Can you add search to an existing application with a large database?

Yes. We implement a sync pipeline that indexes your existing database into the search engine and keeps it updated via change data capture or event-driven triggers. No downtime required for initial indexing.

How do you handle multi-language search?

We configure language-specific analyzers with appropriate stemming, stop words, and tokenization rules. Multi-language content can be indexed with per-field language analyzers or separate language-specific indices.

What about search for geographic or location-based data?

Elasticsearch and Algolia both support geo-queries natively. We implement radius search, bounding box filters, and distance-based sorting for location-aware applications.

Help Users Find Exactly What They Need in Milliseconds

Full-text search with faceted filters, autocomplete, typo tolerance, and relevance ranking. Bookuvai builds search experiences that drive engagement and conversions.

Feature: Search & Filtering

Search and filtering transforms how users navigate your application. Poor search drives users away; great search increases time on site, conversion rates, and customer satisfaction. Bookuvai builds search systems with sub-50ms response times, intelligent ranking, and faceted navigation that adapts to your data model.

Benefits

Lightning-Fast Results: Search responses in under 50ms with pre-indexed data, edge caching, and optimized query strategies for any data volume.
Intelligent Autocomplete: As-you-type suggestions with typo correction, synonym matching, and popular query highlighting to reduce search friction.
Faceted Navigation: Dynamic filter panels with real-time count updates, range sliders for prices/dates, and multi-select category filters.
Relevance Ranking: Custom scoring algorithms that factor in recency, popularity, user behavior, and business rules to surface the best results first.
Analytics & Insights: Track search queries, zero-result rates, click-through patterns, and popular filters to continuously improve the experience.

How It Works

Data Analysis: Analyze your data model, search patterns, and performance requirements to select the right search engine and indexing strategy.
Index Design: Define search indices with field mappings, analyzers, tokenizers, and synonym dictionaries tailored to your content.
Search API Development: Build search endpoints with query parsing, filter application, pagination, and result highlighting.
Frontend Integration: Implement search UI with instant results, filter panels, sort controls, and keyboard navigation.
Tuning & Analytics: Fine-tune relevance scoring, add search analytics tracking, and set up A/B testing for ranking experiments.

Technology Options

Elasticsearch: Distributed search engine with powerful full-text capabilities, aggregations, geo-search, and custom scoring functions. (Best for: Large datasets with complex search requirements and custom ranking needs)
Algolia: Managed search API with built-in typo tolerance, instant search UI components, and AI-powered re-ranking. (Best for: E-commerce and content sites wanting best-in-class search with minimal setup)
PostgreSQL Full-Text Search: Native full-text search with tsvector/tsquery, GIN indexes, and trigram similarity. No additional infrastructure needed. (Best for: Applications already on PostgreSQL that need good search without extra services)
Meilisearch: Open-source, lightning-fast search engine with typo tolerance, faceting, and a developer-friendly REST API. Easy to self-host. (Best for: Teams wanting an open-source Algolia alternative with easy deployment)

Estimated Hours

Simple: 10-18 hours | Moderate: 18-35 hours | Complex: 35-70 hours

Frequently Asked Questions

Can you add search to an existing application with a large database?: Yes. We implement a sync pipeline that indexes your existing database into the search engine and keeps it updated via change data capture or event-driven triggers. No downtime required for initial indexing.
How do you handle multi-language search?: We configure language-specific analyzers with appropriate stemming, stop words, and tokenization rules. Multi-language content can be indexed with per-field language analyzers or separate language-specific indices.
What about search for geographic or location-based data?: Elasticsearch and Algolia both support geo-queries natively. We implement radius search, bounding box filters, and distance-based sorting for location-aware applications.