Frequently asked questions
Straight answers about adding AI to Ruby on Rails applications, modernizing legacy codebases, and what it is like to work with DevZila.
About DevZila
What is DevZila?
DevZila is an AI-first Ruby on Rails consultancy. We modernize legacy Rails applications and add AI capabilities to them — strategy, integration and automation — on top of senior Rails engineering.
How is DevZila different from a general AI agency?
General AI agencies tend to build something new next to your system. We work inside the Rails application you already have — its data model, business rules and permissions — because that is where most of the value and most of the risk are.
How is DevZila different from a traditional Rails agency?
We treat AI as a first-class part of Rails engineering. Every modernization we plan aims at an AI-ready codebase, AI features are built with the same testing discipline as any other code, and we use AI in our own delivery with senior engineers reviewing the result.
Do you only take on AI projects?
No. Ruby on Rails development, upgrades, performance work and Domain-Driven Design workshops remain our core. Many clients start with plain Rails work and add AI later.
Which kinds of companies do you work with?
Companies whose business runs on a Ruby on Rails application: SaaS products, marketplaces, internal platforms. We have particular depth in complex, rule-heavy domains such as fintech, where Domain-Driven Design and event sourcing matter.
AI enablement
Is our Rails application too old for AI?
No. Age decides where we start, not whether it is possible. Some AI features can be added through a small, isolated integration even before an upgrade. Most benefit from bringing Ruby and Rails up to supported versions first, and an assessment tells you which case you are in.
Is Ruby on Rails a good choice for AI applications?
Yes. Most AI features in business software are about orchestrating calls to hosted models, managing data, running background work and presenting results — all things Rails is very good at. Training custom models is a different job, usually done in Python, and rarely needed for product features.
Do you train custom AI models?
Rarely, because it is rarely necessary. Existing foundation models given the right context and tools cover the large majority of product use cases. We consider fine-tuning only when evaluation results show that prompting and retrieval are not enough.
How do you measure the return on an AI feature?
We agree on one business metric before building — handling time, turnaround, error rate, conversion — and measure a baseline. After launch we track the same metric together with the running cost per outcome, so the decision to expand, adjust or stop is based on numbers.
How do you handle data privacy and compliance?
We design for data minimization, redact personal data where possible, respect your permission model during retrieval, and choose providers, regions and retention settings to match your requirements, including self-hosted models. We are engineers rather than lawyers: we implement what your compliance team requires and document the data flows so their review is straightforward.
Do you use AI coding tools on client projects?
Yes, where they help and where your policies allow it. They speed up code audits, test writing and repetitive upgrade work. A senior engineer reviews every change, and nothing is merged without passing your CI.
Legacy Rails and upgrades
Can you take over an application from another agency or a developer who has left?
Yes. We start with an audit to understand the codebase and its risks, stabilize what is fragile, and then take on regular delivery. See legacy Rails modernization.
Do we have to finish upgrading before adding AI features?
Not necessarily. Upgrades and AI work can run in parallel when the feature touches a stable part of the system. We sequence the roadmap so the first AI feature ships as early as it safely can.
Do you work with React frontends as well as Hotwire?
Yes. Our frontend work covers both React and Hotwire, including migrations from React to Hotwire where a simpler architecture is the goal.
Working with us
How do you charge for your services?
Ongoing work is typically time and materials, billed monthly, with no long-term lock-in. The AI Readiness Assessment is a fixed-price engagement agreed up front.
Can we start with something small?
Absolutely. Many long-term collaborations began with a small upgrade, a refactor or a code audit. The AI Readiness Assessment was designed as a small first step for AI.
Who will work on our project?
Experienced developers. Each project is handled by at least two of our senior engineers, so knowledge is never held by one person.
Do you work alongside in-house teams?
Yes. On many projects we work with existing developers — contributing code, supporting architecture decisions and mentoring. We use your project management and communication tools rather than imposing ours.
How quickly will we hear back after contacting you?
Within 24 hours. You can also book a 15-minute call directly.