VoomiSupply: From Keyword Search to a Self-Updating HVAC Commerce Platform
AI & Data Engineering Case Study
VoomiSupply was running a 1.5M+ SKU HVAC marketplace on keyword-only search — buyers couldn't find what they needed, catalogue data was incomplete, and the team had no unified view of what was performing. The business needed a smarter platform, not a better plugin.
Published: 5 April 2026 · By Auriga IT · AI & Data Engineering · Pennsylvania, USA
1.5M+
Product SKUs Processed
80,000+
Enriched HVAC Product Types
Real-Time
Data Enrichment Pipelines
AI
Semantic Search Engine
Cygnus
Admin + Insights Platform
Live
Search & Traffic Analytics
01 - About the Client
VoomiSupply
VoomiSupply
B2B & Retail E-Commerce Marketplace · Pennsylvania, USA
Founded in 2019, VoomiSupply is a technology-driven marketplace serving contractors, technicians, and homeowners across HVAC, plumbing, electrical, and industrial supply categories. With more than 1.5 million products listed on Shopify, the business had grown faster than its product discovery and data infrastructure could support.
As the catalogue expanded, the gaps became harder to ignore. Buyers searching for HVAC products by specification — SEER rating, BTU, tonnage, zone configuration — were hitting keyword mismatches. Product records were incomplete. The team had no single place to see what was selling, what was mis-categorised, or where search was breaking down. The keyword-search plugin they were running was never built for a marketplace of this complexity.
02 - The Challenge
A Catalogue That Had Outgrown Its Search
Four gaps were compounding as VoomiSupply scaled: unstructured product data across 1.5M+ SKUs, no automated way to enrich HVAC-specific attributes, a keyword-only search that could not interpret buyer intent, and no unified view of how the platform was actually performing.
Each problem made the others worse. Bad data degraded search results. Poor search results made attribute gaps more painful. And without a central analytics view, the team couldn't measure any of it.
01
Unstructured Product Catalogue
1.5M+ listings included incomplete, inconsistent, and weakly classified data. Buyers searching by specification — SEER, BTU, tonnage — were getting unreliable results because the underlying data wasn't structured to support it.
02
No Attribute Enrichment at Scale
Critical HVAC attributes were often missing or inaccurate across the catalogue. With 1.5M+ SKUs, manual upkeep was impossible — there was no automated pipeline to fill or correct these gaps.
03
Keyword-Only Search
The existing Searchanise plugin matched exact keywords and could not interpret buyer intent, handle technical specification queries, or understand natural-language product descriptions. A buyer searching “2-ton heat pump for small office” got nothing useful.
04
No Centralized Platform Visibility
Search behaviour, product performance, stock distribution, conversion ratios, and traffic quality were tracked across separate tools — or not tracked at all. Stakeholders had no single operational view.
03 - The Solution
Five Integrated Capabilities, One Intelligence Platform
Five capabilities were built and integrated: a custom AI semantic search engine replacing Searchanise, automated HVAC data enrichment pipelines, Cygnus Copilot for conversational catalogue admin, Cygnus Insights for real-time analytics, and a purpose-built search layer designed specifically for marketplace-scale complexity.
Rather than extending a plugin not designed for this scale, the entire search and data stack was replaced with a connected system — where enriched product data feeds better search, search behaviour informs analytics, and the admin layer can act on both.
1
AI Semantic Search Engine
Buyers can now search in natural language — product type, capacity, use-case, or specification — and get relevant HVAC results instead of keyword mismatches. The engine understands intent rather than just matching text, so a query like “quiet 2-ton mini split for basement” maps correctly to the right product category and attributes. This replaces the keyword-only Searchanise plugin with a purpose-built search layer designed for a 1.5M+ SKU catalogue.
Natural-language product queriesIntent-based result mappingHVAC specification understandingReplaces Searchanise plugin
2
Cygnus Copilot — Conversational Admin
The internal team can now manage search behaviour, inspect catalogue coverage, and update search logic through plain-English prompts — without manual tagging, spreadsheet-based updates, or developer involvement for routine operations. Synonym mapping, product intent linking, and catalogue question answering all happen through a single conversational interface. A memory-driven learning layer means the system improves progressively as the team uses it.
Natural-language catalogue queriesSearch term and synonym mappingPrompt-based admin workflowsMemory-driven progressive learning
3
Real-Time Data Enrichment Pipelines
Product records across the full 1.5M+ SKU catalogue are now continuously enriched with structured HVAC technical attributes — SEER, SEER2, BTU, tonnage, zones, and product classifications — without manual upkeep. Pipelines process product and inventory updates automatically as they come in, so the catalogue stays accurate as VoomiSupply's supplier base expands. The enriched data directly improves search relevance and filter accuracy for buyers.
SEER, SEER2, BTU, tonnage enrichmentAutomated at 1.5M+ SKU scaleContinuous ingestion and classificationFeeds search relevance directly
4
Cygnus Insights — Real-Time Analytics Dashboard
Stakeholders now have a single place to see catalogue structure, stock distribution, pricing ranges, search trends, add-to-cart behaviour, conversion metrics, and traffic quality — all updating in real time. The dashboard also identifies and controls bot and scraper activity through traffic monitoring and IP blocklisting, giving the team both business intelligence and platform protection in one view.
Product and stock distributionSearch trends and conversion metricsAdd-to-cart and pricing analyticsBot detection and IP blocklisting
5
Purpose-Built Search Layer Replacing Searchanise
Rather than extending Searchanise — a keyword-matching plugin not designed for marketplace complexity — the entire search layer was replaced with a system built specifically for VoomiSupply's catalogue scale, HVAC product taxonomy, and internal operational needs. The replacement is integrated with the enrichment pipelines and analytics platform, so search quality improves as the data does, and the team can see the impact in real time.
Full Searchanise replacementHVAC taxonomy-aware searchIntegrated with enrichment pipelinesSearch quality visible in analytics
04 - Technology Stack
What Powered the Platform
Shopify as the commerce foundation, a custom semantic search engine for intent-based discovery, LangGraph and Neo4j for agent orchestration and memory, PostgreSQL with pgvector for semantic retrieval, and real-time enrichment pipelines for automated HVAC attribute quality at scale.
Category
Technology
Role
Commerce
Shopify
Existing marketplace foundation for product catalogue and transactions
Search
Semantic Search Engine
Maps buyer intent and natural-language queries to relevant HVAC products
Admin AI
Cygnus Copilot
Prompt-based interface for search management and catalogue operations
Analytics
Cygnus Insights
Real-time dashboards for catalogue, search, traffic, and conversion visibility
Agents
LangGraph + Neo4j
Orchestration and memory relationships for adaptive AI workflows
Retrieval
PostgreSQL + pgvector
Semantic retrieval and vector-based product intelligence
Pipelines
Real-Time Enrichment
Automated ingestion, classification, and HVAC attribute enrichment at scale
05 - Business Impact
What Changed After Go-Live
VoomiSupply moved from a keyword-driven storefront with incomplete data and no unified analytics to an intent-driven marketplace where search, enrichment, admin, and visibility all work from the same connected platform.
Buyers Find Products by Intent, Not Just Keywords
Natural-language queries — by capacity, use-case, or specification — now return relevant HVAC results. Buyers no longer need to know the exact product name or model number to find what they're looking for.
Catalogue Quality Maintained Without Manual Work
HVAC attributes across 1.5M+ SKUs are enriched automatically as new products come in. The team no longer maintains product data by hand — the pipelines do it continuously as the supplier base grows.
Admin Operations Run Through Prompts, Not Spreadsheets
Search term mapping, synonym management, and catalogue queries that previously required manual effort or developer time now happen through plain-English prompts in Cygnus Copilot.
Stakeholders See One Live View of Platform Performance
Search trends, stock distribution, conversion metrics, pricing ranges, and traffic quality are all visible in one real-time dashboard — replacing scattered tools and manual reporting.
Bot and Scraper Traffic Identified and Controlled
Traffic monitoring within Cygnus Insights flags bot and scraper patterns as they occur. The team can act immediately through IP blocklisting rather than discovering the problem after the fact.
Search Quality Improves as the Catalogue Does
Because the search engine, enrichment pipelines, and analytics platform are integrated, better product data produces better search results — and the impact is visible in the analytics dashboard without manual cross-referencing.
06 - Frequently Asked Questions
Questions About This Project
What is VoomiSupply?
+
VoomiSupply is a technology-driven B2B and retail e-commerce marketplace based in Pennsylvania, USA, offering over 1.5 million products across HVAC, plumbing, electrical, and industrial supply categories. Founded in 2019, it serves contractors, technicians, and homeowners through Shopify.
What was the problem with VoomiSupply's product search?
+
VoomiSupply was using Searchanise, a keyword-matching plugin not designed for a 1.5M+ SKU HVAC marketplace. It could not interpret buyer intent, handle natural-language queries, or support technical specification searches. Combined with incomplete catalogue data, buyers were unable to find products by SEER rating, BTU, tonnage, or use-case description.
How does the AI semantic search engine work?
+
The custom semantic search engine understands the intent behind a buyer's query rather than matching exact keywords. A search like “quiet 2-ton mini split for basement” is mapped to the relevant HVAC product category and specification attributes, returning accurate results even when the query doesn't contain the exact product name. It is built on PostgreSQL with pgvector for semantic retrieval.
What is Cygnus Insights?
+
Cygnus Insights is a real-time analytics dashboard that gives VoomiSupply stakeholders a single view of catalogue structure, stock distribution, pricing ranges, search trends, add-to-cart behaviour, conversions, and traffic quality — including bot detection and IP blocklisting for unwanted scraper activity.
What is Cygnus Copilot?
+
Cygnus Copilot is a conversational admin layer that lets VoomiSupply's internal team manage search behaviour, map synonyms, inspect catalogue coverage, and answer product intelligence questions through plain-English prompts — without manual tagging or spreadsheet-based workflows. It uses a memory-driven learning architecture that improves outputs progressively over time.
Why was Searchanise replaced rather than extended?
+
Searchanise is designed for keyword search on standard e-commerce stores. VoomiSupply needed semantic understanding of buyer intent, automated enrichment for HVAC-specific attributes, conversational admin tooling, and real-time analytics — capabilities that cannot be added to a keyword-matching plugin. A purpose-built replacement was the only way to address the full problem.
What AI technologies power the platform?
+
The platform uses a custom semantic search engine, Cygnus Copilot for conversational admin AI, LangGraph and Neo4j for agent orchestration and memory relationships, PostgreSQL with pgvector for semantic retrieval, and real-time enrichment pipelines for automated HVAC catalogue quality at scale.
Running a Large Catalogue on Keyword-Only Search?
If your e-commerce platform is growing faster than your search and data infrastructure can support, Auriga IT builds custom AI search engines, enrichment pipelines, and analytics platforms designed for catalogue complexity — not generic plugins.
