Backend, APIs & Databases
Scalable APIs, event-driven microservices, and the databases underneath them — the engine room behind everything we build.
Backend work is the part of the product nobody notices until it breaks: a schema that starts timing out under real load, an API with no versioning strategy, a monolith that can't scale past a single Postgres instance. We build and rebuild that layer — REST, GraphQL, and gRPC APIs with proper auth and rate limiting, schemas indexed and sharded for the query patterns you actually run, and event-driven services on Kafka or RabbitMQ that scale horizontally instead of falling over under load. The language and database fit the workload, not the other way around: Node.js and Go for high-throughput services, Python and Java where your team or ecosystem already lives, PostgreSQL and MySQL as relational defaults, and MongoDB, DynamoDB, or Cassandra when the data genuinely doesn't fit a table.
This is for teams past the MVP stage: a startup whose Node.js backend is buckling under real traffic, a product team that inherited a MySQL database nobody wants to touch, or an engineering lead who needs realtime notifications or live data — WebSockets, edge functions — added without rewriting the whole stack. It's also for consolidations and migrations, where the cost of getting it wrong is downtime or lost data, not just a missed deadline. When the bottleneck isn't the primary database at all, we add Elasticsearch or ClickHouse alongside it for search and analytics, or a Redis caching layer in front of it, rather than forcing every problem through the same relational hammer.
Our approach is disciplined because the stakes are: we design schemas and API contracts before writing a line of service code, we run migrations with verification steps and a clean rollback path so they ship with no data loss, and we build on a stack — Node.js, Go, Python, Java, PostgreSQL, MySQL, MongoDB, Redis, Kafka, gRPC, Elasticsearch, ClickHouse — chosen for what actually holds up in production, not what's trendy. Focused engagements, like an API build or a query-tuning pass, start at $8,000; full backend platforms and migrations start at $25,000+, scoped against your real system rather than a generic estimate.
Our process
Audit & Baseline
We profile your existing schema, indexes, and API surface across Postgres, MySQL, or your NoSQL store to find the actual bottlenecks and data-loss risks before proposing any changes.
API & Event Design
We spec REST, GraphQL, or gRPC contracts with auth and rate limiting, and map event flows and topics for Kafka or RabbitMQ before implementation starts.
Build & Integrate
Engineers build services in Node.js, Go, Python, or Java, add Redis caching, Elasticsearch/ClickHouse, or read-replica strategies, and wire up WebSocket or edge-function channels for realtime features.
Migrate & Harden
We execute zero-downtime cutovers with rollback paths, apply sharding where needed, and load-test before handing off documented, versioned endpoints.
What we offer
API Development
Documented REST, GraphQL, and gRPC APIs with auth, rate limiting, and versioning
Database Architecture
Schema design, indexing, query tuning, sharding, and read-replica strategies
NoSQL & Document Databases
MongoDB, DynamoDB, and Cassandra data modeling for flexible schemas and high-write workloads that don't fit a relational table
Search & Analytics Engines
Elasticsearch and ClickHouse for full-text search, log analytics, and sub-second aggregate queries at scale
Caching & Performance Layers
Redis and Memcached caching strategies, connection pooling, and query tuning that cut response times without new hardware
Zero-Downtime Migrations
Safe migrations and consolidations (e.g. MySQL to PostgreSQL, or relational to NoSQL) with no data loss
Microservices & Events
Event-driven, message-queued systems with Kafka or RabbitMQ that scale horizontally
Realtime Services
Messaging, notifications, and live data via WebSockets and edge functions
Technologies we use
Frequently asked questions
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