[ Built for speed and relevance ]
AI Search
A search stack built for production relevance and performance, with a Nuxt + Nitro native developer experience.
Fast by default
Full-text search with typo tolerance, prefix matching, and ranked results.
Hybrid + vector ready
Combine keyword relevance and semantic vectors in the same query flow.
Behavior you can tune
Tune lexical/semantic balance with semanticRatio, filters, sort, and facets.
Design an experience users trust
This stack is focused on search quality: fast full-text retrieval, hybrid vector matching, and precise filtering controls for production UX.
Hybrid retrieval: exact keyword matches plus semantic vector matches
Multi-search API for federated queries across multiple indexes
Geo search with radius, bounding box, and polygon constraints
A retrieval pipeline built for real traffic
From implementation to operations, developers get Nuxt + Nitro native flows, tenant isolation patterns, and optimized JAMlabs SDK integration.
Step 1
Ingest and normalize
Index documents and keep data current with regular ingestion jobs.
POST /indexes/docs/documents
[{ "id": 1, "title": "API auth" }]Step 2
Nuxt + Nitro native integration
Connect search in Nuxt routes and Nitro server handlers using JAMlabs wrappers for faster implementation.
Step 3
Multi-tenant platform ready
Isolate indexes and search configs per tenant while keeping one shared operational platform.
Step 4
Developer experience first
Use optimized SDK flows in JAMlabs to manage search, filters, facets, sorting, and geo from one module.
Built for high-impact workflows
From docs portals to catalog discovery, deploy one search layer that supports both lexical and vector retrieval.
Documentation Search
Ship reliable docs lookup with typo-tolerant full-text and faceted filtering.
Catalog Discovery
Power product discovery with hybrid keyword + vector ranking and relevance tuning.
Geo-aware Search
Filter and rank results by location for store finders and local content experiences.
Frequently asked questions
Common questions teams ask before shipping search to production.
Ship your AI search landing in weeks, not quarters
Define your first production scope with us: index model, ranking strategy, filters, and vector rollout plan.