Services

Search and AI services for production-grade systems. Elasticsearch + AI.

Two practices under one roof. Thirteen years of deep Elasticsearch expertise — architecture, migration, cluster operations, performance tuning, custom search APIs. Plus a modern AI search practice built on hybrid retrieval, RAG, semantic search, LLM integration, and the Smart Search Tools AI agentic relevance framework. We help teams ship production search and AI systems that actually work.

13+

years Elasticsearch

40+

ES projects shipped

200+

node clusters operated

$700k

license savings delivered

practices · ES + AI

01 — Elasticsearch Services

Search that works in production. At enterprise scale.

Our Elasticsearch practice has shipped search systems for streaming platforms, e-commerce, retail, and enterprise content systems. From 200+ node clusters handling terabytes per day to patented Core Search APIs with relevance GUIs, the work is operational, not theoretical.

Enterprise Search Architecture

Hybrid retrieval design from scratch or repair of a struggling system. Query analysis, mapping and analyzer review, ranking logic, evaluation suites tied to measurable outcomes.

architecturemappingsanalyzersevaluation

Cluster Migration

Endeca → Elasticsearch. On-prem → cloud. Elasticsearch ↔ OpenSearch. License-shopping with full cost modeling. Includes data reindex strategy and zero-downtime cutover plans.

endecaopensearchmigration

Performance & Cost Review

Cluster sizing, shard strategy, query tuning, payload optimization, cache layer design. Track record: 50–60% latency cuts, $500k–$700k in license savings on real engagements.

tuningcostsizing

Custom Search Solutions

Patented Core Search APIs, Relevance GUIs that let business users tune ranking without backend code, .NET Smart Search APIs. Track record: $17M pre-launch sales, 70% conversion lift, 40% relevance gains.

patented apirelevance gui.net

Cluster Health Check

A short structured engagement. Architecture review, configuration audit, performance bottleneck identification, monitoring recommendations, prioritized fix list with effort estimates.

auditmonitoringfixes

Embedded Search Advisor

A senior search engineer embedded with your team for the duration of a build. Pair with your engineers, write code, run reviews, transfer expertise. Engagements typically 1–6 months.

embeddedsenior engineer

02 — AI Search Services

Retrieval architecture for the AI era.

Modern search isn't just a query box. RAG pipelines need grounded answers with citations. Recommendation engines need LTR. Customer-facing AI needs weak-evidence detection so it doesn't hallucinate. Our AI search practice covers the full retrieval stack — from embedding model selection to agentic relevance frameworks running in production.

Smart Search Tools AI

Weblink's agentic relevance framework. Three autonomous loops, Loop C weak-evidence detection, built-in LTR in the recommendation engine, counterfactual query testing. Deployed locally, on-prem, or SaaS.

agenticltrloop c

RAG & Hybrid Retrieval

BM25 + dense vector + Reciprocal Rank Fusion + cross-encoder reranking + RAG with grounded citations and confidence scoring. The full retrieval pipeline, not just a vector database lookup.

raghybridrrfreranking

Semantic Search Implementation

Vector search architecture, embedding model selection (OpenAI, Cohere, open-source), HNSW vs IVF index choice, hybrid ranking strategy. We pick what fits your data and latency budget.

embeddingshnswsemantic

LLM Integration

Context-aware retrieval, agentic flows with tool calling, prompt engineering for production, evaluation harnesses that catch regressions, permission-aware retrieval with synced ACL data.

llmagentsevals

AI Use Case Development

Industry-specific AI search solutions: e-commerce product discovery, enterprise knowledge bases, customer support copilots, compliance search. From discovery through pilot through production.

use casespilotsindustry

AI Performance Optimization

HNSW tuning (M, efConstruction, ef), embedding caching strategy, payload reduction for vector responses, latency budgeting across the retrieval pipeline, cost optimization for embedding APIs.

vector tuningcachinglatency

Smart Search Tools AI vs. Elastic Native

What the framework gives you that stock Elasticsearch doesn't.

Elastic ships a great search engine. It doesn't ship an opinionated agentic relevance framework. Smart Search Tools AI fills that gap — sitting on top of Elasticsearch or OpenSearch and giving your team a repeatable way to test, tune, and improve retrieval.

Smart Search Tools AI

Three autonomous relevance loops (continuous eval, LTR signal ingestion, weak-evidence detection)
Loop C — counterfactual query testing when evidence is thin
Built-in LTR inside the recommendation engine, not bolted on
Permission-aware RAG with ACL data synced to the retrieval index
Deploys locally, on-prem, or as SaaS — your choice
Consulting build + container retainer model, not a SaaS subscription
Evaluation test suites delivered with the system, not as an extra

Elastic Native

No agentic relevance loops out of the box
No counterfactual query testing
LTR plugin exists but is not wired into recommendations by default
ACL syncing is a separate engineering project
Cloud-only for ESRE features; on-prem capabilities limited
License costs scale with data and feature tier
Evaluation tooling is bring-your-own

Search and AI services for production-grade systems. Elasticsearch + AI.

Search that works in production. At enterprise scale.

Retrieval architecture for the AI era.

What the framework gives you that stock Elasticsearch doesn't.

Two practices. One conversation.