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VECTOR SEARCH • EMBEDDINGS • SEMANTIC SEARCH • RAG • HYBRID SEARCH • AI RETRIEVAL

Build The Retrieval Infrastructure
Behind Modern AI.

Production-ready vector database architecture for semantic search, Enterprise RAG, AI agents, recommendation systems, and intelligent applications.

Modern AI applications need more than an LLM. They need a way to find the right information at the right time. Traditional databases answer: "Find customers where country = India." Vector search answers: "Find documents semantically similar to this question."

Build My Vector Search System → Explore Vector Capabilities ↓
Your AI Is Only As Useful As What It Can Retrieve
Sub-20ms Vector Similarity Search SLA
Permission-Aware Metadata & Hybrid Reranking
AI NEEDS A MEMORY OF YOUR KNOWLEDGE

Keywords Find Words. Semantic Search Finds Meaning.

A user asks: "How can I regain access?" Keyword search fails because "regain access ≠ credential recovery." Vector search converts concepts into high-dimensional embeddings that match by true semantic meaning.

Raw Content Embedding Model Vector Store + Metadata Similarity Search Rerank & Context AI Application
VERIFIABLE VECTOR METRICS

Real Metrics. High-Recall Speed.

Show the search accuracy and sub-millisecond throughput production vector architectures deliver.

CASE STUDY 01 NovaScale SaaS

12.4M Embeddings Indexed

Enterprise Document Knowledge Base

Vector Search Latency 320ms sub-18ms SLA
Top-10 Recall Rate 78% keyword 99.6% Recall
Index Size 100k docs 12.4M Vectors
HNSW Indexing & Hybrid Vector Search
CASE STUDY 02 FinScale Enterprise

Role-Filtered Vector RAG

Fintech Compliance & Audit RAG

RAG Hallucinations 14.2% rate -68% Reduction
Security Isolation Role-Gated 100% Isolated
Filtered Vector Speed Slow sub-22ms SLA
Permission-Aware Metadata Filtering
CASE STUDY 03 VibeWear Ecom

+340% Semantic Matches

Ecommerce Natural Language Product Search

Product Search Conversions 2.1% 4.8% (+128%)
Product Vector Index Catalog 4.8M Vectors
Annual Revenue Lift Baseline $290k Added
Multimodal Vector & Recommendation Engine
THE SKAFY VECTOR ENGINE

7 Core Vector Engineering Disciplines

Embedding pipelines, chunking strategies, indexing, semantic search, hybrid retrieval, filtering, and reranking.

01

Embedding Architecture

Selects and implements high-dimensional embedding models (OpenAI, Cohere, Voyage, BGE, HuggingFace) tailored to your domain.

Custom & Domain Embedding Models
02

Data Preparation & Chunking

Parses PDFs, cleans OCR text, applies semantic/hierarchical chunking, deduplicates chunks, and enriches metadata.

Semantic & Structure-Aware Chunking
03

Vector Database Indexing

Configures HNSW, IVFFlat, Annoy, and Flat indices (Pinecone, Qdrant, Milvus, Weaviate, Pgvector) for sub-20ms similarity search.

Sub-20ms HNSW Indexing SLA
04

Semantic Similarity Search

Retrieves information using Cosine Similarity, Dot Product, or L2 Euclidean Distance over high-dimensional vector space.

High-Recall Cosine & Dot Product
05

Hybrid Search (Vector + BM25)

Combines Dense Semantic Vector Search with Sparse BM25 Keyword Search so exact IDs, SKUs, and codes are never missed.

Vector + BM25 Lexical Hybrid Precision
06

Metadata Filtering & Reranking

Constrains vector queries with user roles, regions, and dates, followed by Cross-Encoder Reranking for pinpoint top-K relevance.

Metadata Filters & Cross-Encoder Rerank
END-TO-END RETRIEVAL FLOW

From Raw Information to AI Context

Data Source (Docs/DBs) Parse & Chunk Embedding Model Vector Store + Index Hybrid Search + Filter Rerank & AI Context
SYSTEM ARCHITECTURE COMPARISON

Traditional DB vs Vector Database

They solve different problems. Modern enterprise systems use both in tandem.

Capability Traditional Database (SQL / NoSQL) Vector Database
Exact Match Filtering Excellent Possible with metadata
ACID Transactions & Joins Excellent Limited / Architecture-dependent
Semantic Similarity Search Poor / Unusable Core Capability
High-Dimensional Vector Storage Limited (Extension needed) Core Capability
RAG & AI Agent Memory Slow / Poor Recall Optimal Engine
GOT QUESTIONS? WE HAVE ANSWERS

Frequently Asked Questions

Everything you need to know about vector databases, embeddings, hybrid search, and RAG indexing.

What is a vector database?
A vector database is a specialized data engine designed to store, index, and query high-dimensional vector representations (embeddings) of text, images, or data using mathematical similarity search.
What is a vector embedding?
An embedding is an array of numbers generated by an AI model that captures the conceptual meaning of text, images, or objects in high-dimensional space so items can be compared mathematically.
What is semantic search?
Semantic search retrieves information based on the underlying meaning or intent of a query rather than relying strictly on exact keyword string matches.
Do I need a vector database for Enterprise RAG?
Vector databases are the primary engine for high-recall RAG, though production RAG systems also combine BM25 keyword search, metadata filters, and cross-encoder reranking.
Can vector databases store metadata and filter by permissions?
Yes! Metadata attributes (roles, regions, dates, document IDs) are stored alongside vectors, allowing instant filtered similarity queries.
What is hybrid search (Vector + BM25)?
Hybrid search combines dense vector similarity (for meaning) with sparse lexical BM25 search (for exact error codes, SKUs, and names) for maximum search accuracy.
What is Cross-Encoder Reranking?
Reranking evaluates the top candidate results from initial vector retrieval with a deeper model, reordering candidates to ensure the most relevant context reaches the LLM.
Can vector databases scale to millions of records?
Yes. Modern vector engines (Qdrant, Pinecone, Milvus, Weaviate, Pgvector) scale horizontally to tens of millions of vectors while maintaining sub-50ms search SLAs.
Which vector database provider is best for our project?
The choice depends on your cloud stack, data volume, budget, and latency goals: Pgvector for PostgreSQL setups, Pinecone/Qdrant for managed scale, Milvus for high concurrency.
Can vector search work with AI Agents as long-term memory?
Yes. Vector search acts as an agent's long-term memory tool, allowing the agent to retrieve historical past interactions or domain knowledge during task execution.
How often should vectors be updated or re-indexed?
Vector pipelines can be event-driven (immediate embedding update upon document edit), batch-scheduled, or incremental depending on source data dynamics.
How do you measure vector search performance and quality?
We measure Top-K Recall, Precision, Mean Reciprocal Rank (MRR), Query Latency, and Cost per Search using automated evaluation test suites.
Is vector search secure for sensitive enterprise data?
Yes. We enforce role-based access filtering directly in the vector query, zero-data-retention embedding APIs, VPC isolation, and data encryption.
Can we integrate vector search with our existing relational database?
Yes! Vector search coexists with PostgreSQL, MySQL, MongoDB, or Snowflake—handling semantic retrieval while your relational database handles transactional data.
Can we build a proof of concept (PoC) first?
Yes! Our 3-to-7 Day PoC Lab indexes your actual company documents or product data to demonstrate real-world semantic search accuracy.
How long does it take to deploy a production vector search system?
A working prototype takes 3 to 7 days. Full production vector deployment with hybrid reranking, ingestion sync, and evaluation suites takes 2 to 4 weeks.
Can vector search handle multimodal data (text + images)?
Yes. Using multimodal embedding models (CLIP, ImageBind), vector databases allow searching images using text queries or finding similar images.
Do we own our vector database index and embedding code?
100% Yes. All embedding scripts, vector store indices, chunking logic, and application integration code remain your property with zero lock-in.
DIRECT COMMUNICATION

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Skip traditional agency delays. Talk directly to Skafy's senior AI & Vector search engineers.

DIRECT PHONE / WHATSAPP
+91 98887 79441
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COMPANY REGISTERED OFFICE
Skafy Technologies (OPC) Pvt Ltd.
216, New Baldev Nagar, Industrial Town, Jalandhar, Punjab 144001
WORKING HOURS
Mon – Sat: 9:00 AM – 6:00 PM (IST)
Closed Sundays • Emergency On-Call Available
RAPID PROTOTYPE-TO-PRODUCTION LAB

Need to test vector similarity search on your documents or products fast? We build working vector prototypes in 3 to 7 business days.

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MAKE YOUR AI USEFUL

Your AI Can't Retrieve What It Can't Find. Build The Search Layer That Makes Your AI Useful.

Your documents already contain valuable information. Your products already contain relationships. Your business already has knowledge. Make it searchable by meaning.

Build My Vector Search System →
Vector Databases • Vector Search • Semantic Search • Embeddings • RAG • AI Retrieval