The Product Spec Sheet Policy

When to use it

Select this policy for any data source containing product landing pages, technical data sheets, service descriptions, brochures, or feature comparisons.

How the Spec Sheet Policy Protects Your Data

1. The "No Financials" Firewall

The most important feature of this policy is what it removes. Marketing PDFs and scraped web pages often contain outdated MSRPs, old promo banners, or shipping estimates. This policy applies a strict redaction of all financial data—including prices, discounts, and costs.

Why? Relational Integrity: By stripping prices from spec sheets, we force your AI agent to retrieve financial data only from your master Price List. This guarantees the AI never quotes a 2023 brochure price to a 2025 customer.

2. Smart Identifier Mapping (The "SKU" Anchor)

Just like our Price List policy, this engine forces a strict hierarchy to identify products. It scans the text and anchors the document to a primary ID so it can be cross-referenced with your other databases (like pricing or inventory APIs).

• Priority 1: It searches for 'Item' or 'Item Number'.
• Priority 2: If absent, it searches for 'Model' or 'Item ID'.
• Fallback: It defaults to the most prominent H1 heading or Page Title.

3. Availability & URL Routing

The policy enforces two mandatory metadata fields for every product it processes, ensuring the LLM always has operational context:

• Availability Status: It analyzes the text to determine if a product is "Available" or "Not Available." (Note: It is smart enough to differentiate between a product being temporarily out of stock versus being permanently archived/discontinued).
• Product URL: It actively hunts for the direct link to the specific product page so the agent can provide direct routing to the customer.

4. Technical Data Consolidation (No Hallucinations)

The engine reads the entire document, consolidating paragraphs, bulleted lists, and messy tables into clean, structured Markdown text. It is strictly forbidden from paraphrasing, embellishing, or inferring details. If a specification isn't explicitly written in the source, it will not be generated. Inventory levels are also strictly redacted.

What the Output Looks Like

When your n8n workflow queries the Knowledge Base for product information, it receives a perfectly structured Markdown document optimized for the LLM's context window.

# Model: X500-Pro
**Availability:** Available
**Product Url:** `https://bugsys-company.com/products/x500-pro`

## Product Description
The X500-Pro is a heavy-duty thermal imaging camera designed for industrial inspections and HVAC diagnostics.

## Technical Specifications
- **Resolution:** 320 x 240 pixels
- **Thermal Sensitivity:** <0.04°C
- **Battery Life:** 8 hours continuous use
- **Drop Test Rating:** 2 meters

## Included Accessories
- Hard transport case
- 2x Lithium-ion batteries
- Charging dock

Why This Matters for Automation Developers

By applying the Product Spec Sheet Policy during ingestion, you build a much more resilient RAG architecture:

• Separation of Concerns: By keeping technical data (Spec Sheets) entirely separate from financial data (Price Lists), your agents become immune to version-conflict hallucinations.
• Ready for Tool Use: Because every spec sheet forces a "Product URL" field, you can easily instruct your agent to output that URL as a clickable link to the user in the chat interface.
• Clean Markdown: You don't have to write scripts to clean up HTML tags, messy PDF line breaks, or navigation bars. You get pure, factual text.