Semantic search and RAG.
Built for production.
Vector embeddings, ELSER, and RAG pipeline architecture built on Elastic’s AI capabilities — designed for production AI applications by senior engineers.
AI search engineering on Elastic.
From semantic search setup to full RAG pipeline architecture — we build the AI retrieval layer that makes your LLM applications actually work.
ELSER and dense vector search
We configure ELSER (Elastic Learned Sparse Encoder) and dense vector mappings to enable semantic search that understands meaning, not just keywords.
- ELSER model deployment and index setup
- Dense vector mapping and embedding pipeline
- Semantic query testing and validation
RAG pipeline architecture
We design and build the full retrieval-augmented generation pipeline — from document ingestion and chunking to retrieval ranking and LLM handoff.
- Document chunking and embedding strategy
- Retrieval pipeline design and tuning
- LLM integration with Elastic as retrieval layer
Hybrid search (BM25 + semantic)
We build hybrid search architectures that combine BM25 keyword matching with semantic vector retrieval — giving you the best of both precision and recall.
- Reciprocal rank fusion configuration
- Score normalization and weighting
- Benchmarking and relevance evaluation
From prototype to production.
Most AI search prototypes break under real-world data volumes and query patterns. We design for production from day one.
Scaling vector workloads
Dense vector search puts different pressure on Elasticsearch than traditional keyword search. We tune shard configuration, segment merging, and indexing throughput for vector-heavy workloads.
Evaluation and benchmarking
We build evaluation datasets and benchmarking pipelines so you can measure retrieval quality and catch regressions before they reach production.
Let’s build your AI search layer.
Tell us what you are working on. We will respond within one business day with a clear assessment — no sales pitch.