ON-PREMISE SOLUTIONS SOLUTION

Transform Your
PostgreSQL On-Premise Solutions

Enhance your PostgreSQL workflows with AI-powered on-premise solutions. Get instant insights, automated optimization, and intelligent analytics.

88%
Performance
65%
Cost Reduction
92%
Query Speed
78%
Automation

Key Features for PostgreSQL

Advanced Full-Text Search

Enhance your data operations with advanced full-text search

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Custom Extensions

Enhance your data operations with custom extensions

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JSON/JSONB Operations

Enhance your data operations with json/jsonb operations

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Materialized Views

Enhance your data operations with materialized views

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Table Partitioning

Enhance your data operations with table partitioning

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Parallel Query Processing

Enhance your data operations with parallel query processing

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Real-World Examples

Use Case:

"Create an intelligent search and analytics system"

Solution:

                        
-- Create necessary extensions and indexes
CREATE EXTENSION IF NOT EXISTS pg_trgm;
CREATE EXTENSION IF NOT EXISTS unaccent;

CREATE MATERIALIZED VIEW product_search_index AS
WITH product_details AS (
    SELECT 
        p.product_id,
        p.product_name,
        p.description,
        p.price,
        c.category_name,
        ARRAY_AGG(t.tag_name) as tags,
        AVG(r.rating) as avg_rating,
        COUNT(r.rating) as review_count,
        SUM(s.quantity) as total_sales
    FROM products p
    LEFT JOIN categories c ON p.category_id = c.category_id
    LEFT JOIN product_tags pt ON p.product_id = pt.product_id
    LEFT JOIN tags t ON pt.tag_id = t.tag_id
    LEFT JOIN reviews r ON p.product_id = r.product_id
    LEFT JOIN sales s ON p.product_id = s.product_id
    GROUP BY p.product_id, p.product_name, p.description, p.price, c.category_name
)
SELECT 
    product_id,
    product_name,
    description,
    price,
    category_name,
    tags,
    avg_rating,
    review_count,
    total_sales,
    setweight(to_tsvector('english', product_name), 'A') ||
    setweight(to_tsvector('english', category_name), 'B') ||
    setweight(to_tsvector('english', COALESCE(array_to_string(tags, ' '), '')), 'C') ||
    setweight(to_tsvector('english', description), 'D') as document_vector
FROM product_details;

-- Create GIN index for fast text search
CREATE INDEX idx_product_search ON product_search_index USING gin(document_vector);

-- Intelligent search function
CREATE OR REPLACE FUNCTION search_products(
    search_query TEXT,
    min_price DECIMAL = NULL,
    max_price DECIMAL = NULL,
    min_rating DECIMAL = NULL,
    category_filter TEXT = NULL
) RETURNS TABLE (
    product_id INT,
    product_name TEXT,
    category_name TEXT,
    price DECIMAL,
    avg_rating DECIMAL,
    review_count INT,
    search_rank DECIMAL,
    matched_terms TEXT[]
) AS $$
BEGIN
    RETURN QUERY
    SELECT 
        psi.product_id,
        psi.product_name,
        psi.category_name,
        psi.price,
        psi.avg_rating,
        psi.review_count,
        ts_rank(document_vector, query) * (1 + ln(COALESCE(review_count, 0) + 1)) as search_rank,
        ARRAY(
            SELECT token 
            FROM ts_debug(search_query) 
            WHERE alias = 'word'
        ) as matched_terms
    FROM 
        product_search_index psi,
        to_tsquery('english', regexp_replace(search_query, '\s+', ' & ', 'g')) query
    WHERE 
        document_vector @@ query
        AND (min_price IS NULL OR price >= min_price)
        AND (max_price IS NULL OR price <= max_price)
        AND (min_rating IS NULL OR avg_rating >= min_rating)
        AND (category_filter IS NULL OR category_name = category_filter)
    ORDER BY search_rank DESC
    LIMIT 50;
END;
$$ LANGUAGE plpgsql;

-- Example usage
SELECT * FROM search_products(
    'organic sustainable food',
    min_price := 10,
    max_price := 100,
    min_rating := 4.0,
    category_filter := 'Organic Foods'
);
                    

Explanation:

PostgreSQL advanced search features: • Full-text search with weights • Trigram similarity matching • Custom function creation • Materialized views for performance • Complex ranking algorithm Implementation highlights: 1. Multi-field search vectors 2. Weighted relevance scoring 3. Price and rating filtering 4. Category-based filtering 5. Review count boosting Perfect for: - E-commerce platforms - Content management systems - Product catalogs - Search optimization

Common Use Cases

Data Sovereignty

Optimize your PostgreSQL on-premise solutions with AI-powered automation

Network Isolation

Optimize your PostgreSQL on-premise solutions with AI-powered automation

Custom Security

Optimize your PostgreSQL on-premise solutions with AI-powered automation

Hardware Optimization

Optimize your PostgreSQL on-premise solutions with AI-powered automation

Legacy Integration

Optimize your PostgreSQL on-premise solutions with AI-powered automation

Compliance Management

Optimize your PostgreSQL on-premise solutions with AI-powered automation

Why Choose AI-Powered PostgreSQL?

Complete Control

Full control over your PostgreSQL infrastructure, security, and data.

Data Sovereignty

Keep sensitive data within your physical premises and jurisdiction.

Custom Hardware

Optimize performance with specialized hardware configurations.

Network Isolation

Enhanced security through complete network isolation and custom firewall rules.

Legacy Integration

Seamlessly integrate with existing on-premise systems and workflows.

Compliance Ready

Meet strict regulatory requirements with full infrastructure control.

Easy Integration

Simple Setup

Connect your PostgreSQL instance with just a few clicks

Secure Connection

Enterprise-grade encryption and security measures

Instant Results

Start seeing improvements immediately after integration

Simple, Transparent Pricing

Standard

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  • Single Server Deployment
  • Basic Security Features
  • 8x5 Support
  • Annual Updates
  • Basic Monitoring
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Professional

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  • High-Availability Setup
  • Advanced Security
  • 24x7 Support
  • Quarterly Updates
  • Advanced Monitoring
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Enterprise

Custom
  • Custom Architecture
  • Custom Security
  • 24x7 Premium Support
  • Monthly Updates
  • Custom Monitoring
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Ready to Transform Your PostgreSQL On-Premise Solutions?

Frequently Asked Questions

What are the hardware requirements?

Hardware requirements for PostgreSQL vary based on your scale and performance needs. We'll help you design the optimal configuration.

How is security handled?

You maintain complete control over security with custom firewall rules, network isolation, and access controls.

Can you integrate with existing systems?

Yes, we provide tools and expertise to integrate with your existing infrastructure and legacy systems.

How are updates managed?

You control the update schedule. We provide tested updates and support for deployment within your maintenance windows.