DATA & STORAGE
Database Technology Stack for Modern Applications
We design relational, document, key‑value, and vector storage that fits your workloads—from transactional systems to AI‑driven experiences.
PostgreSQL, MongoDB, SQL Server, DynamoDB, Redis, and vector databases each play a specific role in your data platform, and we help you put them together safely.
PostgreSQL & SQL Server for transactional and reporting workloads.
MongoDB for flexible schemas and evolving products.
Vector databases for semantic search and AI retrieval.
DynamoDB for high‑throughput, low‑latency access patterns.
Redis for caching, sessions, queues, and real‑time features.
Automated backups, restores, and DR plans built into the platform.
PostgreSQL
Advanced relational database for complex transactional and analytical workloads.
MongoDB
Document database for evolving schemas, content, and user‑generated data.
Vector Databases
Specialised stores for embeddings and semantic search powering AI retrieval.
SQL Server
Microsoft SQL Server for line‑of‑business apps, reporting, and integrations.
DynamoDB
AWS NoSQL database for high‑throughput, low‑latency workloads at scale.
Redis
In‑memory store for caching, sessions, queues, and real‑time features.
How We Design Your Data Layer
We align storage choices with access patterns, consistency needs, and the way AI and analytics will use your data—so you avoid over‑engineering or premature bottlenecks.
Workload‑Driven Design
OLTP vs OLAP, read/write ratios, and multi‑tenant requirements all influence whether we choose relational, document, key‑value, or vector storage.
Migrations & Evolution
We plan for growth and change: schema migrations, versioned APIs, blue‑green or online migrations, and clear rollback strategies.
Reliability & Governance
Backups, restore testing, security controls, and monitoring are treated as first‑class features of your data platform—not afterthoughts.
Database Technologies FAQs
How we design, migrate, and operate the databases that sit underneath your products and AI systems.
How do you choose between relational and NoSQL databases?
We look at access patterns, data relationships, and future reporting needs. Often the right answer is a small combination of stores rather than a single database for everything.
Can you work with our existing database infrastructure?
Yes. We improve performance, reliability, and developer ergonomics around the databases you already use, and only introduce new technology when it clearly pays for itself.
How do you design databases for AI and analytics workloads?
We separate transactional and analytical concerns, design clean event or ETL pipelines, and add vector stores where AI retrieval or personalisation benefits from embeddings.
Do you handle migrations between databases?
We plan and execute migrations with staged rollouts, verification, and fallbacks so your teams always know the state of the data during the change.