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
Faster load times with caching
Monthly transactions processed
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.
Model the Data
We design the entity-relationship model together before any tables are created. Normalization, indexing, and access patterns are planned upfront.
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.
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.
- APostgreSQL schema design, migrations, and indexing strategy
- BSupabase setup with Row Level Security and real-time subscriptions
- CVector database configuration for AI/RAG workloads (pgvector, Pinecone)
- DORM setup with Prisma or TypeORM
- ERedis caching layer for high-frequency read operations
- FAPI layer with full CRUD, filtering, pagination, and auth
03 — Related projects
Delivered and running in production.
All projectsSmartPaie
Payroll SaaS for Tunisian companies: payslips, CNSS, IRPP and HR documents.
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Internal dashboard turning Exact Online ledgers into live balance sheet and P&L reports.
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Full storefront that moved a clothing brand off DM orders.
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Social media SaaS: AI captions, scheduling and analytics in one workspace.