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13 Years. 40+ Elasticsearch Projects. Elite-Level Retrieval Architecture.

Search, RAG, and agentic retrieval. Built to work in production.

Weblink Technologies designs and repairs enterprise search systems on Elasticsearch, OpenSearch, and Weaviate. We cover hybrid retrieval, RAG, LTR, agentic relevance, and cluster operations for teams that need production-grade results.

Elasticsearch
OpenSearch
Weaviate
Book a search architecture review See case studies
0+
years Elasticsearch
0+
ES projects
0+
node clusters
$700k
license savings
Built on
+ Apache Lucene, HNSW, LTR, RAG
01 — The Problem

Search breaks when retrieval, indexing, and generation are treated as separate projects.

Your users see weak results, stale answers, slow queries, and RAG responses without enough evidence. Your engineering team spends time tuning symptoms rather than fixing the retrieval architecture.

Weblink starts at the retrieval layer. We inspect queries, analyzers, mappings, embeddings, ranking logic, source permissions, cluster sizing, and evaluation data, then give your team a build plan tied to measurable search outcomes.

⚠
Weak relevance
BM25 alone misses semantic intent. Vector alone misses exact terms. Most teams use neither well.
⏱
Slow query paths
Untuned shard counts, missing caches, and oversized payloads push latency past 1,000ms.
🔒
Permission drift
Stale ACL data in the index exposes documents users should not see in search or RAG answers.
📉
RAG without evidence
Generated answers that cite no source content create compliance risk and user distrust.
02 — Architecture

Retrieval architecture as a single pipeline, not a stack of disconnected tools.

Source ingestion through BM25, vector retrieval, hybrid ranking, LTR, RAG answers, citations, and evaluation loops.

Retrieval pipeline
SOURCES → INGEST → BM25 + VECTOR → RRF FUSION → RERANK → LTR → RAG + CITATIONS
03 — Smart Search Tools AI
Agentic Relevance Framework

Three autonomous loops. Built-in LTR. Counterfactual query testing.

Smart Search Tools AI is Weblink's agentic relevance framework for teams that need more than a static search box. It sits on top of Elasticsearch or OpenSearch and gives your team a repeatable way to test, tune, and improve retrieval.

The framework runs three autonomous relevance loops, counterfactual query testing, Loop C for weak-evidence detection, and built-in LTR inside the recommendation engine.

Deployment model
Weblink installs Smart Search Tools AI locally, on-prem, or as SaaS. The engagement is a consulting build with a one-time fee and a monthly container retainer. This is a framework, not a standard software purchase.
Loop A · Continuous Relevance Monitoring
Continuous relevance monitoring against evaluation test suites
Loop B · LTR Signal Ingestion
Click and engagement signal ingestion for LTR model updates
Loop C · Weak-Evidence Detection
Weak-evidence detection that triggers counterfactual query paths
3 Autonomous Loops
vs. none in Elastic
Loop C Detection
counterfactual queries
Built-in LTR
inside recommendation engine
Local / On-Prem / SaaS
flexible deployment
04 — Services

Six services. Built around retrieval outcomes, not deliverables.

01
Enterprise Search Architecture
Hybrid retrieval, LTR, evaluation, governance.
02
RAG and Hybrid Retrieval
BM25 + dense vector + RRF + reranking + RAG with citations.
03
Smart Search Tools AI
Agentic relevance framework with three autonomous loops.
04
Migration
Endeca → Elasticsearch; on-prem → cloud; ES → OpenSearch.
05
Performance Review
Cluster sizing, query tuning, payload optimization, cost reduction.
06
Embedded Advisor
Senior search engineer embedded with your team for the duration of the build.
05 — Case Studies

Production search systems with measurable outcomes.

Streaming Platform
Dish Network / Sling TV
200+ node cluster, $500k+ cost savings, 3x TPS increase
200+
ES nodes
3x
TPS increase
60%
render speed
$500k+
cost savings
Designed and operated a 200+ node Elasticsearch cluster handling several terabytes per day with sub-400ms response time. Delivered 3x TPS increase and 60% rendering speed gain.
High-Load Search
Enterprise Search Platform
400,000+ daily users, $700k license savings, 50% payload reduction
400k+
daily users
5,000
TPS
$700k
license savings
50%
payload reduction
Built a high-load search system supporting 400,000+ daily users and 5,000 TPS. Delivered $700k in license savings, 60% rendering speed gain, and 50% payload reduction.
Retail Search
Walgreens
Patented Core Search API, 40% relevance gain
40%
relevance gain
1
patented API
Built a patented Core Search API with a Relevance GUI that allows query tuning without backend code changes. Delivered a 40% relevance gain.
E-Commerce
Visual Search Platform
$17M pre-launch sales, 70% conversion lift, 85% shorter dev time
$17M
pre-launch sales
70%
conversion lift
11%
order value
85%
shorter dev time
Built Smart Search API and ElasticSI in .NET for a visual product search platform. Delivered $17M in pre-launch sales, 70% conversion lift, and 85% shorter development time.
06 — Process

A predictable engagement from review to production handoff.

01
Discovery
Review queries, mappings, embeddings, cluster, evaluation data. We start with what's actually in production.
02
Design
Retrieval pipeline blueprint with measurable success criteria. No generic recommendations.
03
Build
Implement with your team or embed a senior advisor. Code, tests, and evaluation suites shipped together.
04
Operate
Evaluation loops, LTR updates, performance tuning. Continuous improvement, not a one-time deliverable.
07 — Live Pipeline Demo

Watch the retrieval pipeline run

Select a query and run the full pipeline. Each stage shows exactly what Smart Search Tools AI executes, from BM25 through Loop C to the final RAG answer.

Select query
$search.query("What is our refund policy for enterprise contracts?")
01BM25 Lexical Retrieval
Inverted index scan. Matches exact terms, field boosts, and phrase proximity.
02Dense Vector Retrieval
HNSW ANN search on the embedding index. Retrieves semantically similar chunks.
03RRF Fusion
Reciprocal Rank Fusion merges both candidate sets into a unified ranked list.
04Cross-Encoder Reranking
Top-50 candidates re-scored by a cross-encoder model for precision.
05Loop C — Weak Evidence
Smart Search Tools AI checks confidence. Triggers a counterfactual query if evidence is thin.
06LTR Ranking
Learning to Rank model applies click and engagement signals to adjust final order.
07RAG Answer + Citations
LLM generates a grounded answer with inline source citations and confidence score.

Stop tuning symptoms. Fix retrieval at the architecture level.

We review your cluster, queries, mappings, ranking logic, and evaluation data, then give you a concrete build plan. No generic audit reports.

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