Production RAG systems, AI chatbots and agents from a Toronto studio. We built a RAG system for a major automotive brand, now running at 10+ companies.
We build AI systems that answer from your own documents and data, take defined actions in your tools, and are measured before they are trusted. The work covers retrieval pipelines, chatbots, agents and the cloud infrastructure that runs them.
Our largest deployment is a retrieval-augmented generation (RAG) system for a major automotive brand, running on Google Cloud across more than ten companies. We also ship AI inside products, such as EngPrep’s instant TOEFL feedback and JOL’s weak-spot practice.
AI development
Search and grounded answers over manuals, policies and knowledge bases, with sources shown for every answer.
Chatbots that answer from approved content, hand off to people when needed and report what they could not resolve.
Agents that perform a defined set of actions in business software, with approval steps for anything hard to undo.
Feedback, recommendations and generation inside web and mobile apps, designed with the product team.
Cloud deployment, vector databases, monitoring and test sets that show whether a change made answers better or worse.
AI Infrastructure
Built a retrieval-augmented generation system and the cloud AI infrastructure behind it, deployed across 10+ companies on Google Cloud. (Client under NDA.)
EdTech · AI
An AI-powered TOEFL iBT prep app with instant Japanese feedback across Speaking, Writing, Reading, and Listening. 100+ customers — one learner raised their score from 40 to 65 in a month.
Mobile · EdTech · AI
A game-based corporate learning platform from NUTS Inc. (Tokyo). We built the learner mobile app for iOS and Android — gamified quests, AI-driven weak-spot practice, and a multilingual UI for foreign employees preparing for the JLPT.
Use standard RAG when people need grounded answers from a known set of documents. Use an agent when the system must also choose and perform actions in business software. Start with the least autonomous design that can do the job reliably.
Most teams start with a pilot on one workflow that has a clear baseline. Our readiness assessment and 90-day pilot roadmap lay out the steps.
Typically Python, LangChain and OpenAI models on Google Cloud or Supabase, with a vector database for retrieval. We choose per project based on your data and security needs.
Compare standard RAG, agentic RAG and action-taking AI agents across use cases, cost, latency, evaluation, security and implementation complexity.
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Score your company across workflow, data, technology, people and governance before funding an AI pilot or enterprise rollout.
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A week-by-week AI pilot plan covering workflow selection, data, prototypes, evaluations, integration, supervised use and the final scale-or-stop decision.
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Design an AI customer service system with grounded knowledge, safe actions, human handoffs, evaluations and metrics that protect customer outcomes.
Read articleWhy prompt injection cannot be filtered away, a quick test to find risky AI features, six design patterns that contain it, and a checklist for your team.
Read articleWhat the Model Context Protocol is, what it does and does not secure, three ways a business can use it, and a checklist for approving any MCP server.
Read articleHow to choose AI search for internal knowledge: suite assistant, standalone tool or custom RAG, the permission checks to run first, and a pilot plan.
Read articleFive steps to choose an LLM for a business app: acceptance criteria, a starting model, where data is stored and processed, and planning for retirement.
Read articleTell us what you want to achieve. We will help define the next step.