{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_pricegpt","slug":"pricegpt","name":"PriceGPT","type":"product","url":"https://www.price-gpt.com","page_url":"https://unfragile.ai/pricegpt","categories":["data-analysis"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_pricegpt__cap_0","uri":"capability://search.retrieval.real.time.competitive.price.monitoring.across.multiple.channels","name":"real-time competitive price monitoring across multiple channels","description":"Continuously scrapes and aggregates pricing data from competitor websites, marketplaces, and public APIs (Amazon, eBay, etc.) using web crawlers and API integrations, normalizing product matches through SKU/GTIN mapping and fuzzy product name matching. The system maintains a time-series database of competitor prices indexed by product and channel, enabling detection of price changes within hours rather than manual daily checks.","intents":["I need to know when competitors change prices on my products without manually checking 10+ websites daily","I want to track pricing trends across Amazon, eBay, and direct competitor sites in one dashboard","I need to identify which channels have the highest/lowest prices for my SKUs to inform channel strategy"],"best_for":["E-commerce sellers with 50-500 SKUs across multiple sales channels","Marketplace sellers (Amazon, eBay) competing on price-sensitive categories","Small retailers without dedicated pricing analyst resources"],"limitations":["Accuracy depends on product matching quality — requires clean, standardized product data (GTIN/SKU) to avoid false matches","Web scraping may be rate-limited or blocked by some retailers; API-based integrations limited to platforms offering official pricing APIs","Latency of 2-24 hours typical for web-scraped data vs real-time for API sources; some channels may not be covered","Cannot track prices behind authentication walls or dynamic pricing that varies by geography/user"],"requires":["Product catalog with standardized identifiers (GTIN, SKU, or UPC)","API keys for supported marketplace integrations (Amazon SP-API, eBay API, etc.)","Inventory sync mechanism (CSV upload, API, or webhook) to keep product data current","Internet connectivity for outbound web scraping and API calls"],"input_types":["product catalog (CSV, JSON, or API feed with SKU, GTIN, product name, category)","competitor URLs or marketplace seller IDs","sales channel credentials (optional, for direct API integrations)"],"output_types":["structured price data (JSON/CSV with timestamp, competitor, price, URL)","price change alerts (webhook, email, or dashboard notification)","competitive pricing dashboard (web UI with charts and tables)"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_pricegpt__cap_1","uri":"capability://data.processing.analysis.demand.elasticity.and.price.sensitivity.analysis","name":"demand elasticity and price sensitivity analysis","description":"Analyzes historical sales volume and price data to estimate price elasticity (how demand changes with price) using regression models or machine learning (e.g., linear regression, gradient boosting). The model learns category-specific elasticity curves and identifies price thresholds where demand drops sharply, enabling recommendations that maximize revenue rather than just matching competitor prices.","intents":["I want to know the optimal price for each product that maximizes profit, not just match competitors","I need to understand if my products are price-sensitive or if I can raise prices without losing sales","I want to avoid race-to-the-bottom pricing by understanding true demand at different price points"],"best_for":["E-commerce sellers with 6+ months of historical sales and pricing data","Categories with moderate to high price sensitivity (apparel, electronics, home goods)","Sellers willing to experiment with price changes to build elasticity models"],"limitations":["Requires clean historical data (sales volume, price, date) — garbage in, garbage out; missing or inaccurate data degrades model accuracy","Model accuracy improves with 12+ months of data; shorter histories lead to overfitting or unreliable elasticity estimates","Cannot account for external shocks (seasonality, supply chain disruptions, viral trends) without explicit feature engineering","Elasticity varies by customer segment, geography, and channel — single global elasticity estimate may be oversimplified","Free tier likely excludes advanced elasticity modeling; may only provide basic competitor-matching recommendations"],"requires":["Historical sales data (minimum 6 months, ideally 12+) with columns: date, SKU, quantity_sold, price, channel","Consistent pricing and inventory data across the period","Product categorization (category, subcategory) to enable category-level elasticity learning"],"input_types":["sales transaction data (CSV or API feed with date, SKU, quantity, price)","product metadata (SKU, category, cost, current price)"],"output_types":["elasticity coefficient per product or category (numeric: -0.5 to -3.0 typical for e-commerce)","price optimization recommendations (suggested price, expected revenue/margin impact)","demand curve visualization (price vs. quantity chart)"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_pricegpt__cap_10","uri":"capability://search.retrieval.competitor.tracking.and.new.entrant.detection","name":"competitor tracking and new entrant detection","description":"Continuously monitors the competitive landscape, detecting new competitors entering the market for specific products or categories and alerting users to shifts in competitive intensity. Tracks competitor entry/exit, identifies emerging competitors with aggressive pricing, and segments competitors by strategy (price leader, premium, niche). Enables proactive strategy adjustments before competitive pressure becomes severe.","intents":["I want to know immediately when a new competitor starts selling my products","I need to understand which competitors are most aggressive on price and which are premium-positioned","I want to identify emerging competitors before they become dominant"],"best_for":["Sellers in competitive categories (electronics, apparel, home goods) where new competitors emerge frequently","Sellers wanting early warning of competitive threats","Sellers wanting to segment competitors by strategy (price vs. premium vs. niche)"],"limitations":["Detection accuracy depends on web scraping coverage — competitors not tracked in initial setup may go undetected","Free tier likely limited to basic competitor tracking without advanced segmentation or strategy analysis","New competitor detection has inherent latency (1-7 days typical) — cannot detect competitors faster than scraping frequency","Competitor segmentation (price leader vs. premium) requires sufficient pricing history and may be subjective","Cannot predict competitor actions or market share impact — only detects presence and pricing"],"requires":["Baseline list of known competitors (URLs or marketplace seller IDs)","Web scraping infrastructure to detect new sellers/competitors","Product matching capability to identify which products new competitors are selling"],"input_types":["product catalog (SKU, product name, category)","known competitor list (competitor_id, URL, marketplace_seller_id)"],"output_types":["new competitor alerts (competitor_name, entry_date, products_offered, initial_pricing)","competitor segmentation (competitor_id, strategy_type: price_leader/premium/niche, price_percentile)","competitive intensity trends (category, num_competitors_over_time, price_variance_trend)"],"categories":["search-retrieval","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_pricegpt__cap_2","uri":"capability://planning.reasoning.ai.driven.pricing.recommendation.engine.with.margin.constraints","name":"ai-driven pricing recommendation engine with margin constraints","description":"Synthesizes competitive pricing data, demand elasticity models, inventory levels, and cost data to generate price recommendations that maximize revenue or profit subject to business constraints (minimum margin %, max/min price bounds, channel-specific rules). Uses reinforcement learning or constraint optimization (linear programming) to balance competing objectives: staying competitive, maintaining margins, and clearing slow-moving inventory.","intents":["I want AI to recommend prices that balance staying competitive with maintaining my target margin","I need different pricing strategies for fast-moving vs. slow-moving inventory","I want to set pricing rules (e.g., 'never go below 30% margin') and have AI respect them while optimizing"],"best_for":["E-commerce sellers with clear margin targets and pricing constraints","Multi-channel sellers needing channel-specific pricing rules (e.g., higher prices on own website vs. marketplace)","Sellers with mixed inventory (fast-moving commodities + slow-moving specialty items)"],"limitations":["Recommendations are only as good as input data quality — inaccurate cost data, elasticity estimates, or competitor prices lead to poor recommendations","Free tier likely provides basic recommendations without advanced features like inventory-aware pricing or multi-objective optimization","Model cannot predict sudden market shifts (new competitor entry, viral demand) — relies on historical patterns","Requires manual review and approval of price changes; no indication of how much automation the free tier allows","No transparency on how the AI weights different objectives (revenue vs. margin vs. competitiveness) — users cannot customize weighting"],"requires":["Product cost data (COGS or landed cost per SKU)","Current inventory levels (quantity on hand per SKU)","Pricing constraints (minimum margin %, price floor/ceiling, channel-specific rules)","Competitive pricing data (from monitoring capability)","Elasticity estimates (from demand analysis capability)"],"input_types":["structured product data (SKU, cost, current price, inventory, category)","pricing constraints (JSON or form: min_margin, max_price, min_price, channel_rules)","competitive pricing snapshot (competitor prices for matching SKUs)"],"output_types":["price recommendations (JSON/CSV: SKU, current_price, recommended_price, expected_margin, expected_revenue_impact)","recommendation explanation (text: why this price was recommended, which constraints were binding)","batch recommendation report (dashboard or downloadable file with all SKU recommendations)"],"categories":["planning-reasoning","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_pricegpt__cap_3","uri":"capability://tool.use.integration.real.time.price.change.automation.and.syncing.to.sales.channels","name":"real-time price change automation and syncing to sales channels","description":"Automatically applies recommended prices to products across connected sales channels (e.g., Shopify, WooCommerce, Amazon, eBay) via APIs or integrations, with optional approval workflows for high-impact changes. Maintains price consistency across channels while respecting channel-specific rules (e.g., higher prices on own website, lower on marketplace). Includes rollback and audit logging to track all price changes.","intents":["I want to automatically apply price recommendations to my store without manually updating each channel","I need to update prices across Shopify, Amazon, and eBay simultaneously when competitors change prices","I want to require approval for large price changes but auto-apply small adjustments"],"best_for":["Multi-channel sellers (own website + marketplaces) who need price consistency","Sellers with high SKU counts (100+) where manual price updates are impractical","Sellers comfortable with automation but wanting safety guardrails (approval workflows)"],"limitations":["Requires API integrations with each sales channel; not all platforms support real-time price updates (some have batch-only APIs with 1-4 hour delays)","API rate limits may prevent updating all SKUs simultaneously during peak times; requires queuing/throttling logic","Free tier likely limited to basic automation without approval workflows or advanced scheduling","Price changes may not propagate instantly to search results/listings (Amazon, eBay indexing delays of 15 min - 2 hours typical)","No built-in handling of channel-specific pricing rules beyond simple overrides — complex rules (e.g., 'price 5% higher on website if competitor price > $100') require custom configuration"],"requires":["API credentials for connected sales channels (Shopify API token, Amazon SP-API credentials, WooCommerce REST API, etc.)","Product mapping between PriceGPT's product database and channel-specific product IDs (SKU, ASIN, listing ID)","Approval workflow configuration (optional: define price change thresholds requiring human approval)","Channel-specific pricing rules (optional: define min/max prices, margin floors per channel)"],"input_types":["price recommendations (from recommendation engine)","channel API credentials and product mappings","pricing rules and approval thresholds (JSON or form)"],"output_types":["price update confirmations (webhook or API response: SKU, channel, old_price, new_price, timestamp, status)","audit log (CSV/JSON: all price changes with timestamp, user/system, old/new price, reason)","approval queue (dashboard showing pending price changes awaiting review)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_pricegpt__cap_4","uri":"capability://planning.reasoning.inventory.aware.dynamic.pricing.with.clearance.optimization","name":"inventory-aware dynamic pricing with clearance optimization","description":"Adjusts price recommendations based on inventory age, turnover rate, and stockout risk, automatically suggesting deeper discounts for slow-moving or aging inventory to avoid deadstock. Uses inventory velocity metrics (days-to-sell, turnover ratio) and demand forecasts to identify products at risk of obsolescence, then recommends aggressive pricing to clear inventory before expiration or seasonal shifts.","intents":["I have slow-moving inventory that's tying up cash — I want AI to recommend aggressive discounts to clear it","I want to avoid markdowns by pricing products optimally based on how fast they're selling","I need different pricing strategies for fast-moving vs. slow-moving SKUs in the same category"],"best_for":["Retailers with seasonal inventory (fashion, home goods, toys) where clearance is critical","Sellers with high inventory carrying costs who benefit from faster turnover","Multi-category sellers with mixed inventory velocity (some fast-moving, some slow)"],"limitations":["Requires accurate inventory age and turnover data — many sellers lack this visibility, especially if using multiple warehouses","Aggressive clearance pricing may damage brand perception or train customers to wait for discounts","Free tier likely excludes inventory-aware optimization; may only provide basic competitor-matching","Cannot predict demand spikes or seasonal shifts without explicit seasonality data or external signals","Clearance recommendations may conflict with margin constraints — no clear guidance on how to balance margin floors with inventory clearing"],"requires":["Inventory data with age tracking (SKU, quantity on hand, date received or last restocked, warehouse location)","Historical sales velocity (SKU, units sold per day/week, trend direction)","Inventory carrying cost or target inventory turnover rate","Product seasonality or lifecycle stage (new, mature, end-of-life)"],"input_types":["inventory snapshot (SKU, quantity, age, location, cost)","sales velocity data (SKU, units_sold_per_day, trend)","inventory targets (target_turnover_days, max_holding_cost)"],"output_types":["clearance pricing recommendations (SKU, current_price, clearance_price, expected_sell_through_time, margin_impact)","inventory health dashboard (SKU, age, velocity, risk_level, recommended_action)","clearance campaign suggestions (bundle slow-moving items, set flash sale prices)"],"categories":["planning-reasoning","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_pricegpt__cap_5","uri":"capability://search.retrieval.competitive.pricing.intelligence.dashboard.with.trend.analysis","name":"competitive pricing intelligence dashboard with trend analysis","description":"Visualizes competitive pricing data, price changes, and market trends over time in an interactive dashboard, enabling quick identification of pricing patterns, competitor strategies, and market shifts. Includes trend charts (price over time), heatmaps (price by competitor/channel), and alerts for significant price movements or new competitor entries. Supports filtering by product, category, competitor, and date range.","intents":["I want to see at a glance how my prices compare to competitors across all my products","I need to understand if competitors are in a price war or if prices are stable","I want to identify which competitors are most aggressive on price and which categories are most competitive"],"best_for":["E-commerce sellers who want visibility into competitive pricing without deep data analysis skills","Pricing managers or merchandisers who need to communicate pricing strategy to leadership","Sellers with 50+ SKUs where manual competitive analysis is impractical"],"limitations":["Dashboard is only as useful as underlying data quality — inaccurate competitor matching or missing price data leads to misleading visualizations","Free tier likely limited to basic charts and tables without advanced analytics (e.g., price elasticity curves, competitor clustering)","Trend analysis requires historical data; new products or competitors have no baseline for comparison","No built-in export or reporting features mentioned; may require manual screenshot/download for sharing with stakeholders","Real-time updates depend on scraping frequency — dashboard may lag actual market prices by hours"],"requires":["Competitive pricing data (from monitoring capability)","Product catalog with categories and SKUs","Web browser with JavaScript support (for interactive dashboard)"],"input_types":["competitive pricing time-series data (timestamp, competitor, SKU, price, channel)","product metadata (SKU, category, own price)"],"output_types":["interactive dashboard (web UI with charts, tables, filters)","price trend charts (line charts showing price over time by competitor)","competitive heatmaps (matrix: products vs. competitors, color-coded by price difference)","price change alerts (notifications for significant movements)"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_pricegpt__cap_6","uri":"capability://data.processing.analysis.product.matching.and.deduplication.across.channels","name":"product matching and deduplication across channels","description":"Automatically matches products across different sales channels and competitor sites using fuzzy string matching, GTIN/SKU lookup, and machine learning-based product embeddings. Handles variations in product names, descriptions, and identifiers (e.g., 'iPhone 15 Pro Max 256GB' vs. 'Apple iPhone 15 Pro Max 256GB Space Black') to ensure price comparisons are accurate. Deduplicates products in the internal database to avoid tracking the same product multiple times.","intents":["I want to track prices for my products across competitors, but product names and SKUs don't match exactly","I need to consolidate my product catalog across multiple sales channels into a single view","I want to avoid false price comparisons where slightly different product variants are treated as the same product"],"best_for":["Sellers with products across multiple channels (own website, Amazon, eBay, Shopify) with inconsistent naming","Sellers in categories with many variants (apparel, electronics) where exact matching is insufficient","Sellers without clean, standardized product data (GTIN/SKU)"],"limitations":["Fuzzy matching is probabilistic — some false positives (matching different products) or false negatives (missing matches) are inevitable, especially for generic product names","Requires training data or manual validation to tune matching thresholds; out-of-the-box accuracy may be poor","Cannot match products without any common identifier (GTIN, SKU, URL) — relies on text similarity alone","Free tier likely excludes manual matching review or correction interface; users may be stuck with incorrect matches","Matching quality degrades for non-English product names or regional variants"],"requires":["Product catalog with at least one identifier per product (GTIN, SKU, UPC, or product name)","Competitor product data (from web scraping or API)","Optional: manual validation data to train or tune matching algorithm"],"input_types":["product catalog (CSV/JSON: SKU, GTIN, product_name, description, category)","competitor product data (competitor_id, product_name, price, URL, GTIN if available)"],"output_types":["product match pairs (JSON: own_sku, competitor_product_id, match_confidence_score)","deduplication report (CSV: canonical_product_id, matched_variants, recommended_consolidation)","match quality metrics (precision, recall, false positive rate)"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_pricegpt__cap_7","uri":"capability://data.processing.analysis.market.segmentation.and.category.level.pricing.analysis","name":"market segmentation and category-level pricing analysis","description":"Groups products into market segments or categories and analyzes pricing patterns, elasticity, and competitive intensity at the segment level rather than individual product level. Identifies which categories are price-sensitive (high competition, low margins) vs. margin-friendly (low competition, high elasticity). Enables category-specific pricing strategies and helps identify underpriced or overpriced categories.","intents":["I want to understand which product categories are competitive and which have pricing power","I need to set different pricing strategies for different categories (e.g., aggressive on commodities, premium on specialty items)","I want to identify categories where I'm underpriced or overpriced relative to competitors"],"best_for":["Multi-category sellers (e.g., general e-commerce stores) with diverse product types","Sellers wanting to optimize pricing strategy by category rather than individual SKU","Sellers with limited historical data per SKU but sufficient data at category level"],"limitations":["Category definitions must be consistent and meaningful — poor categorization leads to misleading analysis","Segment-level analysis may mask important SKU-level variations (e.g., one outlier SKU skewing category elasticity)","Free tier likely excludes advanced segmentation or custom category definitions","Requires sufficient data per category to estimate reliable elasticity — small categories with few SKUs may have unreliable estimates"],"requires":["Product catalog with consistent category/segment assignments","Sales and pricing data aggregated at category level (or SKU-level data that can be aggregated)","Competitive pricing data by category"],"input_types":["product catalog with category assignments (SKU, category, subcategory)","sales data by category (category, date, units_sold, revenue)","competitive pricing by category (category, competitor, avg_price, price_range)"],"output_types":["category-level elasticity estimates (category, elasticity_coefficient, confidence_interval)","competitive intensity analysis (category, num_competitors, price_variance, margin_distribution)","category-specific pricing recommendations (category, recommended_price_range, strategy)"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_pricegpt__cap_8","uri":"capability://planning.reasoning.price.change.impact.simulation.and.a.b.testing.framework","name":"price change impact simulation and a/b testing framework","description":"Simulates the expected impact of price changes on revenue, margin, and demand before applying them, using elasticity models and historical data. Supports A/B testing by applying different prices to subsets of inventory or channels and measuring actual impact on sales, enabling validation of elasticity estimates and continuous model improvement. Includes statistical significance testing to determine if observed differences are real or due to chance.","intents":["I want to test a price increase on a subset of my inventory to see if it actually impacts demand before rolling out","I want to simulate the revenue impact of a 10% price increase before committing to it","I want to validate my elasticity model by running A/B tests and comparing predicted vs. actual outcomes"],"best_for":["Data-driven sellers willing to experiment with pricing and measure outcomes","Sellers with sufficient traffic/sales volume to run statistically significant A/B tests","Sellers wanting to continuously improve pricing models based on real-world feedback"],"limitations":["Simulation accuracy depends on elasticity model quality — poor models produce misleading predictions","A/B testing requires sufficient traffic and time to reach statistical significance; low-traffic products may need weeks to generate reliable results","Free tier likely excludes A/B testing framework; may only provide basic simulation","Confounding variables (seasonality, competitor actions, external events) can invalidate A/B test results","Requires careful test design (sample size, duration, control group selection) — poorly designed tests produce unreliable results"],"requires":["Elasticity model (from demand analysis capability)","Historical sales data for simulation baseline","Ability to apply different prices to test groups (requires channel API support or manual implementation)","Sufficient traffic/sales volume for statistical significance (typically 100+ transactions per variant per week)"],"input_types":["proposed price change (SKU, current_price, new_price)","elasticity estimate (elasticity_coefficient, confidence_interval)","test parameters (test_duration_days, control_group_size, significance_level)"],"output_types":["simulation results (predicted_revenue_change, predicted_margin_change, predicted_demand_change, confidence_interval)","A/B test results (control_group_metrics, test_group_metrics, p_value, statistical_significance)","test recommendations (sample_size_needed, test_duration_needed, power_analysis)"],"categories":["planning-reasoning","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_pricegpt__cap_9","uri":"capability://memory.knowledge.historical.pricing.data.storage.and.trend.analysis","name":"historical pricing data storage and trend analysis","description":"Maintains a time-series database of own prices, competitor prices, and sales data, enabling historical analysis and trend detection. Supports queries like 'show me price trends for this product over the last 6 months' or 'identify products where I've consistently underpriced vs. competitors'. Includes data retention policies and archival to manage storage costs while preserving historical context.","intents":["I want to see how my prices and competitor prices have changed over the last 6-12 months","I want to identify seasonal pricing patterns to inform next year's strategy","I want to understand if my pricing changes actually impacted sales (correlation analysis)"],"best_for":["Sellers with 6+ months of pricing history who want to understand trends","Seasonal businesses (fashion, toys, home goods) where historical patterns inform future strategy","Sellers wanting to analyze pricing effectiveness retrospectively"],"limitations":["Historical data quality depends on data collection consistency — gaps or errors in historical data degrade analysis","Free tier likely limited to recent history (e.g., last 30-90 days) without long-term retention","Trend analysis requires sufficient data points; short histories (< 3 months) may show noise rather than real trends","Correlation between price and sales does not imply causation — other factors (seasonality, marketing, competitor actions) may drive observed changes","No indication of data retention policies or archival costs for long-term storage"],"requires":["Consistent price tracking (own prices and competitor prices recorded daily or more frequently)","Sales data linked to pricing (date, SKU, quantity, price)","Data storage infrastructure (database, data warehouse, or cloud storage)"],"input_types":["historical price snapshots (timestamp, SKU, own_price, competitor_prices)","historical sales data (timestamp, SKU, quantity, revenue)"],"output_types":["price trend charts (time-series visualization of price over time)","trend analysis reports (identified trends, seasonality, anomalies)","correlation analysis (price vs. sales correlation, lag analysis)"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":40,"verified":false,"data_access_risk":"high","permissions":["Product catalog with standardized identifiers (GTIN, SKU, or UPC)","API keys for supported marketplace integrations (Amazon SP-API, eBay API, etc.)","Inventory sync mechanism (CSV upload, API, or webhook) to keep product data current","Internet connectivity for outbound web scraping and API calls","Historical sales data (minimum 6 months, ideally 12+) with columns: date, SKU, quantity_sold, price, channel","Consistent pricing and inventory data across the period","Product categorization (category, subcategory) to enable category-level elasticity learning","Baseline list of known competitors (URLs or marketplace seller IDs)","Web scraping infrastructure to detect new sellers/competitors","Product matching capability to identify which products new competitors are selling"],"failure_modes":["Accuracy depends on product matching quality — requires clean, standardized product data (GTIN/SKU) to avoid false matches","Web scraping may be rate-limited or blocked by some retailers; API-based integrations limited to platforms offering official pricing APIs","Latency of 2-24 hours typical for web-scraped data vs real-time for API sources; some channels may not be covered","Cannot track prices behind authentication walls or dynamic pricing that varies by geography/user","Requires clean historical data (sales volume, price, date) — garbage in, garbage out; missing or inaccurate data degrades model accuracy","Model accuracy improves with 12+ months of data; shorter histories lead to overfitting or unreliable elasticity estimates","Cannot account for external shocks (seasonality, supply chain disruptions, viral trends) without explicit feature engineering","Elasticity varies by customer segment, geography, and channel — single global elasticity estimate may be oversimplified","Free tier likely excludes advanced elasticity modeling; may only provide basic competitor-matching recommendations","Detection accuracy depends on web scraping coverage — competitors not tracked in initial setup may go undetected","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.31666666666666665,"quality":0.72,"ecosystem":0.15000000000000002,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.35,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:32.438Z","last_scraped_at":"2026-04-05T13:23:42.560Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=pricegpt","compare_url":"https://unfragile.ai/compare?artifact=pricegpt"}},"signature":"TG82ooiRAo6yXR8UPubU4AkUpbd2DbXtutsrrA02dyySSUQAeT1gojQ3lxvjmQP5s9WUtadl9RTnPUhrdLxOAA==","signedAt":"2026-06-21T00:07:13.733Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/pricegpt","artifact":"https://unfragile.ai/pricegpt","verify":"https://unfragile.ai/api/v1/verify?slug=pricegpt","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}