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PostgreSQL Development

We design and operate PostgreSQL databases for enterprise software — schema design, replication, and partitioning architected around the domain's actual data integrity and scale requirements, not a generic reference schema.

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  • ACID Transactions
  • High Availability
  • JSONB
  • Replication
  • Enterprise Scale
Overview

Our approach

We design and operate PostgreSQL databases for enterprise software — schema design, replication, and partitioning architected around the domain's actual data integrity and scale requirements, not a generic reference schema.

  • ACID Guarantees That Actually Hold

    Transactions that either fully complete or fully roll back, which matters most in the systems where a partial write is a real business problem.

  • Extensible Where It Matters

    Extensions like pgvector and PostGIS add real capability inside the database instead of forcing a second system alongside it.

  • JSONB Without Giving Up Relational Integrity

    Semi-structured data lives alongside strongly typed relational data in the same database, queried with the same SQL.

  • Battle-Tested at Scale

    A database engineered for decades of production workloads, not a newer option with scale properties still being discovered.

  • Rich Indexing for Real Query Patterns

    B-tree, GIN, GiST, and partial indexes chosen for the actual queries an application runs, not a single default index type.

  • An Ecosystem That Doesn't Lock You In

    Standard SQL and an open license mean the database doesn't become a dependency you're negotiating around later.

What's Included

Everything under one roof

Everything included in this engagement, from architecture to long-term support — one team, one system.

Enterprise Databases

Core transactional databases architected around the domain's actual integrity, concurrency, and audit requirements.

Multi-tenant SaaS

Tenant isolation strategies — shared schema, schema-per-tenant, or database-per-tenant — chosen based on real isolation requirements.

Marketplace Platforms

Relational data models for listings, transactions, and reviews that stay consistent under concurrent writes.

Financial Systems

Transactional integrity and auditability for systems where a data inconsistency is a compliance problem, not just a bug.

Healthcare Platforms

Data models built for the integrity and access control healthcare data requires.

Analytics Platforms

Schemas and indexing strategies designed for the query patterns analytical workloads actually run.

AI Applications

pgvector-backed similarity search combined with relational data in a single database, avoiding a separate vector store where it isn't justified.

Business Automation

Data models that support workflow state, audit trails, and the queries automation logic depends on.

CRM Systems

Relational data models for contacts, accounts, and activity history that stay queryable as data volume grows.

ERP Systems

Complex, highly relational data models for the interconnected processes an ERP system has to represent accurately.
Why Aixo Lab

Why companies choose Aixo Lab

  1. We Design Schemas Around the Domain, Not the ORM

    The schema reflects the business's actual entities and constraints, not whatever an ORM's default conventions generate.

  2. We Treat Indexes as a Deliberate Decision

    Every index is chosen for a real query pattern, because an unnecessary index has a write-performance cost that's easy to forget about.

  3. We Architect Replication and Backups Before They're Needed

    High availability and recovery procedures are designed and tested during the build, not assembled after the first outage.

  4. We Use JSONB Where It's the Right Tool

    Semi-structured data goes in JSONB when the data genuinely varies in shape — not as a way to skip schema design.

  5. We Hand Off a Database Your Team Can Own

    Clear schema documentation and migration history mean your own engineers — or ours, later — can extend this database without archaeology.

Our Capabilities

Our PostgreSQL Capabilities

The specific technical capabilities behind every PostgreSQL engagement — not a generic feature list, the actual engineering surface we work in daily.

Relational Databases

Strongly typed, constraint-enforced schemas that keep data consistent as an application grows in complexity.

Data Modelling

Entities and relationships modeled around the domain's real rules, not around what's easiest to query.

ACID Transactions

Multi-step operations that either fully complete or fully roll back, with isolation levels chosen for the actual concurrency needs.

High Availability

Failover and redundancy architected so a single node failure doesn't take down the database.

Replication

Streaming and logical replication configured for the actual read-scaling and disaster-recovery requirements.

Partitioning

Large tables split by range or list so queries and maintenance operations stay fast as data volume grows.

Performance Optimisation

Query plans analyzed and tuned against real workloads, not optimized against a synthetic benchmark.

Indexing

The right index type for the actual query — B-tree, GIN, GiST, or partial — not a default applied everywhere.

Query Optimisation

Slow queries diagnosed with EXPLAIN ANALYZE and fixed at the query or schema level, not worked around with more hardware.

JSONB

Semi-structured data queried with native JSON operators, indexed with GIN when it needs to be searched efficiently.

Full-text Search

Native text search for applications that need it without the operational overhead of a separate search engine.

Extensions

pgvector, PostGIS, and other extensions integrated deliberately when they solve a real problem inside the database.

Backup Strategies

Automated, tested backups with a defined recovery point objective, not a cron job nobody has verified works.

Scalability

An architecture — read replicas, partitioning, connection pooling — sized to the application's actual growth trajectory.
Process

How we work

The same disciplined process behind every engagement, from the first architecture decision to launch.

  1. 01
    Discovery

    Understand the business problem and its real constraints.

    Output:
    Scope and goals document
    Your involvement:
    Initial workshop
  2. 02
    Product definition

    Translate the problem into concrete product requirements.

    Output:
    Feature spec and priorities
    Your involvement:
    Requirements review
  3. 03
    UX/UI design

    Design user flows and interface before development starts.

    Output:
    Wireframes and design system
    Your involvement:
    Design feedback
  4. 04
    Technical architecture

    Define system structure, data flow, and technology stack.

    Output:
    Architecture document
    Your involvement:
    Technical review (optional)
  5. 05
    Iterative development

    Build in short cycles with visible, regular progress.

    Output:
    Regularly shipped working versions
    Your involvement:
    Sprint review participation
  6. 06
    Quality assurance

    Test functionality, performance, and security before release.

    Output:
    Test results and fixes
    Your involvement:
    Acceptance sign-off
  7. 07
    Launch

    Deploy to production with a rollback plan in place.

    Output:
    Product deployed to production
    Your involvement:
    Launch approval
  8. 08
    Continuous improvement

    Monitor, maintain, and evolve the product after launch.

    Output:
    Maintenance and improvement roadmap
    Your involvement:
    Regular check-in meetings
Technology Stack

Built on a modern, production-grade stack

Every technology here is a deliberate choice, not a default.

PostgreSQL

The relational database underlying every engagement built this way.

Laravel

An enterprise backend framework with strong migration and query-building tooling for PostgreSQL.

Node.js

A backend runtime commonly paired with PostgreSQL through typed query builders and ORMs.

Next.js

A full-stack framework for applications that need both a frontend and a PostgreSQL-backed API layer.

React

The frontend layer most commonly paired with PostgreSQL-backed APIs.

React Native

The mobile client layer that consumes APIs backed by PostgreSQL.

Redis

Caching and session storage for workloads that need speed a database alone can't provide.

Docker

Containerized PostgreSQL instances for consistent environments across development, staging, and production.

AWS

Cloud infrastructure for PostgreSQL deployments that need more control than a managed platform alone provides.

pgvector

Vector similarity search directly inside PostgreSQL, for AI applications that don't need a separate vector database.

Prisma

A type-safe ORM connecting the application to PostgreSQL with full TypeScript inference.

OpenAI

AI model access paired with pgvector for retrieval-augmented generation backed by PostgreSQL.
Database Engineering

Database Engineering

The engineering decisions that determine whether a PostgreSQL database stays fast, consistent, and recoverable as it grows, not just at launch.

Database Design

Schema designed around the domain's real entities and rules during planning, not reverse-engineered from application code later.

Normalization

Tables structured to eliminate redundancy where it matters, with deliberate denormalization only where performance genuinely requires it.

Indexes

Index strategy built around the application's actual query patterns, reviewed as those patterns change.

Transactions

Transaction boundaries and isolation levels chosen for the real consistency requirements, not left at the default everywhere.

Replication

Read replicas and standby servers configured for the application's actual availability and read-scaling needs.

Partitioning

Large tables partitioned before they become a performance problem, not after queries start timing out.

JSONB

Semi-structured data modeled deliberately, with indexing applied where JSONB fields are actually queried.

Performance

Query performance monitored and tuned continuously, not assessed once before launch and left alone.

Monitoring

Visibility into query performance, connection counts, and replication lag — the difference between debugging and guessing.

Backup

Automated backup procedures tested by actually restoring from them, not assumed to work because they run on schedule.

Disaster Recovery

Recovery procedures designed against a defined recovery time objective and tested before they're needed.
Representative Solutions

Where this technology fits

Reference architectures from our Representative Solutions collection that could plausibly be built on this stack.

Enterprise CRM
Representative Solution

Enterprise CRM

Enterprise Software
Smart Warehouse Management System
Representative Solution

Smart Warehouse Management System

Logistics Software
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