Semantic search for SaaS: When keywords aren't enough

Ever search for "best laptop for coding" and get results for "optimal development machine"? That's semantic search at work: it gets what you mean, not just what you typed. It uses vector embeddings and transformer neural networks to capture intent and context. So "ways to cool down a room without AC" surfaces results about fans and cross-ventilation even when those exact terms aren't in the query.

If you're handling natural language questions, ambiguous searches, or diverse vocabularies in your SaaS app, semantic search can reduce zero-results rates and improve user experience. This guide covers how semantic search works, when it makes sense over keyword search, top SaaS use cases, and practical implementation considerations.

Why implement semantic search in your SaaS app?

If you're seeing high zero-results rates or users reformulating queries multiple times, semantic search can help. It understands what users mean rather than just matching their exact words—so "laptop cooling solutions" finds results about "thermal management" and "ventilation systems" even without those specific terms.

Teams often see improvements in user satisfaction and conversion when queries are naturally expressed questions rather than carefully crafted keyword combinations. You'll typically need embedding generation infrastructure, specialized vector storage, and Approximate Nearest Neighbor (ANN) search systems to make this work.

Redis supports vector search alongside sub-millisecond performance for many core operations, letting you add semantic search without introducing a separate vector database. Most production systems find the trade-off worth it when their users struggle with keyword-based search or ask questions in natural language.

What is semantic search & how does it work?

Semantic search converts text into numerical vector representations that capture meaning. The Transformer architecture powers this through self-attention mechanisms that understand relationships between words in context. When you search for "How big is London," the system doesn't just look for those exact words—it generates a vector embedding that represents the semantic concept of London, then finds documents whose embeddings are mathematically similar.

Self-attention computes relationships between all words in a sequence simultaneously. Given an input sequence, the system creates three learned projections: queries (Q), keys (K), and values (V). The attention formula computes Attention(Q, K, V) = softmax(QK^T / √d_k)V, where the softmax function normalizes attention weights to create a weighted combination of value vectors based on similarity between queries and keys.

For practical implementation, you'll use pre-trained models like Sentence Transformers, which offers over 500 models optimized for semantic search. The standard pattern is straightforward: load a model, encode your query and documents into vectors, then compute cosine similarity to find the closest matches. Models produce vectors ranging from 384 to 3,072 dimensions or higher—lower dimensions mean faster computation and less storage, while higher dimensions capture more semantic nuance.

How does semantic search work behind the scenes?

The production pipeline operates in two phases: offline indexing and online queries. During indexing, you split documents into chunks (a common starting point is 512 tokens, which is roughly 2,000 characters for English text as a rule of thumb, though token-to-character ratios vary by language and content type). Then you generate embeddings for each chunk and store them in a specialized vector index.

When queries arrive, you convert them through the same embedding model, perform similarity search, and optionally re-rank results using a cross-encoder for refinement.

Vector indexing at scale

For vector indexing at scale, you'll typically choose between two approaches. Hierarchical Navigable Small World (HNSW) creates a multi-layer graph designed for logarithmic search scaling, though actual performance varies with dataset characteristics and dimensionality. The trade-off: HNSW uses additional memory to store graph links, increasing storage requirements in exchange for faster, higher-recall search.

Alternatively, Inverted File with Product Quantization (IVF-PQ) reduces memory usage by compressing vectors into compact codes—often tens of bytes per vector—trading some accuracy for much lower storage requirements. Redis supports multiple vector index types, including FLAT, HNSW, and Scalable Vector Search with Vamana (SVS-Vamana).

In a billion-scale benchmark, Redis reported 90% precision with 200 ms median latency when retrieving the top 100 nearest neighbors under 50 concurrent queries. Because Redis is often deployed with a memory-first architecture and delivers sub-millisecond performance for many core operations, it's particularly effective for AI workloads requiring fast vector search. Redis provides hybrid query capabilities combining vector similarity with metadata filtering—important for multi-tenant SaaS apps requiring tenant isolation alongside semantic search.

Neural reranking for improved accuracy

Adding neural reranking often delivers measurable improvements. Two-stage pipelines combining semantic search with cross-encoder reranking can improve retrieval accuracy significantly; cross-encoders process query-document pairs jointly through a transformer, enabling more nuanced relevance scoring than bi-encoder approaches.

The initial retrieval phase uses fast approximate nearest neighbor search, while the reranker applies a more sophisticated (and computationally expensive) model to refine the top-k results.

Top use cases for semantic search in SaaS

Semantic search delivers the most value when users describe what they need in their own words rather than using exact terminology from your content. Here are the primary applications:

• Enterprise knowledge management: The most mature application. Systems handling technical documentation, policies, and internal communications benefit from semantic search's ability to surface relevant information regardless of exact terminology.
• Customer support & ticket resolution: AI-powered support tools show gains in productivity and routing accuracy. According to G2 research, agents supported by AI handle 13.8% more inquiries per hour. But most vendors operate hybrid models where AI supports human-led workflows rather than fully automating support. Semantic search works better when your underlying content is organized and current.
• Developer tools: Code assistance and technical documentation systems typically use Retrieval-Augmented Generation (RAG) pipelines where semantic search retrieves relevant code snippets or docs, then feeds them to language models for generating contextual responses.
• Product discovery: E-commerce contexts benefit from understanding natural language queries about product features and use cases.

The common thread across these use cases: semantic search shines when vocabulary varies widely and users don't know the exact terms your content uses.

What to consider when implementing semantic search

Before diving into implementation, you'll need to make decisions about models, infrastructure, and caching that shape your system's performance and cost profile.

Embedding model selection

Embedding model selection significantly influences what follows. You're balancing parameter count against inference latency—smaller models like all-MiniLM-L6-v2 (384 dimensions) offer faster inference with acceptable accuracy for many use cases, while larger models provide deeper semantic understanding at higher computational cost. Vector dimensionality directly impacts storage requirements—higher dimensions mean more storage per item. At scale, that difference matters.

Infrastructure requirements

You'll typically need more than just a database upgrade. ANN search systems handle high-dimensional vectors (traditional database indices aren't designed for similarity search in high-dimensional spaces), dedicated vector storage with efficient serialization patterns, and embedding generation infrastructure for both real-time query processing and batch document updates are typically needed.

Semantic search typically needs more advanced infrastructure, AI models, and ongoing tuning, making it more costly to build and operate than keyword search.

Caching strategy

Caching strategy becomes valuable at scale. Production systems often implement a pragmatic two-tier approach: traditional exact-match caching for repeat queries (fastest path) and semantic caching for intent-based query matching (fallback strategy). Semantic caching demands more infrastructure: embeddings for your content, real-time query processing, and ongoing compute costs.

Redis addresses this through semantic caching capabilities that enable similar query recognition beyond exact matches. Instead of making duplicate calls for "What's the weather?" and "Tell me today's temperature?" when they represent the same intent, you serve cached responses based on semantic similarity.

The RedisVL toolkit provides a SemanticCache interface combining traditional caching with vector search capabilities, while Redis LangCache offers a fully-managed service for teams wanting semantic caching without infrastructure overhead. This is particularly valuable for SaaS applications with LLM integration, where reducing API costs directly impacts unit economics.

Starting with hybrid search

A practical approach for most SaaS platforms is hybrid search from the outset. The architecture routes queries based on their characteristics—keyword-style queries to BM25 for precision, natural language queries to semantic search for contextual understanding.

This lets you validate semantic search benefits on use cases where they matter most—natural language questions and ambiguous searches—while preserving the cost and performance benefits of keyword search for exact-match patterns. You can validate semantic search benefits incrementally through controlled testing on your own domain-specific queries and user patterns, rather than replacing keyword search globally.

Building semantic search that actually works

Semantic search trades higher infrastructure costs for better user experience when your queries are naturally expressed questions rather than keyword combinations. Most teams start with hybrid search, combining BM25 precision with vector semantic understanding, then expand based on measured improvements.

Redis handles vector search and semantic caching alongside your operational data with sub-millisecond performance for general operations and 200ms median latency at billion-scale, eliminating separate vector databases and complex integration patterns.

Try Redis free to experiment with vector search on your actual data, or talk to our team about architecting semantic search for your SaaS workload.