All practice areas

Practice area 06 · Database & backend

Robust data foundations that scale as your product grows.

I design and build PostgreSQL, MongoDB, and Supabase backends optimised for AI workloads, real-time applications, and high-volume transactional systems. From schema design to query optimisation to vector storage — your data layer is built to handle production load from day one.

Engagement model
Fixed scope, fixed price
Progress
Weekly demo + written update
Support
30 days included
Discuss your project
%

Faster load times with caching

$50K+

Monthly transactions processed

SOC-2

Compliance-ready auth implementation

Results from delivered engagements in this practice area.

01 — How it works

From your current process to a system that runs itself.

  1. Step 01

    Model the Data

    We design the entity-relationship model together before any tables are created. Normalization, indexing, and access patterns are planned upfront.

  2. Step 02

    Build the Backend

    The API layer is built on Node.js with full authentication, input validation, and error handling. Queries are optimised for the expected data volume.

  3. Step 03

    Test & Harden

    Load testing, security hardening (SQL injection, rate limiting), and backup strategies are configured before handing over to production.

02 — Scope of work

What the engagement delivers.

  1. APostgreSQL schema design, migrations, and indexing strategy
  2. BSupabase setup with Row Level Security and real-time subscriptions
  3. CVector database configuration for AI/RAG workloads (pgvector, Pinecone)
  4. DORM setup with Prisma or TypeORM
  5. ERedis caching layer for high-frequency read operations
  6. FAPI layer with full CRUD, filtering, pagination, and auth

03 — Related projects

Delivered and running in production.

All projects

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