Services

Database Design & Architecture

Bad schema decisions are the most expensive mistakes in software — they're invisible until the data volume grows, and by then a fix means a migration under production load. We design schemas for correctness first: proper normalization, constraints that actually constrain, and indexes chosen from real query patterns instead of guesswork.

We work in both relational (PostgreSQL) and document (MongoDB) databases, using Prisma, SQLAlchemy, or Mongoose depending on the stack, and pick the model that fits your data — not whichever database happens to be trendy. For existing systems, we also do schema audits: finding the N+1 queries, missing indexes, and denormalization decisions that are quietly costing you performance.

PostgreSQLMongoDBPrisma ORMSQLAlchemyMongoose

What's Included

Schema design with proper normalization, constraints, and indexes chosen from real query patterns
Relational (PostgreSQL) or document (MongoDB) modeling — whichever fits your data shape, not a default
Migration strategy for schema changes that doesn't require downtime on a live system
Query performance audits for existing databases: slow queries, missing indexes, N+1 problems
Backup and recovery strategy defined before you need it, not discovered during an incident

How We Work

We use Claude Skills to encode reusable, task-specific best practices — document generation, boilerplate setup, coding conventions — for consistent, high-quality output across every project.
We implement automated workflow hooks — pre/post actions, validation, formatting — throughout the development lifecycle to catch issues before they ship.
We practice deliberate context and token management: scoping tasks, batching related changes, and structuring prompts and workflows to move fast without sacrificing accuracy.
We plan before we execute — breaking down features, defining clear specs, sequencing steps — to minimize rework when AI-assisted development is part of the process.
We pair AI-assisted development with manual code review and testing, so what ships is production-quality and maintainable, not just fast to generate.

Frequently Asked Questions

How do you decide between PostgreSQL and MongoDB?

By the shape of your data and how it's queried. Highly relational data with strict consistency needs points to PostgreSQL. Flexible, nested, or rapidly evolving schemas often fit MongoDB better. We've built production systems on both and don't default to either.

Can you audit a database we already have instead of designing a new one?

Yes — this is common work for us. We look at slow query logs, index coverage, and schema shape, then hand back a prioritized list of fixes, from quick index additions to structural changes worth planning for.

Do you handle data migrations from legacy systems?

Yes, including migrations from spreadsheets, older SQL databases, and no-code tools. We script migrations to be repeatable and reversible, so a bad migration doesn't mean data loss.

Have a project in mind?

Tell us what you're building — we'll help you architect it right the first time.

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What's Included

Schema design with proper normalization, constraints, and indexes chosen from real query patterns

Relational (PostgreSQL) or document (MongoDB) modeling — whichever fits your data shape, not a default

Migration strategy for schema changes that doesn't require downtime on a live system

Query performance audits for existing databases: slow queries, missing indexes, N+1 problems

Backup and recovery strategy defined before you need it, not discovered during an incident

How We Work

We use Claude Skills to encode reusable, task-specific best practices — document generation, boilerplate setup, coding conventions — for consistent, high-quality output across every project.

We implement automated workflow hooks — pre/post actions, validation, formatting — throughout the development lifecycle to catch issues before they ship.

We practice deliberate context and token management: scoping tasks, batching related changes, and structuring prompts and workflows to move fast without sacrificing accuracy.

We plan before we execute — breaking down features, defining clear specs, sequencing steps — to minimize rework when AI-assisted development is part of the process.

We pair AI-assisted development with manual code review and testing, so what ships is production-quality and maintainable, not just fast to generate.

Frequently Asked Questions

How do you decide between PostgreSQL and MongoDB?

Can you audit a database we already have instead of designing a new one?

Do you handle data migrations from legacy systems?

Yes, including migrations from spreadsheets, older SQL databases, and no-code tools. We script migrations to be repeatable and reversible, so a bad migration doesn't mean data loss.