AI Integration & AI Product Development in Ottawa.
RAG pipelines and autonomous LLM workflows built into your product. Mojo Studio delivers ai products for founders and businesses in Ottawa, senior engineers only, weekly demos, full IP ownership.
Built right,
shipped fast.
We integrate production-grade AI into real products — custom RAG pipelines over your own data, autonomous LLM agent workflows, and semantic search, built with the same engineering rigor as the rest of your stack. This isn't a chatbot bolted onto a landing page; it's AI wired into your actual product logic, with the guardrails to keep it from hallucinating in front of customers.
Built for Ottawa's market.
Ottawa's tech base is unusually concentrated around government and public-sector digitization plus a handful of major anchor companies (Shopify is headquartered here, alongside a dense cybersecurity and telecom cluster), which means much of the addressable work is either compliance-heavy govtech or spillover product work from larger tech employers rather than a broad startup ecosystem. That anchor-company gravity also pulls local engineering salaries up around a small number of large, well-funded employers, leaving smaller Ottawa companies and agencies priced out of local senior talent and more open to India-based partners for full builds. Ottawa's public-sector proximity means procurement and security expectations can be more formal than in a typical startup market, rewarding studios that can produce documentation and audit-ready processes.
Ottawa's govtech & public sector and e-commerce platforms (shopify ecosystem) businesses have specific needs when it comes to ai products — this is the local context that shapes how we scope every Ottawa engagement, not a generic playbook applied everywhere.
From brief to launch.
Use-case scoping — where AI actually adds value vs. hype
Data pipeline & vector store architecture
Model integration (OpenAI, Gemini, Claude, or fine-tuned)
Evaluation, guardrails, and production monitoring
AI Products FAQ.
Grounded retrieval (RAG) over your actual data, tight prompt scoping, output validation layers, and explicit fallback behavior when the model isn't confident — hallucination is a design problem, not something you patch after launch.