Hybrid Search Implementation Services for Your Business
Hybrid search implementation blends semantic vector search with keyword search, so a query for a product code and a query for a concept both work. Omeiro combines dense embeddings from OpenAI or Voyage with BM25, merged by Reciprocal Rank Fusion.
How hybrid search implementation works, step by step
A made-up example of one run, from start to finish. Each step is logged so you can see what happened.
- Your documents are loaded
Files are split, indexed and kept in your own storage.
- A question comes in
The system finds the parts of your documents that match.
- The model answers from them
Answers point back to the source so people can check.
- People review the edge cases
We agree what happens when no good answer is found.
Sample Made-up names, data and steps to show how a run is logged. Your steps, tools and rules follow your business.
What you get with hybrid search implementation
Production RAG pipelines combining dense embedding vectors (OpenAI/Voyage) with sparse lexical keywords (BM25) via Reciprocal Rank Fusion (RRF) for unbeatable retrieval precision.
Basic Package
Hybrid dense; BM25 search with RRF over up to 500 docs, API. 5-day delivery, 1 round + 30-day fixes.
Standard Package
Up to 5,000 docs, metadata filters, retrieval eval report, docs. 9-day delivery, 1 round + 30-day fixes.
Premium Package
Up to 50,000 docs, re-ranker, chat UI, deployment, 30 days support. 14-day delivery, 1 round + 60-day fixes.
Clear brief first
We confirm what you need before work starts, so the scope is clear on both sides.
Revisions listed
Each package shows its revision rounds. A round is one list of changes sent together.
Yours once paid
Copyright of the final work passes to you once the order is paid in full.
Third-party tools, API usage and hosting are billed to your own accounts, so the setup stays with your business.
How our hybrid search implementation works
Prepare the corpus
Documents are cleaned and split into passages.
Index twice
Each passage is indexed for vectors and for keywords.
Fuse the results
RRF merges the two ranked lists.
Evaluate
Test questions show how much retrieval improved.
Hybrid search implementation: tuning the balance between meaning and keywords
The right mix depends on how people search. Users who type product codes, error messages or legal citations lean on exact matching, while those who ask in natural language lean on meaning. A good hybrid search implementation measures both with your real queries before choosing weights. Collect a few dozen genuine searches, with the documents that should come back, so tuning rests on evidence rather than opinion.
Chunking and metadata quietly decide quality. Passages that are too long dilute meaning, passages that are too short lose context, and missing metadata such as dates or document type removes useful filters. Hybrid search implementation also needs a plan for fresh content, so new documents are indexed both ways without manual work. Tell us your document formats, update frequency and the filters users expect, and the evaluation report on Standard shows where results still fall short.
In short
- Delivery in 5 to 14 days, by package.
- Fixed price, shown before you order.
- Teams whose RAG app misses exact names, codes or abbreviations.
- Copyright yours after full payment.
Hybrid Search Implementation: common questions
How much does hybrid search implementation cost?
Prices are fixed and shown by package on our Hybrid Search Implementation pricing page, so you see the full price before you order. You can also ask for a custom quote.
How long does vector and bm25 search development take?
Delivery depends on the package: Basic 5-day delivery; Standard 9-day delivery; Premium 14-day delivery.
What is included in the Basic Package?
The Basic Package includes: Hybrid dense; BM25 search with RRF over up to 500 docs, API. Higher packages add more scope, revisions and support.
Can I get a custom RAG search optimization package?
Yes. If none of the packages fits, use the custom package form on our pricing page to describe what you need, and our team sends you a fixed price.
What is BM25?
A classic keyword ranking method that scores documents by how often and how rarely query terms appear.
What is RRF?
A way of merging ranked lists by position, so results ranked high in both lists rise.
Which embeddings do you use?
OpenAI or Voyage.
How do I know it is better?
Standard includes a retrieval evaluation report.
When should I use hybrid search instead of vector search alone?
When users search for exact terms such as part numbers, names or error codes, which pure vector search can miss. If queries are almost entirely natural language, the gain may be smaller, and a test on your data will show it.
Who owns the final work?
Copyright of the final work passes to you once the order is paid in full. Paid fonts, stock files or licences, if used, are licensed in your name.
Ready to start your hybrid search implementation?
Compare the packages and pick the one that fits. Not sure which? Write to us and we will help you choose.