Accelerating Amazon Opportunity Evaluation

Calm Ventures is a technology-focused venture investment firm that works with founders to build innovative companies. Beyond investment capital, the firm connects portfolio companies with entrepreneurs, operators, investors, and industry specialists to help accelerate growth and market expansion. 

Its work involves evaluating opportunities across technology-driven businesses and supporting companies as they refine strategy, partnerships, and go-to-market execution. Efficient access to reliable market intelligence can strengthen these decisions by making large volumes of commercial data easier to assess and compare. 

Amazon contains extensive product and seller data, and it becomes very hard to identify commercially relevant opportunities across large catalogs. It requires repeated research across categories, stock conditions, pricing, sales activity, and product history. 

The client needed a more efficient way to discover products matching specific criteria and then enrich selected ASINs with the information required for deeper evaluation. With datasets reaching more than 300,000 products, individual lookups and manual analysis could not provide the required scale or speed. 

Key Issues 

• Identifying relevant products across large Amazon categories and subcategories 
• Finding out-of-stock products that continued to demonstrate recent sales demand 
• Processing hundreds of thousands of ASINs efficiently 
• Collecting pricing, sales rank, FBA fees, brand, category, shipping, and seller information 
• Operating within Keepa API request and result limits 
• Avoiding repeated processing of previously reviewed products 
• Preserving progress during long-running enrichment jobs 
• Supporting Amazon marketplaces across multiple regions 
• Producing structured outputs suitable for further analysis and decision-making 

The project therefore required product discovery and enrichment to work as one coordinated process. The goal was to narrow large Amazon catalogs to relevant opportunities, enrich those products with detailed market data, and make the resulting intelligence easier to evaluate at scale.

The solution moved from targeted product screening to detailed market enrichment. We created a structured workflow for evaluating Amazon opportunities. Instead of treating search and analysis as separate tasks, the system first narrowed the catalog using commercial criteria. Once the base was set, it enriched selected ASINs with the detailed information needed for evaluation. 

Opportunity Discovery 

Keepa's Product Finder was used to identify products that match specific business criteria. The list included stock status, recent sales activity, weight limits, category, and product structure. This reduced the volume of irrelevant items before deeper analysis and helped surface products with stronger demand signals. 

Adaptive Catalog Coverage 

Large Amazon categories were processed through a dynamic sales-range strategy. This helped us to adjust query ranges when Keepa result limits were reached. High-density ranges were automatically divided into smaller segments, helping preserve broader product coverage. It also ensured that there are no missing records (hidden behind API result caps). 

High-Volume Product Enrichment 

Selected products were grouped into batches of up to 250 ASINs and enriched through Keepa. We updated the details with pricing, FBA fees, sales rank, brand, category, Buy Box shipping, seller, and related product information. Immediate output persistence after each batch allowed long-running jobs to continue from the last successful point if interrupted. 

Demand & Availability Validation 

Out-of-stock opportunities were evaluated against recent sales activity before being included in research outputs. Historical rank information and 30-day sales signals helped distinguish products with demonstrated demand from items that were unavailable but commercially less relevant. 

Recurring Opportunity Delivery 

The system was designed to run on a schedule and identify new opportunities, without wasting resources. Previously processed ASINs were tracked to prevent unnecessary repeat queries.  Qualified results were exported into structured files and were delivered automatically through Discord to the relevant person (for review). 

Key Deliverables 

• Combined Amazon product discovery and enrichment into a unified research workflow 
• Implemented multi-criteria product filtering using Keepa 
• Developed adaptive sales-range pagination for dense Amazon categories 
• Processed product enrichment in batches of up to 250 ASINs 
• Captured pricing, fees, sales rank, category, brand, shipping, and seller information 
• Added checkpoint-based persistence for long-running enrichment jobs 
• Implemented processed-ASIN tracking to reduce redundant API requests 
• Added scheduled product discovery for recurring market monitoring 
• Automated structured CSV and JSON outputs for downstream analysis 
• Integrated Discord delivery for newly identified product opportunities 

The following technologies supported product discovery, enrichment, processing, and recurring delivery.

Quicker Market Decisions 

Shortened the time between identifying products and having detailed pricing, demand, fee, and category information available for evaluation. 

More Efficient Research Operations 

Reduced the resource overhead associated with large catalog analysis. This allowed high-volume enrichment to operate efficiently within Keepa usage constraints. 

Stronger Opportunity Relevance 

Focused research outputs on products with clearer commercial signals, helping teams spend less time reviewing unsuitable or low-value items. 

Reliable Multi-Hour Research Runs 

Large catalog analyses could continue with less risk of losing completed work. This gave a significant boost to reliability during extended processing cycles. 

Better Market Visibility 

Structured product intelligence made pricing, sales rank, fees, seller activity, and availability easier to compare across categories and marketplaces.