Canonical: https://nexuri.de/en/services/semantic-search
Language: en

# Search that understands what you mean.

[Discuss my search use case](https://nexuri.de/en/contact)

[07]

Service — Embeddings & Semantic Search

Embeddings turn documents, tickets and product data into vectors that capture meaning, not just keywords. We build semantic search, hybrid retrieval and RAG pipelines on top of them, so your team and your customers find the right answer even when they use different words.

## From raw documents to relevant results.

### From raw documents to relevant results.

What we build

01

Semantic & hybrid search

Search across your documents, knowledge base or product catalogue that combines vector similarity with classic keyword ranking (BM25), so exact terms and meaning both count.

02

RAG pipelines

Retrieval-Augmented Generation on your own content: chunking, embedding, retrieval and reranking, with answers that cite their sources.

03

Vector database setup

Selection, schema and operation of pgvector, Qdrant or a managed alternative, including metadata filters, access rights and EU hosting.

04

Retrieval evaluation

Test sets of real questions, metrics such as recall@k and MRR, and regression tests, so changes to models or chunking are measured instead of guessed.

Stack

## Open, portable and EU-ready.

Python

FastAPI

pgvector

PostgreSQL

Qdrant

Elasticsearch

OpenSearch

BM25

Sentence Transformers

Hugging Face

OpenAI Embeddings

Cohere Rerank

LlamaIndex

LangChain

Ragas

Langfuse

[Discuss my search use case](https://nexuri.de/en/contact)

## Every change is checked against real questions.

Relevance you can measure

Search quality is not a feeling. We keep an evaluation set of real queries and track whether the right passage appears in the top results. Model upgrades, new chunking strategies or filters are only rolled out when they improve that score.

### Measured relevance, step by step.

How we work

[Build]

[01]

Questions & content audit

We collect real search questions and review the sources: formats, quality, duplicates and permissions.

[02]

Baseline & evaluation set

A labelled test set and a simple baseline define what “better” means before we optimise.

[Deliver]

[03]

Pipeline & index

Chunking, embedding, hybrid retrieval and reranking are built and compared against the baseline.

[04]

Operate & improve

Monitoring, re-indexing and feedback loops keep results relevant as your content grows.

Further reading

## Go deeper on this topic.

[### Guide: an AI knowledge assistant with sources RAG, access rights and the cost of an internal assistant.](https://nexuri.de/en/guides/knowledge-assistant) [### RAG Pipeline Development: Lessons from Production Article: chunking, retrieval and evaluation from real projects.](https://nexuri.de/en/blog/rag-pipeline-development) [### GraphRAG vs RAG: When Graphs Actually Win Article: when a knowledge graph is worth adding to vector search.](https://nexuri.de/en/blog/graphrag-vs-rag) [### Private AI Self-hosted LLMs on your infrastructure or in the EU.](https://nexuri.de/en/services/private-ai)

FAQ

## Embeddings, explained plainly.

An embedding is a list of numbers (a vector) that represents the meaning of a text, image or record. Texts with similar meaning end up close to each other, which lets software find relevant content even when the wording differs.

Keyword search matches the exact words in a query. Semantic search compares meaning via embeddings. In practice, hybrid search that combines both usually gives the most reliable results, especially for product names, codes and technical terms.

Not always. For many projects, pgvector inside an existing PostgreSQL database is enough. A dedicated engine such as Qdrant becomes relevant with very large indexes, demanding filter requirements or high query volumes.

Yes, with a suitable multilingual embedding model. We test candidate models on your own German and English documents before choosing one, because quality differs noticeably between languages and domains.

RAG uses embedding-based retrieval to find relevant passages and then lets a language model answer based on them. Good retrieval is the foundation: if the right passage is not found, the answer cannot be grounded in it.

Yes. Permissions are stored as metadata and applied as filters during retrieval, so a user only receives results from documents they are allowed to see.

## Want search that finds the right answer?

Bring a few real questions your team or customers ask. We show you where semantic search would help and how we would measure it.

[Discuss my search use case](https://nexuri.de/en/contact)

Get in Touch

## Book an intro call.

Tell us about your project — we'll get back within 24 hours with a tailored proposal. No fluff, no boilerplate.

[info@nexuri.de](mailto:info@nexuri.de) [0170 4667236](tel:+491704667236)
Bavaria · Germany

Pick a slot that suits you — or use the form. We reply within 24 hours on working days.

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