Intelligent B2B Supplier Search Engine with Elasticsearch
The vendor platform needed a high-performance search engine capable of handling complex multi-faceted queries across thousands of international suppliers with sub-second response times.
Discuss Your Project
The Challenge
Traditional database queries couldn't meet the search requirements:
- Full-text search across 80+ fields per vendor was too slow with SQL
- Priority-based ranking needed to factor in data completeness and verification
- Social media presence needed to be searchable as a first-class attribute
- Fuzzy matching and typo tolerance were essential for international supplier names
- Category and location hierarchies required faceted search capabilities
Our Solution
We implemented a custom Elasticsearch integration with priority-based indexing, multi-field search, and intelligent ranking for vendor discovery.
Architecture
- Search Engine: Elasticsearch with custom mappings for vendors, categories, social media
- Data Layer: TypeORM/PostgreSQL as source of truth, synced to Elasticsearch
- API Layer: Node.js/Express with Elasticsearch client
- Frontend: React with real-time search-as-you-type
- Analytics: PostHog for search behavior tracking
Search Capabilities
- Multi-Field Search - Query across vendor name, description, brands, categories simultaneously
- Social Media Filtering - Find vendors by their presence on specific platforms
- Category Facets - Drill down through product category hierarchies
- Location Filtering - Search by country, region, or city
- Priority Ranking - Verified and data-complete vendors rank higher
- Fuzzy Matching - Handles typos and international name variations
Key Features
- Custom Index Mappings - Optimized schema for vendor, category, and social media data
- Real-Time Sync - Database changes reflected in search within seconds
- Search Analytics - Track popular queries, zero-result searches, and click-through rates
- Bulk Indexing - Efficient batch indexing for large vendor imports
- Weighted Scoring - Configurable relevance scoring based on field importance
Results
Technology Stack
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Frequently Asked Questions
MicrocosmWorks configured Elasticsearch with custom analyzers that combine edge n-gram tokenization for partial matching, synonym dictionaries for industry terminology, and a dedicated keyword field for exact part number lookups. This approach returns relevant suppliers even when buyers use different terminology than what appears in the supplier's catalog.
MicrocosmWorks designed the Elasticsearch cluster with a sharding strategy that distributes supplier documents across multiple nodes based on industry vertical, enabling horizontal scaling without reindexing. The architecture supports cross-cluster search for geographic distribution, maintaining sub-200ms query response times even at millions of supplier records.
Yes, MicrocosmWorks implemented function score queries that dynamically boost supplier rankings based on buyer-defined weights for proximity, MOQ fit, lead time, certification requirements, and past transaction history. Buyers can save their weighting profiles and apply them across searches for consistent sourcing preferences.
MicrocosmWorks built a change data capture pipeline using Debezium connected to the PostgreSQL source database, streaming supplier record changes to Elasticsearch in near real-time via Kafka. This ensures search results reflect database updates within seconds rather than waiting for batch reindex cycles.
MicrocosmWorks delivers Elasticsearch-powered search solutions at rates of $20-$45/hr, with a full B2B supplier search engine including custom analyzers, relevance tuning, faceted filtering, and CDC pipeline typically requiring 350-550 development hours. The Elasticsearch infrastructure itself runs cost-effectively on three-node clusters starting around $500/month on AWS.
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