AI Search / Semantic Search
AI Semantic Search Development Services to Enhance Your Applications with Powerful AI Capabilities
Our AI semantic search development services replace broken keyword search with a system that understands what users mean, not just what they typed — so "noise reduction headphones for travel" finds what it should, even when those exact words aren't in the product description.
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The Problem With Keyword Search
Keyword search has one job: find documents that contain the words the user typed. It does that job well. The problem is that users don't search the way documents are written. They use synonyms, describe intent rather than terminology, or abbreviate in ways that don't match indexed text. The result is a search experience that misses relevant results, surfaces irrelevant ones, and forces users to guess the "right" words — or give up.
This is a solved problem in consumer search. For most product catalogs, internal knowledge bases, and support ticket systems, it hasn't been applied yet.
Keyword Search vs. AI Semantic Search
Keyword Search
How it works: Exact or fuzzy match on terms in the query vs. indexed text
Finds synonyms: No — "laptop" and "notebook computer" are different queries
Understands intent: No — searches "cheap flights" and "affordable airfare" return different results
Handles misspellings: Partially, with fuzzy matching configuration
Performance at scale: Fast and predictable
Best for: Exact lookups, structured data, high-precision retrieval where user vocabulary matches document vocabulary
AI Semantic Search
How it works: Embeds query and documents as vectors, retrieves by meaning similarity
Finds synonyms: Yes — semantically similar terms return related results
Understands intent: Yes — concept-level matching, not term-level
Handles misspellings: Yes — meaning-level matching is naturally more robust
Performance at scale: Requires vector index management and tuning
Best for: Natural language queries, diverse user vocabulary, discovery use cases where users don't know exact terminology
Most production systems we build use hybrid search — vector similarity for semantic relevance combined with keyword scoring for precision on exact matches (model numbers, proper nouns, specific codes). Neither alone is as good as both together. This is also the foundation of the retrieval layer in our RAG Engineering & Deployment service.
What We Build
Vector search pipeline — embedding model selection, document chunking strategy, vector index build and management
Hybrid search layer — combined vector + BM25/keyword scoring tuned to your content type and query patterns
Query understanding — query expansion, intent classification, and spell correction upstream of retrieval
Faceted filtering — combine semantic relevance with structured attribute filters (category, price, date, status) without degrading search quality
Re-ranking layer — cross-encoder re-ranking of top-N results for higher precision on the final list shown to users
Search analytics — visibility into zero-result queries, low-engagement results, and search abandonment to drive continuous improvement
A/B testing framework — run controlled experiments on ranking changes before rolling them out to all users
Who This Is For
E-commerce platforms with large product catalogs where keyword search misses relevant products when user vocabulary doesn't match product descriptions
SaaS products with internal documentation or knowledge bases where employees search for answers in natural language
Support teams that need semantic search across ticket history to surface similar past issues and resolutions quickly
Enterprise platforms replacing legacy search (Elasticsearch keyword-only, basic Solr setups) that no longer meets user expectations
Trusted Across 50+ Countries
Codersarts maintains a 4.9/5 client satisfaction rating across hundreds of engagements. Clients highlight the team's technical depth and follow-through on complex infrastructure work — Jing (China) described the team as consistently knowledgeable and thorough, while Tan (Malaysia) pointed to the team's explanations as making a real difference in understanding what was being built and why.
Results
An e-commerce platform with a 200,000+ SKU catalog saw a 22% increase in search-to-purchase conversion after replacing keyword search with a hybrid semantic search system that correctly handles intent-based queries.
A B2B SaaS company reduced support ticket volume by roughly 25% after deploying semantic search across their help documentation — users finding answers themselves instead of opening tickets.
An enterprise platform cut average time-to-resolve for support engineers by roughly 40% after implementing semantic search across 5 years of ticket history, surfacing similar past resolutions in seconds.
(Client names withheld under NDA; case studies available on request.)
Pricing
Starter
Scope: Single content corpus, vector search pipeline, basic hybrid scoring
Price: $8,000–$12,000
Production
Scope: Hybrid search with re-ranking, faceted filtering, query understanding, search analytics
Price: $12,000–$18,000
Enterprise
Scope: Multi-corpus search, A/B testing framework, custom embedding model, full analytics
Price: $18,000–$20,000+
For context: enterprise search platform implementations in the US market typically run $50,000–$200,000+ depending on scope. Our pricing reflects high-quality offshore delivery at a fraction of those rates for equivalent engineering quality.
How We Work
Content and query audit (Week 1) — analyze your content corpus, sample real user queries, and identify the biggest gaps in current search
Build (Weeks 2–4) — vector pipeline, hybrid scoring, query understanding layer
Relevance tuning (Week 5) — evaluate against real queries, tune ranking until results are measurably better than baseline
Launch — deploy with search analytics active from day one to feed continuous improvement
Why Codersarts
As a vector search implementation company, we start from your actual user queries and content characteristics — not a generic embedding model applied to whatever you have. Embedding model selection, chunking strategy, and hybrid scoring weights all get tuned to your specific corpus and use case, because the default settings on any vector database are a starting point, not a production configuration.
Related Services
RAG Engineering & Deployment — semantic search is the retrieval foundation of every RAG system we build
AI Document Intelligence — for enterprises that need to make unstructured document archives searchable before building search on top
LLM Integration & API Orchestration — for products that want to add natural language query interpretation on top of the search layer
AI Strategy & Architecture Audit — if you're unsure whether semantic search, RAG, or a hybrid approach fits your use case
Get Started
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FAQ
What is the difference between semantic search and RAG? Semantic search retrieves relevant documents and surfaces them to the user. RAG takes those retrieved documents and uses them as context for an LLM to generate an answer. Semantic search is the retrieval layer — RAG adds a generation layer on top. They're complementary, and we often build both together.
Which vector database do you use? We're database-agnostic and recommend based on your scale, existing infrastructure, and query patterns — Pinecone, Weaviate, Qdrant, pgvector, and Elasticsearch vector are all in our standard stack. We don't lock you into a specific vendor.
Will this replace our existing Elasticsearch or Solr setup? Not necessarily — many implementations extend existing Elasticsearch setups with vector capabilities rather than replacing them. We assess your current setup in week one and recommend the least-disruption path to meaningful improvement.
How do we know the new search is actually better? We run offline evaluation against a sample of real queries before launch, measuring precision and recall against your existing baseline. We also build an A/B testing framework into the Production and Enterprise tiers so improvement can be measured against real user behavior after launch.
How long does a build take? Starter tier: 2–3 weeks. Production tier: 4–5 weeks. Enterprise tier: 6–8 weeks depending on corpus size and the number of custom components required.