Search Strategy & Architecture
We look at your content, users and scale, then design the search architecture and pick the database and models that fit.
Vector Database & Semantic Search Services
Search that understands what people mean, not just the words they type
We design and build vector search for your website, apps and internal knowledge: embeddings, the right vector database, hybrid search and reranking, tuned on your own data. It's also the retrieval layer that makes RAG assistants accurate.
my laptop won't turn on after the update
Keyword search
0 results. Try different keywords.
Semantic search
Troubleshooting boot failures after a firmware update, plus 2 related guides, found by meaning even though they never say "won't turn on".
Semantic search
What Are Vector Databases and Semantic Search?
Semantic search turns text, images and other content into embeddings: lists of numbers that capture meaning. Content with similar meaning ends up close together, so a search can find the right answer even when it uses different words.
A vector database stores those embeddings and finds the closest matches in milliseconds, across millions of items, with filters for things like date, category or user permissions.
Where it makes a difference
- Customers find products and help articles on the first try
- Staff search every document, ticket and wiki in one place
- RAG assistants retrieve the right passages, so answers are accurate
- Similar items, duplicates and anomalies are found automatically
What we offer
Our Vector Database & Semantic Search Services
From choosing an embedding model to tuning relevance in production, we build every part of your search stack.
Vector Database Setup
pgvector, Pinecone, Qdrant, Weaviate, Milvus or OpenSearch, set up with the right indexes, filters and backups for your workload.
Embeddings & Data Pipelines
Content is cleaned, chunked and embedded, and kept in sync as documents, products or records change.
Learn moreHybrid Search & Reranking
Semantic and keyword search combined, then reranked, so exact terms like product codes and meaning-based matches both work.
Retrieval for RAG & Agents
The retrieval layer behind AI assistants and agents, exposed through APIs or MCP, with permission filters so users only see what they're allowed to.
Learn moreRelevance Tuning & Monitoring
Test sets from real queries, relevance metrics and dashboards, so search quality improves and stays high.
Learn moreChoosing the approach
Keyword, Semantic or Hybrid Search?
Each approach has strengths. For most business search, combining them gives the best results, and that's where we usually start.
| Keyword Search | Semantic (Vector) Search | Our focusHybrid Search | |
|---|---|---|---|
| How it matches | Exact words and spellings | Meaning, using embeddings | Both, then reranked for relevance |
| Great at | Product codes, names and exact phrases | Natural questions and different wording | Real-world searches that mix both |
| Struggles with | Synonyms and questions in plain language | Exact codes, IDs and rare terms | Needs more tuning to set up well |
Use cases
What You Can Build With Vector Search
Choosing a database
How We Choose the Right Vector Database
There's no single best vector database. We recommend one based on four practical questions, and often the answer is the database you already run.
- 01
Scale & speed
How many items, how many searches per second and how fast results must return.
- 02
Filtering & hybrid needs
Whether you need rich metadata filters, keyword search and permissions alongside vector search.
- 03
Where it runs
Managed cloud service, your own servers, or an extension to the Postgres or search engine you already have.
- 04
Total cost
Storage, compute and team effort at your expected scale, including vector compression (quantization) that can cut memory costs sharply.
Process
How We Build Your Search
- 1
Discovery & Test Queries
We collect real searches and the results people expect, which becomes the test set for everything that follows.
- 2
Embedding Model Selection
We compare embedding models on your content and languages, balancing accuracy, speed and cost.
- 3
Database & Indexing
We set up the vector database, indexes and metadata filters, and load your content through automated pipelines.
- 4
Hybrid Search & Reranking
Keyword and semantic results are combined and reranked, then tuned against the test set.
- 5
Launch & Monitoring
We go live, track relevance and latency, and keep improving from real search behavior.
Technology
Vector Search Tools We Use
We work with the leading vector databases and embedding models, and fit around the stack you already have.
- Vector Databases
- pgvectorPineconeQdrantWeaviateMilvusChroma
- Search Engines
- ElasticsearchOpenSearchAzure AI SearchVertex AI Vector Search
- Embeddings & Rerankers
- OpenAI EmbeddingsCohere Embed & RerankVoyage AIOpen-source (BGE, E5)
- Frameworks
- LlamaIndexLangChainHaystack
FAQ
Vector Database & Semantic Search FAQs
What is a vector database?
A vector database stores embeddings, which are numeric representations of the meaning of text, images or other data, and quickly finds the items most similar to a query. It's the foundation of semantic search, recommendations and RAG.
What's the difference between semantic search and keyword search?
Keyword search matches the exact words in a query. Semantic search matches meaning, so "cheap flights" can find "low-cost airfare". Hybrid search combines both, which usually gives the best results.
Do we need a separate vector database?
Not always. If you already use PostgreSQL, Elasticsearch or OpenSearch, their vector features are often enough. A dedicated vector database makes sense at very large scale or when you need advanced filtering and speed.
How does vector search help RAG and AI assistants?
An AI assistant can only answer well if it's given the right information. Vector and hybrid search find the most relevant passages from your data, which the model then uses to write an accurate, cited answer.
Can semantic search work in multiple languages?
Yes. Multilingual embedding models place text with the same meaning close together across languages, so a query in one language can find content written in another.
How do you keep vector search costs down at scale?
We choose right-sized embeddings, compress vectors with quantization, keep only what needs to be searchable in memory and use filters to narrow each search. Together these can cut infrastructure costs substantially without a noticeable drop in quality.
How do you measure search quality?
We build a test set of real queries with the results people expect, and track metrics like recall and ranking quality. Every change to models, chunking or ranking is checked against it before going live.
How long does it take to add semantic search?
A working prototype on your own content usually takes two to four weeks. A production rollout with pipelines, permissions and monitoring typically takes six to ten weeks.
Ready to Make Your Search Understand Your Users?
Send us a few searches that don't work well today. We'll show you what semantic and hybrid search would return instead.
Talk to a Search Expert