Search Platforms Projects
Large-scale search engineered by Exuverse — from a 50,000-employee ServiceNow enterprise migration to healthcare knowledge graphs and 50B+ page indexes. Solr, Elasticsearch and Azure AI Search, tuned for relevance and sub-second discovery.
Search engineering behind IntelloWork (enterprise search & RAG chatbot) and ProtectComply (India’s DPDP compliance platform).
Search Systems Built for Precision & Scale
From ServiceNow enterprise search to healthcare knowledge graphs and decentralized search infrastructure, Exuverse engineers discovery systems that turn complex data into fast, relevant results.
Global Enterprise Search Platform ServiceNow
Led migration from Apache Solr to Azure AI Search for a global enterprise search platform serving over 50,000 employees.
Implemented enterprise ontology using OWL for standardized taxonomy across departments, enabling semantic search and intelligent query expansion. The system combines keyword and semantic search with ontology-based query refinement and multilingual support for 12+ languages.
Vehicle Trouble Codes Search Engine
Developed large-scale search infrastructure managing over 50 million automotive diagnostic trouble codes.
The platform integrates NLP-based symptom analysis allowing technicians to match real-world vehicle issues with diagnostic codes. Using spaCy and NLTK for entity extraction and preprocessing, the system delivers semantic search, real-time suggestions and diagnostic decision trees.
Healthcare Knowledge Search Platform
Built healthcare knowledge search platform integrating medical ontologies including MeSH, SNOMED CT and ICD-10 to provide semantic search for clinicians and researchers.
The system leverages semantic web technologies and medical knowledge graphs to connect diseases, symptoms and treatments while integrating PubMed and medical journals for federated research search.
Global Job Posting & Search Platform
Built a global job posting and job search platform supporting multilingual search across 40+ countries.
The system includes advanced candidate-job matching algorithms using NLP and resume parsing. The platform handles over 50,000 active job listings and processes 100,000+ applications monthly with advanced recruitment analytics.
Decentralized Web Search Engine on Blockchain Network
Developed decentralized web search engine leveraging distributed nodes within blockchain network infrastructure.
The system indexes over 50 billion web pages across globally distributed Solr clusters. The architecture provides privacy-focused search without user tracking and uses distributed indexing with automatic failover for high availability and global scalability.
Search Platform Impact
50k+
Enterprise Employees Served
95%
Search Relevance Improvement
50M+
Diagnostic Codes Managed
50B+
Web Pages Indexed
Ready to Build Intelligent Search Infrastructure?
Partner with Exuverse to engineer enterprise search, semantic discovery and relevance platforms that help users find the right information faster.
Start Your Search ProjectHow We Engineer Search Platforms
Great search is measured, not guessed. We build discovery systems the way we built IntelloWork’s enterprise search — grounded in relevance evaluation, semantic modeling and infrastructure that stays fast at scale.
Model the Domain
We study your content, taxonomy and how users actually query. Where it adds value we build an ontology (OWL/RDF, MeSH, SNOMED) so the engine understands meaning, not just keywords.
Index & Retrieve
We stand up the right engine — Solr, Elasticsearch or Azure AI Search — with tuned analyzers, hybrid keyword-plus-semantic retrieval and distributed indexing that scales from millions to billions of documents.
Tune Relevance
We measure relevance against a labeled judgment set and iterate: query expansion, re-ranking, synonyms and boosting. Improvements are proven against real queries, not vibes.
Scale & Operate
Sub-second response under load with caching, sharding and automatic failover. We hand over dashboards and relevance metrics so quality holds as your corpus and traffic grow.
Search Platform FAQs
Which search engine should we use — Solr, Elasticsearch or Azure AI Search?
It depends on your scale, hosting and semantic needs. We’re engine-agnostic and have shipped all three — including a full Apache Solr to Azure AI Search migration for a 50,000-employee enterprise. We recommend based on your data volume, relevance requirements and cloud, not a favorite tool.
How do you actually improve search relevance?
We treat relevance as an engineering discipline: build a labeled judgment set, measure precision and recall, then iterate with query expansion, re-ranking, synonyms, ontology-based refinement and boosting. Every change is validated against real queries so quality provably improves.
Can you handle very large or multilingual corpora?
Yes. Our platforms index from tens of millions of records up to 50B+ web pages using distributed clusters, sharding and automatic failover, with multilingual support across 12+ languages and 40+ countries in production deployments.
What is semantic and ontology-based search, and do we need it?
Semantic search understands intent and relationships, not just literal keywords. With an ontology (OWL/RDF, or domain standards like MeSH and SNOMED CT in healthcare) the engine connects related concepts — diseases to symptoms, parts to diagnostics. It’s worth it when your users search by meaning, not exact terms.
Can search run alongside AI and RAG?
Absolutely — strong retrieval is the foundation of good RAG. We combine keyword and vector search with re-ranking to feed accurate, grounded context to AI assistants, the same hybrid approach behind IntelloWork’s enterprise RAG chatbot.
Ready to build intelligent search infrastructure?
Tell us what your users struggle to find. We’ll show you how to make discovery fast, relevant and semantic — and how we’d measure that it worked.
Book a Free Search Consultation