Search Platform
We deliver end-to-end technology solutions that drive innovation and create lasting business value.
Global Enterprise Search Platform (ServiceNow)
Technology Stack: Azure AI Search, Apache Solr, ServiceNow, Azure OpenAI, Python, Node.js, Redis, OWL Ontologies
Led migration from Apache Solr to Azure AI Search for global enterprise search platform serving 50,000+ employees. Implemented enterprise ontology using OWL for standardized taxonomy across departments and knowledge domains, enabling semantic search and intelligent query expansion. Developed RESTful APIs and ServiceNow workflows, integrated Azure OpenAI with prompt caching for intelligent search summaries with source citations. The system uses hybrid search combining keyword and semantic search with ontology-based query refinement, achieving 95% relevance improvement. Added multilingual support for 12+ languages with automatic language detection. Features include faceted navigation with dynamic filtering, personalized search results based on user role and history, real-time indexing with under 5-minute latency, and advanced query understanding with spell correction and synonym expansion powered by domain ontologies. Reduced average search time from 45 seconds to 3 seconds and increased user satisfaction by 68%.
Vehicle Trouble Codes Search Engine
Technology Stack: Apache Solr, Python, NLP (spaCy, NLTK), React, PostgreSQL, Redis, Kubernetes
Developed comprehensive Solr-based search infrastructure managing 50M+ automotive trouble code records with advanced NLP processing for semantic search and issue matching. Implemented NLP-based symptom analysis for matching user descriptions to diagnostic trouble codes, automated clustering of similar diagnostic cases for pattern recognition and ML-based recommendation engine for related problems and repair solutions. The system provides multi-faceted search across make, model, year, symptoms and diagnostic codes with integration to parts inventory system. Features diagnostic decision trees with confidence scoring, mobile-responsive interface optimized for technicians in repair shops and real-time search with autocomplete and query suggestions. Used spaCy for entity extraction from symptom descriptions and NLTK for text preprocessing and tokenization, enabling technicians to find relevant diagnostic information quickly and accurately, reducing diagnostic time by 50%.
Healthcare Knowledge Search Platform
Technology Stack: Apache Solr, Python, MeSH, SNOMED CT, ICD-10, OWL/RDF, DBpedia SPARQL, React, PostgreSQL
Description: Built comprehensive healthcare knowledge search platform using Apache Solr with medical ontology integration for clinical research and healthcare professionals. Implemented Medical Subject Headings (MeSH) taxonomy for standardized medical terminology mapping and integrated medical ontologies (SNOMED CT, ICD-10) for disease classification and clinical decision support. Used DBpedia SPARQL endpoint for enriching medical entity information and linking symptoms to relevant medical literature. The system leverages semantic web technologies (OWL/RDF) for representing medical knowledge graphs and reasoning over clinical data. Features include advanced medical concept search with synonym expansion, automated medical entity disambiguation using ontologies, cross-referencing of diseases, symptoms, and treatments, integration with PubMed and medical journals and semantic search capabilities for finding related medical concepts. Supports federated search across multiple medical databases with unified ranking and role-based access control for different user types (clinicians, researchers, students). Achieved 94% accuracy in medical concept matching and reduced research time by 60% for healthcare professionals.
Global Job Posting & Search Platform for Startup
Technology Stack: Python, Elasticsearch, React, Node.js, PostgreSQL, Redis, AWS, Docker, Kubernetes
Helped a startup launch worldwide job posting and job search portal from ground up, providing end-to-end platform development and deployment. Built scalable job board platform using Elasticsearch for advanced job search capabilities across multiple countries and industries with multilingual support for 15+ languages. Implemented sophisticated matching algorithm using NLP for candidate-job fit scoring, resume parsing with 96% accuracy for automatic profile creation and Boolean search with complex filtering (location, salary, experience, skills, industry). Features include employer dashboard for job posting and candidate management, candidate portal with profile building and job alerts, application tracking system (ATS) integration, real-time job recommendations based on candidate profile and behavior, email notification system for job alerts, application updates and payment gateway integration for premium job postings. Implemented geolocation-based search for nearby jobs, salary benchmarking tools, and company reviews and ratings system. Platform handles 50,000+ active job listings across 40+ countries, processes 100,000+ applications monthly and achieved 2 million registered users within first year. The startup successfully secured Series A funding based on platform traction and user growth.
Decentralized Web Search Engine on Blockchain Network
Technology Stack: Apache Solr, React, Node.js, Python, Distributed Crawlers, Blockchain Network Infrastructure
Developed decentralized web search engine leveraging nodes available in blockchain network, providing censorship-resistant and privacy-focused alternative to traditional search engines. Implemented distributed Apache Solr deployment across blockchain network nodes with 50 billion documents indexed across multiple distributed Solr instances. The search architecture uses blockchain network infrastructure for hosting Solr nodes without implementing blockchain technology for indexing itself – Solr handles all search indexing and query processing traditionally. Built distributed web crawling system where crawler instances run on network nodes, feeding indexed data to corresponding Solr instances for efficient distribution of crawling workload. Features include privacy-preserving search with no query logging or user tracking, federated search across distributed Solr nodes with result aggregation, distributed index preventing single point of failure, load balancing across multiple Solr instances and node-based architecture where participants contribute computing resources and bandwidth. Implemented geographic distribution of Solr nodes for improved search latency worldwide, automated shard distribution for horizontal scaling, and fault-tolerant architecture with replica management. The search engine indexes 50 billion web pages across distributed Solr nodes, processes 100K+ daily searches with sub-3 second response time, serves 2,000+ active nodes contributing storage and compute and achieved 99.95% uptime with automatic failover mechanisms.