Enterprise Search

Enterprise Search Solutions & Relevance Engineering

Search is the most-used feature in most enterprise applications and the least-engineered. We fix relevance, then keep it measurable.

Discuss your requirement See capabilities

What you get

Relevance engineering
Semantic & vector search
Hybrid search architecture
Indexing pipelines

Overview

Why teams bring us in

When search fails, users do not file a ticket — they leave. Yet most search stacks are installed once, tuned never, and judged by anecdote. Exubers treats relevance as an engineering discipline with baselines, test sets and regression checks.

We have implemented custom tokenisation, entity recognition and synonym mapping tailored to a client domain, fine-tuned Solr configuration, and combined it with advanced NLP pipelines using spaCy to reach a deeper understanding of user queries. Today that work increasingly means hybrid search — keyword precision plus vector recall — behind a single ranking layer.

Capabilities

Inside our Enterprise Search

Every engagement is scoped from this set. We do not sell all of it to everyone — we sell the parts that move your constraint.

Relevance engineering

Analyser and tokenisation design, synonym and stop-word strategy, field boosting, and judgement-list driven tuning with measurable NDCG movement.

Semantic & vector search

Embedding selection, chunking strategy, ANN index design and reranking on pgvector, OpenSearch k-NN, Milvus, Qdrant or Pinecone.

Hybrid search architecture

Fusing BM25 and vector recall with reciprocal rank fusion or learned reranking, so precise queries and vague ones both work.

Indexing pipelines

Connectors, incremental and near-real-time indexing, document enrichment, entity extraction and access-control-aware indexing.

Search analytics

Zero-result tracking, click and conversion attribution, query clustering, and dashboards that tell you which queries are failing this week.

RAG & AI answers

Retrieval layers built for LLM applications — grounded answers, citations, and evaluation harnesses so quality is tested rather than assumed.

How we work

A delivery sequence you can plan around

Baseline

We capture real query logs, build a judgement set with your domain experts, and measure current relevance so improvement is provable.

Model

Schema, analysers, enrichment and embedding strategy designed against your actual content and query mix, not a generic template.

Tune

Iterative ranking work against the judgement set with automated relevance regression tests in CI.

Measure

Search analytics in production: zero-result rate, click-through, refinement rate and downstream conversion.

Zero-resultRate tracked as a first-class product metric

HybridKeyword and semantic recall behind one ranking layer

Regression-testedRelevance changes gated in CI like any other code

Technology

Tools we use in Enterprise Search work

Chosen per engagement against your team's existing skills and constraints, never as a default.

Apache Solr Elasticsearch OpenSearch Vespa pgvector Milvus Qdrant Pinecone spaCy Hugging Face Kafka Airflow

FAQ

Enterprise Search questions, answered

The questions procurement and engineering ask us most often before an engagement starts.

Ask us something else

What is the difference between keyword, semantic and hybrid search?

Keyword search matches the words a user typed and is precise when they know the exact term. Semantic search converts the query and documents into vectors and matches on meaning, which handles paraphrasing and vague queries. Hybrid search runs both and fuses the results, which in practice outperforms either approach alone because real query logs contain both kinds of query.

Should we use Solr, Elasticsearch or a vector database?

Solr and Elasticsearch remain the right foundation for structured, faceted, permission-aware enterprise search at scale. A dedicated vector database earns its place when embedding volume is very large or the workload is primarily semantic retrieval for AI applications. Many of our builds use both, which is why we design the ranking layer first and the storage second.

How do you prove search relevance actually improved?

With a judgement set built from real queries and rated by your domain experts, scored using metrics such as NDCG and mean reciprocal rank. Those scores run in CI, so a relevance regression fails the build the same way a broken test would.

Can enterprise search respect our existing permissions model?

Yes. We implement early-binding or late-binding access control depending on how volatile your permissions are, so a user never sees a document in results that they could not open.

Do you build retrieval layers for AI and RAG applications?

Yes. Retrieval quality is the main determinant of answer quality in a RAG system, and it is the same discipline as relevance engineering: chunking, recall, reranking and evaluation.

How long does an enterprise search engagement take?

A focused relevance improvement on an existing index typically takes four to six weeks. A full platform build including indexing pipelines, hybrid ranking and analytics is usually three to five months.

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