AI development services
AI development services that make it to production
Devino Solutions Inc is a Canadian company headquartered in Ottawa. Our senior engineers design, build and run AI features and products on your data, from the first working slice to a system your users rely on every day.
Get it builtWhat our AI development services cover
LLM integration
Language-model features added to your existing product and data, with the orchestration and the product experience around them.
RAG over your own data
Retrieval pipelines over your documents and databases, so answers come from your data and are checked against it, not guessed.
AI agents and automation
Workflow agents with guardrails: browser automation, document processing and ops handoffs that stay auditable.
Custom machine learning
Task-specific models where an API model is not enough: spaCy, PyTorch, OpenCV and fine-tuned LLMs, trained, deployed and monitored.
Evaluation and cost control
An evaluation set built from your real examples, plus model routing, caching and right-sizing so the cost of each request is predictable.
Production hardening
Taking an AI demo to something that survives real users and real data: input handling, output validation, fallbacks and monitoring.
Who these AI services are for
Three situations bring teams to us: a product that needs an AI feature, an AI demo that has to survive production, and an idea for an AI product with nobody to build it.
The first reply to every brief is an honest read on fit. That includes saying so when an existing tool, or a simpler approach than the one you had in mind, would serve you better.
How an AI engagement runs
You describe the project at /start. An engineer reads it and replies within one business day with a scoped plan: what to build first, what data it needs and how we will know it works.
The first deliverable is a thin slice wired to your real data, not a slide deck. From then on you get a deployed link every week, so you can try the feature on your own cases while it is being built.
How we make AI trustworthy
Before tuning a prompt, we turn real examples from your domain into an evaluation set and measure every prompt, model or retrieval change against it. Regressions show up in the numbers before your users see them.
In production the same discipline continues: guardrails on what the model may do, fallbacks when it is unsure, a handoff to a person when a decision should not be automated, and tracing so failures are visible.
What you own, and who builds it
The prompts, retrieval pipelines, evaluation sets and model code live in your repository from day one. The cloud and model-provider accounts are registered to you and billed to you, so you see every usage bill yourself.
Every engineer brings 5+ years of production experience, and the engineer who scopes your project is the one who builds it. There is no project-manager layer between you and the work.
Case studies
Shorty — AI summaries for YouTube & Spotify
An AI summarization app for YouTube and Spotify that helps people save time and learn faster — designed in Figma, built with Next.js and Python microservices.
Growing 50% month over month
SuperBooks — AI bookkeeping for small businesses
AI bookkeeping and accounting software that connects to your bank, categorizes each transaction and matches it to its receipt or invoice.
GetItDone — a task manager your AI can actually see
An AI-native task manager where daily plans, stand-ups, tasks and time tracking live in one place, and AI agents get the same task context a person does.
Frequently asked questions
What are you building?
Three minutes at /start: describe the project. An engineer replies with a scoped plan within one business day, not a sales call.
Get it built