Service

Backend, APIs & Databases

Scalable APIs, event-driven microservices, and the databases underneath them — the engine room behind everything we build.

Node.jsGoPythonJavaPostgreSQL
Overview

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.

How We Work

Our process

01

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.

02

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.

03

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.

04

Migrate & Harden

We execute zero-downtime cutovers with rollback paths, apply sharding where needed, and load-test before handing off documented, versioned endpoints.

What's Included

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

Stack

Technologies we use

Node.js
Go
Python
Java
PostgreSQL
MySQL
MongoDB
Redis
Kafka
gRPC
Elasticsearch
ClickHouse
FAQ

Frequently asked questions

Yes. We audit, profile, index, and re-architect existing backends and databases for better speed, correctness, and scalability.
PostgreSQL is our default (jsonb, pgvector, TimescaleDB), plus MySQL, MongoDB, DynamoDB, Cassandra, Redis, Elasticsearch, and ClickHouse. We pick the right engine for your actual access patterns, not by habit.
Yes. MongoDB, DynamoDB, and Cassandra all come up regularly for flexible schemas or high-write workloads — we use them when the data genuinely doesn't fit a relational table, alongside our PostgreSQL default.
Yes. We bolt on Elasticsearch for full-text search or ClickHouse for fast analytics alongside your primary database, without disrupting the existing transactional system.
Yes. We plan dual-write, backfill, and cutover strategies so migrations happen with no downtime and a clean rollback path — including relational-to-NoSQL moves, not just database-to-database.
Focused API or optimization engagements start from $8,000; full backend platforms and migrations from $25,000+.
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