Give the product a reliable understanding of its data
Modern applications often combine transactional data, analytical data and AI retrieval. We design the data layer according to how information needs to be written, retrieved, searched, analyzed and protected.
- We choose technology based on the product, team and business problem
- The stack serves the product — not the other way around
- PostgreSQL
- Firestore
Core technologies
What we use in this category
Best for: Relational products, integrity
PostgreSQL
Structured transactional applications and relational data.
Best for: Analytics, warehouses
BigQuery
Analytics and large-scale querying.
Best for: Performance, queues
Redis
Caching, queues and high-speed transient data.
Best for: RAG, search
Vector databases
Semantic search, retrieval and AI context via embeddings.
What we build
What matters more than the database logo
Schema, access patterns, security, retention, performance, auditability and cost decide whether the product stays trustworthy as it grows.
- Schema design
- Access patterns
- Security and retention
- Performance tuning
- Auditability
- Cost-aware analytics
- AI retrieval layers
FAQ
Questions teams ask
Straight answers on stack choices, fit, and how we engage.
PostgreSQL is often stronger for highly relational data and complex querying. Firestore shines for flexible real-time products and rapid serverless development. Some products benefit from both.
When analytics matter, yes — often with BigQuery alongside an operational store. We size the warehouse to decisions you actually need, not vanity dashboards.
Embeddings and vector search sit beside your systems of record. We design chunking, freshness, permissions and evaluation so retrieval stays accurate and safe.
Next step
Design your application’s data architecture
We’ll map how data is written, read, searched and protected — then pick the stores that fit.
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