Ruby on Rails development
Our home framework for AI-ready products. New builds, rescues, upgrades, and the long-term care in between.
Overview
Rails done properly stays cheap to change — and ready for AI
Rails is fast to start with and easy to make a mess of. The difference a few years in comes down to the boring things: a clear domain model, real test coverage, and dependencies that were never allowed to drift.
That same foundation is what makes AI features maintainable — jobs, vectors, APIs, and evals living next to the domain instead of in a fragile sidecar.
- Upgrades to the current Rails and Ruby release
- pgvector, jobs, and APIs ready for AI workloads
- Test suites that run fast enough to be used
- N+1 queries and slow endpoints found and fixed
- Deployment you can run without us
What’s included
Where we spend our time
New applications
Greenfield products on current Rails with a small stack that can host AI features.
AI on Rails
Background jobs, embeddings, streaming responses, and provider integrations that fit the app.
Version upgrades
Move from an old Rails and Ruby version to current, one safe step at a time.
Rescue projects
Take over a codebase nobody wants to touch and get delivery moving again.
Performance tuning
Profile the real bottlenecks — database, cache, and job queue — then fix them.
Maintenance
Security patches, dependency updates, and monitoring on an agreed cadence.
Process
How the engagement runs
-
01
Audit
- Codebase and dependency review
- AI readiness check
- Prioritised findings
-
02
Stabilise
- Critical fixes
- Test coverage
- CI and deployment
-
03
Move forward
- Feature and AI delivery
- Regular upgrades
- Shared roadmap
Stack
Tools we reach for
- Ruby on Rails
- Hotwire
- PostgreSQL
- pgvector
- Redis
- Sidekiq
- Solid Queue
- RSpec
- Kamal
- Docker
Let's talk about the AI you want to ship
Tell us where you are — an idea, a half-built product, or a system that needs AI that earns its keep. We will reply with a practical next step.
