DevZila combines AI transformation strategy, AI integration and automation, and deep Ruby on Rails engineering. We modernize the application you already have and make it AI-ready — without a rewrite.
AI projects fail when strategy, integration and the underlying codebase are handled by different vendors. We do all three, so the plan we write is the plan we ship.
Find where AI pays off in your product and operations, and turn it into a roadmap your team can actually deliver.
Ship LLM-powered features and automated workflows inside your existing Rails application — tested, observable and cost-controlled.
Our core craft. We rescue, upgrade and modernize legacy Rails codebases so new capabilities land on solid ground.
Language models are only as useful as the context and actions you give them. A mature Rails application already holds both: years of business rules in its models and a clean history of everything your customers did.
AI enablement for a Rails application means adding an AI layer — retrieval, model calls, tools and guardrails — on top of the domain logic and data you already have, while modernizing the code underneath so it stays safe to change.
Structured records in PostgreSQL are ideal raw material for retrieval, classification and summarization. With pgvector, embeddings live next to the data they describe.
Existing service objects and model methods can be exposed to agents as tools, so AI acts through the same validations and permissions as your users.
Background jobs for long-running model calls, Turbo Streams for streaming answers, Active Storage for documents. Less new infrastructure, faster delivery.
We add AI where it earns its keep and modernize incrementally underneath. Your team keeps shipping the whole time.
Practical patterns we design and build inside Ruby on Rails applications. Each one starts small, ships behind a feature flag and is measured against a baseline.
Assistants that answer questions and take actions using your application's own data, business rules and permissions.
Search records, documents and tickets by meaning. Answers are grounded in your data and cite their sources.
Turn invoices, contracts and forms into validated, structured records that flow straight into your models.
Agents that triage, route, reconcile and follow up as background jobs — with human approval where it matters.
Draft replies, summarize long threads, classify and tag incoming requests, and surface the next best action.
AI-assisted code audits, test backfill and upgrade work, reviewed line by line by senior Rails engineers.
Stabilize, refactor and re-architect aging Rails applications so they are ready for AI and for the next decade.
Audit · Refactor · AI-readyIncremental, low-risk upgrades to current Ruby and Rails versions with dual-booting and zero-downtime deploys.
Rails 4 → 8 · Dual bootSenior engineers for new features, APIs, performance work and long-term maintenance of business-critical apps.
Rails · Hotwire · SidekiqA fixed-scope review of your codebase, data and workflows that ends with a ranked AI roadmap and estimate.
Fixed scope · RoadmapHotwire and React interfaces for Rails backends, including streaming AI chat and assistant experiences.
Hotwire · React · TypeScriptEvent-driven financial software with accurate accounting, full audit trails and AI you can explain to an auditor.
Ledgers · Event sourcingPick the entry point that matches where you are. Each engagement produces something you can use on its own, with no long-term lock-in.
We review your Rails codebase, data and workflows, then hand you a ranked list of AI opportunities with an architecture and delivery plan.
We take the highest-value use case and build it end to end inside your Rails app, with evaluation, guardrails and monitoring from day one.
A dedicated senior team that upgrades and refactors your legacy Rails application while delivering AI features on a steady cadence.
Hands-on training that leaves the knowledge in your team: building AI features in Rails, Domain-Driven Design, Hotwire and code mentoring.
We pair battle-tested Rails infrastructure with an AI layer that is deliberately thin, swappable and observable — so a model change is a configuration change, not a rewrite.
stack = { core: { framework: "Ruby on Rails 8", database: "PostgreSQL + pgvector", jobs: "Sidekiq / Solid Queue", realtime: "Hotwire · Turbo Streams", }, ai_layer: { models: ["Claude", "GPT", "Gemini", "open-weight"], retrieval: "RAG over your own data", agents: "tool calling · MCP", safeguards: ["evals", "guardrails", "budgets"], }, frontend: ["Hotwire", "React", "TypeScript"], infra: ["AWS", "Heroku", "Fly.io", "Kamal"] }
Opinionated, fast to ship and maintainable long-term. We keep AI code in plain service objects and jobs — no parallel framework to learn.
Streaming, tool calling, retrieval and agentic workflows woven into your data model and permission system from the first commit.
Providers sit behind a small gateway. Swap or mix models per task as quality, price and privacy requirements change.
N+1 queries eliminated, rate limiting, OWASP-aligned practices, prompt-injection defenses and automated security scanning in CI.
We audit your codebase, data and workflows, then rank AI opportunities by value and feasibility. No code is written before we agree on a brief.
We upgrade Ruby and Rails where it is blocking, and put tests and CI around the parts of the system AI will touch.
Weekly sprints with a demo at the end of each. The feature ships behind a flag with evals, guardrails and cost monitoring.
Zero-downtime rollout, monitoring and a 30-day support window after launch. Then we extend what works to the next use case.
Plenty of firms can call a model API. Very few can do it safely inside a ten-year-old Rails codebase. That intersection is where we work.
Deep Rails expertise, pioneering remote-first consulting and sharing knowledge through books, blogs and courses since the framework's inception.
Domain-Driven Design pioneers in Ruby on Rails. Clear domain boundaries are also what make AI agents predictable.
Delivering open and closed workshops that have empowered dozens of developers to master Domain-Driven Design.
Every AI feature ships with tests, evaluations, cost budgets and a rollback path — the same standard as the rest of your code.
AI speeds up our audits, test writing and upgrades. Senior engineers review every change before it reaches your main branch.
We start from the workflow and the metric, not the model. If a simple rule beats an LLM, we will tell you.
DevZila is an AI-first Ruby on Rails consultancy. We help companies that run on a Rails application do three things: decide where AI creates real business value, integrate LLM-powered features and automation into the existing app, and modernize the codebase — upgrades, tests and architecture — so it is safe to build on.
Yes. In most cases AI features are added as a layer on top of the application you already have: service objects that call a language model, background jobs for long-running work, and a vector index inside your existing PostgreSQL database. Where an outdated Ruby or Rails version blocks progress, we upgrade incrementally in parallel instead of starting over.
AI enablement is the work of making a company and its software able to use AI productively. It covers choosing use cases with measurable value, preparing data, integrating models into existing workflows, adding evaluation and guardrails, and training the team that will own the result.
We are model-agnostic. We work with OpenAI, Anthropic Claude, Google Gemini and open-weight models, and pick per use case based on quality, latency, cost and data-privacy constraints. On the Rails side we use PostgreSQL with pgvector, background jobs, Turbo Streams for streaming responses, and Ruby libraries such as RubyLLM, ruby-openai and langchainrb.
Most teams start with an AI Readiness Assessment: a short, fixed-scope review of your codebase, data and workflows that ends with a ranked list of AI opportunities and a delivery plan. If you already know what you want to build, we can go straight to a pilot.
We design every integration around data minimization: only the fields a task needs are sent to a model, personal data can be redacted first, and retrieval respects your existing permission rules. We help you choose providers and settings that match your privacy requirements, including self-hosted open-weight models when data must not leave your infrastructure.