Ajay Agrawal|Java · Kafka · Search Architect
Java · Kafka · Search Architect · Vector RAG · Endeca Migrations

Next-Generation Search Architecture, Hybrid Retrieval & Migration

Enterprise discovery systems across Coveo, Elasticsearch, OpenSearch, Apache Solr, and Oracle Endeca. Powering multi-million SKU catalogs with Vector embeddings, RAG contextual retrieval, NLP query understanding, and Anvi Search.

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Hybrid & Vector Search

Blending BM25 lexical precision with dense vector k-NN embeddings via Reciprocal Rank Fusion (RRF) for zero-loss semantic recall.

RAG & NLP Understanding

Natural language query intent parsing, facet extraction, semantic query expansion, and high-precision context retrieval.

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Endeca Migration Engine

Drop-in parity for Endeca dimensions, cartridge rules, and DGraph queries to seamlessly modernize onto Solr, Elastic, or Anvi Search.

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Relevance & Merchandising

Multi-match modes, dynamic boost & bury, precedence rules, facet hierarchies, and real-time inventory-aware ranking.

LIVE ARCHITECTURE SIMULATOR

Hybrid Retrieval & Reciprocal Rank Fusion in Action

See how Anvi Search blends BM25 lexical precision with dense vector embeddings (pgvector) and NLP query understanding to outperform traditional single-model search engines.

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)

1536-dim 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
VISUAL SEARCH PIPELINE ARCHITECTURE

Interactive End-to-End Query Flow

Click on any architectural block to inspect its latency budget, data inputs/outputs, and underlying technology stack.

⏱ 0.1ms
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1. Query IntakeStorefront / MCP Agent
⏱ 1.2ms
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2. NLU & Intent ParserTokenization, Entities & Synonyms
⬇ (Parallel Dual-Branch Execution)
⏱ 3.5ms
BRANCH A (SPARSE)
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3A. Lexical BM25 CoreExact SKU & Keyword Match
⚡ CONCURRENT
⏱ 4.8ms
BRANCH B (DENSE)
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3B. Dense Vector k-NNSemantic Similarity Graph
⬇ (Reciprocal Rank Fusion Merge)
⏱ 0.6ms
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4. Reciprocal Rank Fusion (RRF)RRF(d) = Σ w / (k + rank) · Calibrated Fusion
⏱ 2.4ms
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5. Neural RerankerCross-Encoder Top-30 Re-scoring
⏱ 0.8ms
⚖️
6. Merchandiser RulesBoost, Bury, Pin & Cartridges
⏱ 1.1ms
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7. Facets & DeliveryMulti-Select Disjunctive Facets < 10ms Total
STAGE INSPECTOR

4. Reciprocal Rank Fusion

Score-Free Ranked Merge
Latency Budget: 0.6ms

Merges lexical and vector candidate lists using calibrated RRF weighting (Lexical 1.0, Vector 0.4, k=60), solving vocabulary mismatch.

📥 Inputs & Triggers:
  • BM25 Candidates
  • Vector k-NN Candidates
📤 Outputs & Artifacts:
  • Unified Top 60 Hybrid Candidates
Engine Technology:
Application-Layer RRFMonotonic Rank Sorter
DATA INGESTION ARCHITECTURE

Three-Lane Multi-Speed Catalog Ingestion

How Anvi Search ingests high-scale catalog data: combining full-fidelity offline batch reindexing with sub-20ms streaming price/stock CDC.

Lane AHourly

Batch Blue-Green Reindex

Hourly / Nightly Baseline

Lane BContinuous

Real-Time CDC Streaming

Continuous (Sub-20ms Freshness)

Lane CGraph-RAG

Dynamic Relations Graph

Graph-RAG Periodic Linker

Lane B: Real-Time CDC Streaming

Continuous (Sub-20ms Freshness)
PRODUCTION TESTED

Captures row-level database changes (price modifications, stock level drops, flash sale flags) and streams them via CDC directly into an in-memory document overlay.

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1. Transactional DB Change (Postgres / Oracle / Mongo CDC)
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2. Kafka / Debezium Stream Event Router
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3. High-Speed In-Memory Price/Stock Cache Mutation
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4. Query-Time Document Attribute Overlay (< 20ms total)
Peak Throughput:10,000 events / sec
Propagation Latency:< 20ms end-to-end
Core Isolation:In-Memory Monotonic Watermark
Rollback Contract:Kafka offset rewind
✦ ARCHITECTURAL COMPARISON

Enterprise Search Platform Comparison

Evaluating capabilities, latency profiles, data sovereignty, and AI readiness across Coveo AI, Elasticsearch, OpenSearch, Apache Solr, Oracle Endeca, and Anvi Search.

Capability & ArchitectureCoveo AIElastic / OpenSearchApache SolrOracle Endeca⚡ Anvi Search
Architecture & Deployment
Retrieval & AI Capabilities
Migration & Merchandising
100% In-VPC Data SovereigntyRuns completely inside private enterprise cloud with zero external telemetry or SaaS leakage.✗ SaaS Only✓ Self-Hosted✓ Self-Hosted✓ On-Premise✓ 100% In-VPC Private
Model Context Protocol (MCP) Agent ReadyNative MCP server allowing AI agents (Claude, OpenAI) to securely ground actions on catalog tools.✗ Closed SaaSRequires Custom API✗ Unsupported✗ Unsupported✓ Built-in MCP Tools
Real-Time Streaming Updates (Kafka CDC)Sub-20ms price, inventory, and SKU availability synchronization without batch rebuilds.Webhook Polling✓ Real-time Index APISoft Commit Buffer✗ Batch Forge (30m+)✓ Sub-20ms Kafka CDC
Hybrid BM25 + Vector RRF RetrievalParallel lexical term precision and dense semantic embeddings merged via Reciprocal Rank Fusion.Proprietary MLELSER / k-NNDense Vector (Solr 9+)✗ Lexical Only✓ Calibrated RRF (k=60)
Contextual Retrieval (Situated Metadata)Prepend document/category situated context to eliminate RAG chunk context collapse.✗ Not SupportedRequires Pipeline✗ Unsupported✗ Unsupported✓ Automated Synthesizer
Multi-Hop Graph-RAG TraversalTraverse cross-document relational entity graphs to retrieve companion specifications.✗ Not Supported✗ Not Native✗ Not Native✗ Unsupported✓ In-Memory Entity Graph
Oracle Endeca Drop-in Parity EngineAutomated translation of Dimension Precedence, Record Filters, and Experience Manager Cartridges.Manual SaaS MappingCustom App CodeCustom Solr PluginsNative MDEX✓ 100% Automated Parity
Explainable Relevance WorkbenchVisual scoring breakdown showing exact BM25, Vector, Precedence, and Boost multipliers per record.Black-Box SaaSExplain API (JSON)Debug Query (XML/JSON)DGraph XML Logs✓ Visual Inspector UI
Disjunctive Faceting (Multi-Select Counts)High-speed calculation of dynamic facets without exponential multi-query Cartesian expansion.✓ SaaS HandledPost-filter AggrsTagged Exclude Filters✓ Columnar DGraph✓ In-Memory Roaring Bitsets
FLAGSHIP SEARCH ENGINE

Anvi Search: Enterprise Commerce Search

Commerce search engineered as a drop-in Endeca migration instrument and high-performance retrieval engine. Features hybrid BM25 + pgvector semantic ranking, explainable merchandising workbench, and 100% in-network deployment with zero external SaaS data leakage.

0.537RRF Fusion Score (ESCI Benchmark)
100%On-Prem / Private VPC Isolation
ZeroVendor Cloud Lock-in
Read Architecture & Benchmark Breakdown →

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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