AI Semantic SearchDevelopment
Replace keyword-only search with retrieval that finds what people mean, tuned and measured on your own content and your own users.
AI Semantic Search Development: what the work involves
Customers type a handful of words and get nothing, because the product page says sofa and they searched couch. Staff search the intranet for a leave policy and land on a three-year-old circular. Zero-result pages push shoppers to leave, and support teams answer questions that a good search box should have resolved. Synonym lists help a little, then become a chore nobody maintains.
DevKey builds hybrid search that combines classic keyword matching with embeddings, so exact codes and model numbers still work while loose descriptions find the right items. Content is chunked sensibly, enriched with metadata, and indexed in a vector-capable store. A reranking step orders the final results, and filters handle price, category or access rights. We then test relevance with a judged set of real queries and review logs, because search quality is tuned, not declared.
Core features
Hybrid keyword and vector retrieval
Exact matches on SKUs and names are combined with meaning-based matches, so both precise and vague queries return sensible results.
Reranking
A second model reorders the top candidates for relevance, which lifts the best answer without scanning the whole index with an expensive model.
Filters and access control
Results respect categories, availability, language and user permissions, so staff never see documents they are not cleared for.
Multilingual and Roman Urdu queries
Embedding choices and query normalisation are tested on Urdu, English and mixed spellings, since the same word is often typed several ways.
Search analytics
Dashboards show top queries, zero-result searches and clicks, revealing missing content and vocabulary gaps worth fixing.
Relevance test set
A judged list of queries and the pages that should appear lets us compare changes objectively before they reach users.
What we get right before launch
Embeddings are not magic
Meaning-based search can surface plausible but wrong items, especially for exact specifications. Hybrid scoring, metadata filters and testing keep precision up where mistakes are costly.
Index freshness and cost
Prices, stock and documents change daily. We design incremental indexing and decide what refreshes in minutes versus overnight, balancing embedding costs against how stale a result can be.
Vendor and model lock-in
Switching embedding models means re-indexing everything. We keep the pipeline modular, store raw content separately, and note the cost of migrating before you commit to a provider.
Tools and technology
- PostgreSQL with pgvector
- Pinecone
- OpenAI embeddings
- Cohere Rerank
- Python
- FastAPI
- LlamaIndex
- Elasticsearch
- Next.js
Common questions, answered
Do we need to replace our current search?
Not always. We can add a semantic layer beside an existing engine such as Elasticsearch or your platform's built-in search, then compare results. Replacement only makes sense if the old engine blocks needed features.
How do you measure whether search got better?
We assemble real queries, mark which results are good, and compute ranking metrics before and after each change. Live click and no-result data then confirm whether users are finding things.
Can it search images or PDFs?
PDFs yes, after text extraction and sensible chunking. Image search is possible with multimodal embeddings, though quality depends on your photos and the kind of questions people ask.
Will this power an AI chatbot too?
Yes. The same retrieval layer can feed answers to a chat assistant. Doing retrieval well is the harder part, so we build and test search first, then add generation on top.
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Give managers a chat box that answers questions from your real business data, shows its working, and refuses when it cannot be sure.
Recommendation Engine Development
Suggest the next product, article or course to each visitor from what people like them actually did, and prove it helps with a controlled test.
Ready to start your AI Semantic Search Development project?
Tell us what you need and we will come back with a clear scope, timeline and the questions worth answering before any build starts.