Aidena

— AI STACK RECOMMENDATION

AI Search & Autocomplete Stack for SaaS

Production-ready vector search with embeddings, semantic retrieval, and real-time autocomplete for SaaS products. Scales from startup to enterprise with managed infrastructure.

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AI Search & Autocomplete Stack for SaaS

Production-ready vector search with embeddings, semantic retrieval, and real-time autocomplete for SaaS products. Scales from startup to enterprise with managed infrastructure.

high confidence

Core Stack ℹ︎

Cohere Embed API

Primary

Multimodal embeddings (text, images, tables) with 100+ language support. Enterprise-grade API with built-in reranking for production search quality at startup-friendly pricing.

$0/month (free tier) or $50-200/month at scale

Elasticsearch Vector Search

Primary

Hybrid search combining dense vectors (ANN) with BM25 full-text for autocomplete accuracy. Managed or self-hosted, scales horizontally, and includes built-in analytics for search quality monitoring.

$0/month (self-hosted) or $100-500/month (managed)

Complete the Stack ℹ︎

Cohere Rerank

Alternative

Cross-encoder reranking improves search relevance by 20-40% post-retrieval. Essential for autocomplete quality when users expect top results first.

$0.002/per 1K searches (~$20-100/month for startup)

Airbyte

Alternative

Syncs product data (documents, metadata, user content) into Elasticsearch. 300+ connectors enable continuous indexing from databases, APIs, and data warehouses for fresh search results.

$0/month (self-hosted) or $100-300/month (managed)

Arize Phoenix

Alternative

Open-source observability for search quality. Trace embedding generation, retrieval latency, and reranking performance to debug autocomplete issues in production.

$0/month (self-hosted)

Getting started

  1. 1Set up Elasticsearch cluster (self-hosted on EC2 or managed Elastic Cloud).
  2. 2Integrate Cohere Embed API to generate embeddings for product data (documents, metadata).
  3. 3Index embeddings into Elasticsearch vector field alongside BM25 text.
  4. 4Build autocomplete endpoint that queries both dense vectors and full-text, returning top-K results.
  5. 5Add Cohere Rerank as post-processing step to improve relevance ranking.
  6. 6Set up Airbyte to sync product data updates (daily or real-time) into Elasticsearch.
  7. 7Deploy Arize Phoenix for monitoring search latency, embedding quality, and user click-through rates.
  8. 8Implement caching layer (Redis) for frequent autocomplete queries to reduce latency below 100ms.
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