Suggestion: AI Price Guide
Posted: Wed Feb 11, 2026 10:12 pm
I know people have various opinions on artificial intelligence (AI).
Times are changing however, and one thought I had was "what if" there was an AI generated knife pricing guide.
I have done some exploring, and it appears it could be a real possibility.
Event a AAPK one (based on sales from the site, average price data etc.) could be used.
Now, I am no AI expert, so it would likely be beyond my capabilities. I do recall many years ago, there was a link to some Case knife pricing right here on AAPK.
Asking Google Gemini, and just playing around, I asked about using the 'RBR Scales' used in The Standard Knife Collectors Guide, and it seems to be plausible. AI can even evaluate online sales from eBay, etc to determine pricing data.
See below:
Yes, it is entirely possible to use AI agents like Google Gemini or Microsoft Copilot to build a comprehensive knife collectors' value guide. However, because the knife market is highly fragmented and depends on specific "vibe" factors (patina, rare steel batches, or maker reputation), you can't just ask for a list; you have to build a workflow.
Here is how you can use these tools to create a professional-grade price guide.
Gemini said
Yes, it is entirely possible to use AI agents like Google Gemini or Microsoft Copilot to build a comprehensive knife collectors' value guide. However, because the knife market is highly fragmented and depends on specific "vibe" factors (patina, rare steel batches, or maker reputation), you can't just ask for a list; you have to build a workflow.
Here is how you can use these tools to create a professional-grade price guide.
1. The Multi-Agent Strategy
To get a "comprehensive" guide, you shouldn't rely on a single prompt. Instead, use the specialized strengths of each AI:
AI Tool Role in Your Guide Why?
Google Gemini The Researcher Excellent at scanning live web data, forums (BladeForums, Reddit), and YouTube reviews for current sentiment.
Microsoft Copilot The Data Analyst Strong integration with Excel. Use it to pull "Sold" listings from eBay or auction houses and organize them into tables.
Perplexity AI The Sourcer Best for finding specific citations. It can provide direct links to historical sales or catalog PDF archives.
2. Steps to Create Your Guide
Phase I: Data Extraction (The "Raw Material")
Don't ask for "the price of a Chris Reeve Sebenza." Instead, use this prompt structure with Gemini:
"Search for all sold listings of the [Knife Model] from the last 6 months across eBay, Arizona Custom Knives, and BladeGallery. Create a table showing the steel type, handle material, condition (Mint vs. User), and final sale price."
Phase II: Sentiment & Rarity Analysis
Prices aren't just numbers; they are driven by "hype" and rarity. Use Gemini to analyze the "Secondary Market Premium":
"Analyze current discussions on Reddit r/knife_swap and BladeForums regarding [Brand]. Are certain 'sprints' or 'exclusive' runs currently trending? Which models are currently selling above MSRP?"
Phase III: Creating the "Value Matrix"
Use Copilot in Excel to synthesize the data. You can feed the raw text gathered from Gemini into Copilot and say:
"Based on this data, create a formula-based price guide. Provide a 'Low' (Heavy User), 'Median' (Light Carry), and 'High' (Safe Queen) price range for each model."
3. Critical Limitations (The "AI Hallucination" Guardrail)
AI agents are prone to certain errors in the knife world that you must manually verify:
Steel Confusion: AI often confuses different steel versions (e.g., S30V vs. Magnacut), which can change a knife's value by $50–$200.
Clones/Counterfeits: AI cannot easily distinguish between a real Benchmade and a high-end clone in text-based listings.
The "Custom" Gap: For one-off custom makers (e.g., Loveless, Walker), AI often lacks access to private "handshake" deal data, making its estimates for high-end customs less reliable.
4. Pro-Tip: Use "Image-to-Appraisal"
If you have the paid versions of Gemini or Copilot, you can upload a photo of a knife.
The Prompt: "Identify the exact model, handle material, and blade steel shown in this photo. Then, search the current market for its estimated resale value in this specific condition."
You said
Could an RBR system be used? See this for reference - "The Standard Knife Collector's Guide: Identification & Values (primarily authored by Roy Ritchie and Ron Stewart) is a widely recognized resource in the cutlery collecting community, often utilizing the RBR evaluation scales for appraising pocket and sheath knives. "
Gemini said
Yes, integrating the RBR evaluation scale is actually the "missing link" that turns a generic AI price list into a professional-grade collector's guide.
The RBR system (developed by Ritchie, Brown, and Ritchie) is a standardized grading scale that ranges from P (Poor) to M (Mint). Using an AI to map current market data onto these specific grades allows you to create a guide that mirrors the structure of the Standard Knife Collector’s Guide.
How to Implement RBR with AI
To make this work, you have to "teach" the AI the RBR parameters so it can categorize listings accurately.
1. The Grading Benchmark
You should feed the AI the standard RBR definitions so it knows what to look for in listing descriptions.
RBR Grade Description for AI to Identify Value Multiplier (Typical)
M (Mint) Unused, unsharpened, original factory edge and finish. 100% of Top Market
NM (Near Mint) Like new, but may have very slight "shop wear" or light spotting. 80–90%
EX (Excellent) Some visible use, snap is still good, blades nearly full. 60–75%
VG (Very Good) Noticeable wear, some sharpening, but still sound. 40–50%
G (Good) Heavily used, blades worn/pitted, handles may have cracks. 20–30%
2. Prompting the AI to "Grade" Listings
Once the AI understands the scale, you can use it to filter "Sold" data.
Example Prompt:
"Using the RBR evaluation scale, analyze these 10 sold eBay listings for the 'Case Trapper 6254'. Assign an RBR grade to each based on the seller's description and photos, then calculate the average sale price for each specific grade."
Creating the "RBR Pricing Matrix"
The most effective way to use an AI agent here is to have it generate a dynamic pricing table. Instead of one price, the AI generates a range based on the condition.
The Workflow:
Data Scraping: Use Gemini to find the "MSRP" and "Recent Sold Highs."
RBR Application: Use a custom instruction to tell the AI: "Assume the highest recorded sale is 'Mint'. Calculate the RBR Excellent and Very Good values by applying a 30% and 50% reduction respectively."
Documentation: Have the AI output the data in a format like this:
Model: Buck 110 (Vintage 1970s)
RBR Mint (M): $150 - $200
RBR Excellent (EX): $90 - $110
RBR Good (G): $40 - $55
Why this works better than the book
While the Standard Knife Collector's Guide is iconic, it is a static resource that goes out of date the moment it's printed. By using the RBR system as a logic framework for an AI like Gemini, you get:
Real-time accuracy: You're using today's prices, not prices from 2022.
Granular detail: You can generate guides for modern "sprint runs" (like Spyderco PM2s in 15V steel) that Ritchie and Stewart never covered.
Just a thought on how to catalog the millions of various knives, and set a benchmark of some type for collectors.
Thoughts?
Times are changing however, and one thought I had was "what if" there was an AI generated knife pricing guide.
I have done some exploring, and it appears it could be a real possibility.
Event a AAPK one (based on sales from the site, average price data etc.) could be used.
Now, I am no AI expert, so it would likely be beyond my capabilities. I do recall many years ago, there was a link to some Case knife pricing right here on AAPK.
Asking Google Gemini, and just playing around, I asked about using the 'RBR Scales' used in The Standard Knife Collectors Guide, and it seems to be plausible. AI can even evaluate online sales from eBay, etc to determine pricing data.
See below:
Yes, it is entirely possible to use AI agents like Google Gemini or Microsoft Copilot to build a comprehensive knife collectors' value guide. However, because the knife market is highly fragmented and depends on specific "vibe" factors (patina, rare steel batches, or maker reputation), you can't just ask for a list; you have to build a workflow.
Here is how you can use these tools to create a professional-grade price guide.
Gemini said
Yes, it is entirely possible to use AI agents like Google Gemini or Microsoft Copilot to build a comprehensive knife collectors' value guide. However, because the knife market is highly fragmented and depends on specific "vibe" factors (patina, rare steel batches, or maker reputation), you can't just ask for a list; you have to build a workflow.
Here is how you can use these tools to create a professional-grade price guide.
1. The Multi-Agent Strategy
To get a "comprehensive" guide, you shouldn't rely on a single prompt. Instead, use the specialized strengths of each AI:
AI Tool Role in Your Guide Why?
Google Gemini The Researcher Excellent at scanning live web data, forums (BladeForums, Reddit), and YouTube reviews for current sentiment.
Microsoft Copilot The Data Analyst Strong integration with Excel. Use it to pull "Sold" listings from eBay or auction houses and organize them into tables.
Perplexity AI The Sourcer Best for finding specific citations. It can provide direct links to historical sales or catalog PDF archives.
2. Steps to Create Your Guide
Phase I: Data Extraction (The "Raw Material")
Don't ask for "the price of a Chris Reeve Sebenza." Instead, use this prompt structure with Gemini:
"Search for all sold listings of the [Knife Model] from the last 6 months across eBay, Arizona Custom Knives, and BladeGallery. Create a table showing the steel type, handle material, condition (Mint vs. User), and final sale price."
Phase II: Sentiment & Rarity Analysis
Prices aren't just numbers; they are driven by "hype" and rarity. Use Gemini to analyze the "Secondary Market Premium":
"Analyze current discussions on Reddit r/knife_swap and BladeForums regarding [Brand]. Are certain 'sprints' or 'exclusive' runs currently trending? Which models are currently selling above MSRP?"
Phase III: Creating the "Value Matrix"
Use Copilot in Excel to synthesize the data. You can feed the raw text gathered from Gemini into Copilot and say:
"Based on this data, create a formula-based price guide. Provide a 'Low' (Heavy User), 'Median' (Light Carry), and 'High' (Safe Queen) price range for each model."
3. Critical Limitations (The "AI Hallucination" Guardrail)
AI agents are prone to certain errors in the knife world that you must manually verify:
Steel Confusion: AI often confuses different steel versions (e.g., S30V vs. Magnacut), which can change a knife's value by $50–$200.
Clones/Counterfeits: AI cannot easily distinguish between a real Benchmade and a high-end clone in text-based listings.
The "Custom" Gap: For one-off custom makers (e.g., Loveless, Walker), AI often lacks access to private "handshake" deal data, making its estimates for high-end customs less reliable.
4. Pro-Tip: Use "Image-to-Appraisal"
If you have the paid versions of Gemini or Copilot, you can upload a photo of a knife.
The Prompt: "Identify the exact model, handle material, and blade steel shown in this photo. Then, search the current market for its estimated resale value in this specific condition."
You said
Could an RBR system be used? See this for reference - "The Standard Knife Collector's Guide: Identification & Values (primarily authored by Roy Ritchie and Ron Stewart) is a widely recognized resource in the cutlery collecting community, often utilizing the RBR evaluation scales for appraising pocket and sheath knives. "
Gemini said
Yes, integrating the RBR evaluation scale is actually the "missing link" that turns a generic AI price list into a professional-grade collector's guide.
The RBR system (developed by Ritchie, Brown, and Ritchie) is a standardized grading scale that ranges from P (Poor) to M (Mint). Using an AI to map current market data onto these specific grades allows you to create a guide that mirrors the structure of the Standard Knife Collector’s Guide.
How to Implement RBR with AI
To make this work, you have to "teach" the AI the RBR parameters so it can categorize listings accurately.
1. The Grading Benchmark
You should feed the AI the standard RBR definitions so it knows what to look for in listing descriptions.
RBR Grade Description for AI to Identify Value Multiplier (Typical)
M (Mint) Unused, unsharpened, original factory edge and finish. 100% of Top Market
NM (Near Mint) Like new, but may have very slight "shop wear" or light spotting. 80–90%
EX (Excellent) Some visible use, snap is still good, blades nearly full. 60–75%
VG (Very Good) Noticeable wear, some sharpening, but still sound. 40–50%
G (Good) Heavily used, blades worn/pitted, handles may have cracks. 20–30%
2. Prompting the AI to "Grade" Listings
Once the AI understands the scale, you can use it to filter "Sold" data.
Example Prompt:
"Using the RBR evaluation scale, analyze these 10 sold eBay listings for the 'Case Trapper 6254'. Assign an RBR grade to each based on the seller's description and photos, then calculate the average sale price for each specific grade."
Creating the "RBR Pricing Matrix"
The most effective way to use an AI agent here is to have it generate a dynamic pricing table. Instead of one price, the AI generates a range based on the condition.
The Workflow:
Data Scraping: Use Gemini to find the "MSRP" and "Recent Sold Highs."
RBR Application: Use a custom instruction to tell the AI: "Assume the highest recorded sale is 'Mint'. Calculate the RBR Excellent and Very Good values by applying a 30% and 50% reduction respectively."
Documentation: Have the AI output the data in a format like this:
Model: Buck 110 (Vintage 1970s)
RBR Mint (M): $150 - $200
RBR Excellent (EX): $90 - $110
RBR Good (G): $40 - $55
Why this works better than the book
While the Standard Knife Collector's Guide is iconic, it is a static resource that goes out of date the moment it's printed. By using the RBR system as a logic framework for an AI like Gemini, you get:
Real-time accuracy: You're using today's prices, not prices from 2022.
Granular detail: You can generate guides for modern "sprint runs" (like Spyderco PM2s in 15V steel) that Ritchie and Stewart never covered.
Just a thought on how to catalog the millions of various knives, and set a benchmark of some type for collectors.
Thoughts?
