Java · Apache Kafka · Enterprise Search · Modern AI

Next-Generation Search Architecture & In-VPC Hybrid Retrieval

Architecting high-throughput Java microservices, real-time Apache Kafka event streaming, and modern Enterprise Search with Agentic AI — featuring Anvi Search, an in-VPC hybrid retrieval engine engineered for seamless Oracle Endeca and Lucidworks Fusion migrations.

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ANVI CORE

Hybrid Vector Retrieval (RRF)

Concurrent lexical BM25 precision and 384-dim dense vector embeddings fused via calibrated Reciprocal Rank Fusion, with opt-in neural cross-encoder reranking in sub-12ms.

KAFKA STREAMING

Apache Kafka Event-Driven CDC

Sub-20ms real-time propagation for price drops, stock mutations, and omnichannel store BOPIS inventory into zero-lock in-memory overlays, replacing 4-hour batch crawlers.

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MIGRATION PARITY

Endeca & Lucidworks Fusion Parity

100% automated drop-in parity for Endeca Experience Manager cartridges, dimension precedence trees, and Lucidworks Fusion query pipeline stages with zero rule rewrites.

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IN-VPC & MCP

100% In-VPC Sovereignty & Agentic AI

Deploy in your own private cloud with $0 SaaS query fees, zero customer data leakage, and a native Model Context Protocol (MCP) server for autonomous AI shopping agents.

OPEN ENGINEERING LAB · HOBBY & LEARNING PROJECT

Anvi Search: The Drop-In Endeca Migration Instrument

Anvi Search is enterprise commerce search built as an Endeca migration instrument — delivering hybrid vector retrieval, a visual merchandiser workbench, and 100% in-VPC data sovereignty.

An open engineering research platform built as a hobby, passion project, and hands-on learning laboratory to explore how modern hybrid vector retrieval, an explainable merchandiser workbench, and 100% in-VPC data sovereignty can solve the Endeca migration challenge from first principles. Built purely for learning and technical exploration — not for commercial sale.

PATH 1: STAY ON ENDECAHIGH RISK
High Risk & EOL Trap

Expensive sustaining support, aging MDEX hardware failure risk, zero vector search, and no real-time price/stock streaming.

PATH 2: MOVE TO SAASCOSTLY LOCK-IN
Costly Cloud Lock-In

$100k-$300k+/yr recurring fees. Customer queries and catalog margins leave your VPC. All merchandising rules must be rewritten.

PATH 3: ANVI SEARCHTHE MODERN STANDARD
Drop-In Parity + Modern RAG

100% rule & cartridge parity. Sub-12ms Hybrid BM25 + Vector. Wagtail visual workbench. 100% In-VPC with zero SaaS query fees.

1,081Automated Layering & Parity Tests
0.569nDCG@10 (Amazon ESCI Benchmark)
100%In-VPC & On-Prem Data Sovereignty
< 12msHybrid RRF Retrieval Latency
INTERACTIVE ARCHITECTURE LAB

Interactive Search Architecture Console

Select an architectural subsystem below to test real-world queries, inspect latency budgets, or compare enterprise engines:

✦ Active View:Compare BM25 lexical precision vs. dense vector embeddings vs. application-layer RRF hybrid fusion.
NLP Query Understanding & Automated Facet Binding:Intent: B2B Product Specification Lookup
Category:Commercial Cooking Equipment
Material:Stainless Steel
Capacity:50 lbs
Power:Gas / Electric
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Lexical Stream (BM25)

Exact keyword & SKU token matching
Score: 18.42
Vulcan 1ER50D 50 lb. Stainless Steel Deep Fryer
Exact token matches for 'stainless', 'deep fryer', and '50 lb'
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Vector Stream (pgvector / k-NN)

384-dim (e5-small) dense semantic embeddings
Cosine: 0.884
Heavy-Duty Heavy Oil Restaurant Frying Unit
High semantic closeness to commercial frying concept
Evaluation Engine View:

Ranked via Reciprocal Rank Fusion (RRF) + Merchandising Layer

Execution Latency: 11.4 ms
#1
Vulcan 1ER50D 50 lb. Stainless Steel Floor Fryer
BM25 Score: 18.42·Vector Sim: 0.862·Combined RRF: 0.0328In-Stock & High Margin Boost (+15%)
#2
Pitco 45C+S Natural Gas 50 lb. Stainless Steel Fryer
BM25 Score: 17.15·Vector Sim: 0.871·Combined RRF: 0.0315
#3
Frymaster GF14 40-50 lb. Stainless Steel Commercial Fryer
BM25 Score: 16.8·Vector Sim: 0.855·Combined RRF: 0.0298

Search Architecture Knowledge Base

Browse 114 in-depth technical guides covering enterprise search internals, indexing pipelines, relevance algorithms, high-throughput architectures, and Endeca migration.

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