Semantic Search
Embeddings, vector search, the retrieval that finds by meaning, not just keyword.
AI + Data
AI Search & Semantic SearchSearch That Understands the Question.
AI search and semantic search for products that need to find by meaning, not just by keyword — the indexing, the embeddings, the hybrid retrieval, the reranking, the eval suite and the integration with the systems the user already runs.
10+Years Experience
100+Projects Delivered
50+Expert Developers
20+Industries Served
Semantic Search
Hybrid Search
Document Search
Ecommerce Search
Knowledge Search
Evaluation & Tuning
Overview
AI search finds by meaning, not just by keyword
AI search (semantic search) is the work of finding by meaning, not just by keyword. The work is the indexing, the embeddings, the hybrid retrieval, the reranking, the eval suite and the integration with the systems the user already runs. The search is the product, the eval is the safety net.
We build AI search systems with the indexing, the embeddings, the hybrid retrieval, the reranking, the eval suite and the integration as part of the architecture from sprint one. The output is a search that finds the right thing, not a search that returns ten results and hopes the user picks one.
This is the wrong engagement if the corpus is genuinely small (Elasticsearch will do) or if the use case is filtering, not searching.
- Finds by Meaning — Finds by meaning, not just by keyword, with the accuracy the use case needs.
- Hybrid Retrieval — Vector + keyword + metadata, with the reranking the accuracy needs.
- Per-User Access — Per-user access control, with the documents filtered to the user the search is for.
- Evaluated, Not Vibes — A held-out test set, the metrics and the tuning the search needs to find the right thing.
What we deliver
Everything included in our ai search & semantic search
Hybrid Search
Vector + keyword + metadata, with the reranking the accuracy needs.
Document Search
Search across PDFs, docs, knowledge bases, the documents the user needs to find.
Ecommerce Search
Product search, the catalogue, the facets, the recommendations that lift conversion.
Knowledge Search
Search across the company's documents, with the per-user access control.
Evaluation & Tuning
A held-out test set, the metrics, the tuning the search needs to find the right thing.
Our process
A proven process for successful delivery
01
Discover
We audit the corpus, the access control, the queries and the accuracy the use case needs.
02
Plan & Design
We design the indexing, the embeddings, the retrieval and the eval suite.
03
Develop
We build in two-week sprints with the team testing as we go.
04
Deploy
We ship to production with the eval, the access control and the observability live.
05
Optimize & Grow
We read the accuracy, the eval and the team feedback, and ship the next iteration.
Technology
Built with a stack that stays maintainable
Vector Stores
- Pinecone
- Weaviate
- Qdrant
- pgvector
Search
- Elasticsearch
- OpenSearch
- Algolia
- Meilisearch
Embeddings
- OpenAI
- Cohere
- Voyage
- BGE
Reranking
- Cohere Rerank
- BGE Reranker
- Cross-encoder
What you can expect
- Top-3 Accuracy Target
- 90%+Top-3 Accuracy Target
- Retrieval Latency
- <500msRetrieval Latency
- Access Control
- Per-UserAccess Control
- Vector + Keyword
- HybridVector + Keyword
Industries we serve
Delivered for teams in your sector
When the user needs to find by meaning (e.g. "refund policy for international orders" instead of "refund") and the corpus is large enough that keyword search returns too many irrelevant results. For a small corpus, Elasticsearch or a SQL LIKE is enough. We will say so on the call.
RAG uses retrieval to ground a generation. AI search returns the documents, with the user reading the answer. The retrieval is the same (embeddings, hybrid search, reranking), but the use case is different. AI search is the right answer when the user wants the source, not the summary.
Per-user access control is enforced at the retrieval layer, not the prompt layer. The documents are filtered to the user the search is for, the retrieval only sees the documents the user can see, and the results only return the documents the user is allowed to read. The access control is part of the architecture.
A held-out test set with the queries the user actually types, the expected documents, and the metrics (precision, recall, NDCG, MRR). The eval runs on every change, with the regressions caught early. The accuracy is measured against the test set, not a guess.
Related services
More in AI + Data
RAG DevelopmentEnterprise Knowledge AIPredictive AnalyticsRecommendation EnginesComputer VisionDocument Intelligence & OCR
Business software
ERP, CRM, HRMS & custom software we build
Ready to start your ai search & semantic search project?Let’s scope it together.
Tell us the outcome you need. We’ll come back with an approach, a timeline and a written estimate.