays feature custom interfaces, currently in beta and launching soon. Book a demo with our sales team to request early access.