Users can't find what they're looking for. Search and filters fix that.
As a product's dataset grows, a simple list becomes unusable. Advanced search (full-text, fuzzy matching) and multi-dimensional filters (by date, status, category, tags) are the standard patterns. Implementing them correctly requires indexing strategy and query performance.
Application where users are struggling to find records in long lists — either no search exists, the search is too basic, or filters don't cover the fields users actually need
Search and filter requirements by use case:
Simple text search across a few fields: Postgres ILIKE '%term%' works for small tables (< 100k rows). Performance degrades on larger datasets. Works without additional infrastructure.
Full-text search (Postgres tsvector): Postgres has built-in full-text search using tsvector columns and tsquery queries. Creates an inverted index. Faster than ILIKE; supports stemming and relevance ranking. No external service required. Best for most applications up to a few million rows.
Fuzzy search (typo-tolerant): pg_trgm extension adds trigram-based fuzzy matching. Users searching "invioce" still find "invoice". Index on the column with GIN trigram index.
Full-featured search (Algolia, Typesense, Meilisearch): External search services with typo tolerance, faceted search, and sub-50ms queries at scale. Algolia is managed (expensive); Typesense and Meilisearch are self-hosted. Best for product search, content search, or any use case where search is a core product feature.
Filter implementation:
Multi-dimensional filters append WHERE conditions dynamically. The key is indexing: every column used in a filter should have a database index. Without indexes, filtered queries do full table scans and slow down as data grows.
Combined search + filter: Search query reduces the result set; filters narrow it further. Combining requires careful query construction to pass both the search term and filter conditions.
Full-text search with relevant results and multi-dimensional filters that load quickly even on large datasets
Postgres full-text search
setup (tsvector columns, GIN indexes)
Multi-field filter UI
with date range, status, category pickers
Dynamic query construction
that combines search and filters
Performance indexes
on all filtered columns
Pagination
that works with search and filter state
One honest number to start.
Fixed-scope, fixed-price. The number below is the starting point — final scope is built from your brief.
Full-text search with relevant results and multi-dimensional filters that load quickly even on large datasets
Three steps, every time.
The same repeatable engagement on every project. No surprises, no mystery, no billable ambiguity.
Brief & discovery.
We send you questions, then get on a call. Output: a written scope with every step, feature, and integration listed.
Build & ship.
Fixed schedule, weekly reviews. No scope creep unless you change the scope — and if you do, we reprice it transparently.
Warranty & retainer.
30-day warranty on every launch. Most clients stay on a monthly retainer for ongoing features and maintenance.
Why Fixed-Price Matters Here
Search scope is defined by the tables and columns that need to be searchable. Fixed-price from the data model.
Questions, answered.
When search is a core product feature (e-commerce product search, large content library), response time under 50ms is required at high volume, or faceted search with dynamic facet counts is needed. For most internal tools and B2B SaaS, Postgres full-text search is sufficient.
Semantic search (vector embeddings, pgvector) answers meaning-based queries ("show me invoices about construction") rather than keyword queries. Relevant for AI-assisted features. pgvector extension in Postgres enables this without external infrastructure.
Tell Ryel about your project.
Describe what you’re building and what outcome you need. You’ll have a written, fixed-price scope within the week.