Services
Database Design & Development
Complex data models designed for correctness, performance, and AI integration from day one.
What makes database design critical for AI applications?
Database design is the foundation every AI system depends on. Poorly structured data is the single most common reason AI implementations fail or produce inaccurate results. Code and Trust designs PostgreSQL schemas that support clean data ingestion, efficient retrieval for RAG systems, and real-time analytics — typically reducing query times by 60–80% versus unoptimized legacy schemas.
Who hires Code and Trust for database work?
Database engagements most often start from a performance crisis: pages timing out, reports taking minutes instead of seconds, or an AI feature that can't run against the existing data structure. The other common trigger is a planned AI implementation where clean, structured data is a prerequisite for the model to work correctly.
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Companies with performance problems on existing databases — slow queries, timeouts
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Teams building AI/ML features on top of existing data that isn't structured for it
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Organizations migrating from legacy databases (Oracle, MySQL, MSSQL) to modern PostgreSQL
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Founders building data-heavy products who need schema designed for scale from the start
What does Code and Trust do for database clients?
A FinTech platform client migrated from MySQL 5.7 to PostgreSQL 15 with pgvector, enabling AI-powered transaction categorization and fraud detection. Migration completed in 8 weeks with zero data loss. Query performance improved 4x on average across key reports.
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Data modeling and schema design for complex domain logic
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Query optimization and indexing strategy (targeted 40–80% improvement)
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Data migration from legacy databases with zero data loss guarantee
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Replication and high availability setup
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Vector database setup for AI/RAG workloads (pgvector, Pinecone)
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Database monitoring, alerting, and slow-query analysis
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Read replica configuration for analytics workloads
Database technologies we work with
PostgreSQL is our primary platform — it's the right choice for almost every new project and most migrations. We add pgvector for AI embedding storage, TimescaleDB for time-series data, and Redis for caching hot query results. For serverless deployments, Neon provides PostgreSQL with branching support ideal for staging environments.
Frequently asked questions
The most common questions before a database engagement: which database platform, whether an existing database can be improved without a rebuild, how migrations work without losing data, and whether we support the vector workloads required for AI features.
What database do you primarily work with?
PostgreSQL. It's the most capable open-source relational database, has native vector support via pgvector for AI workloads, handles JSON well, and has excellent tooling. We're also experienced with MySQL, MSSQL, MongoDB, and Oracle for migrations.
Can you optimize an existing database without rebuilding it?
Usually yes. Index analysis, query optimization, and connection pooling often produce 40–70% performance improvements without any schema changes. A 2-week database audit identifies the highest-ROI changes.
How do you handle data migration from a legacy system?
We write migration scripts that run in dry-run mode first — validating counts, checksums, and business logic against the source. We never touch production data until dry-run passes in a staging environment.
Do you support AI/vector workloads?
Yes. We set up pgvector extensions, design embedding storage schemas, optimize ANN (approximate nearest neighbor) queries, and implement hybrid search patterns that combine vector similarity with traditional SQL filters.
What is included in post-launch database support?
90 days of monitoring, query optimization adjustments as your data grows, index maintenance, and emergency response for any performance degradation or availability issues.
Ready to fix your data layer?
We start with a database audit — 2 weeks, written findings, prioritized by ROI. No commitment to continue.