In practice
AI product engineering: what we build
Architecture
Systems designed so the model is one replaceable part, with retrieval, tools, queues and data stores around it, on AWS and serverless infrastructure that scales with use.
Model selection and cost
The right model for each step, caching and batching where they pay off, and cost per task tracked from the first prototype so the margins are known before launch.
Evaluation and reliability
Evaluation suites, tracing, guardrails and rollback, so a new model or prompt ships on evidence rather than on a good feeling.
Interface design
Screens that show what the AI did, how sure it is and what needs a person, built in React and Next.js for the people who use them every day.
A good fit when
- You have a working prototype and need it to carry real users.
- Model costs are growing faster than revenue.
- Quality shifts whenever a model or prompt changes and nobody can say why.
- You need a small senior team that can own the product end to end.
How an engagement runs
One week mapping the real workflow with your team, exceptions included. Two to three weeks proving one workflow on your own documents and systems, against an evaluation set agreed up front. Then the production build — integrations, permissions, monitoring and review points — shipped behind your authentication, in your cloud, and handed over with documentation and training.
We work on site with teams in North Vancouver, West Vancouver and Downtown Vancouver.
Proof in our own products
We run this in production ourselves
RentFlow
Our own AI property management product, designed, built and operated by us: tenant screening, maintenance triage and rent collection for operators.
Visit RentFlowQuorum
Several agents working to a structured process — private chats, a convened board, a challenge round and a synthesis — in one product.
Visit QuorumQuestions we are asked
Can you take over a prototype someone else built?
Yes. We start by reviewing the architecture, the costs and the failure cases, then decide with you what to keep, what to harden and what to rebuild.
Which technologies do you use?
TypeScript, React, Next.js, Node.js and NestJS for the product; AWS, serverless, DynamoDB, PostgreSQL and Docker for the infrastructure; OpenAI, Anthropic Claude and open-weight models behind it, with retrieval pipelines and evaluation and tracing.
Who owns the code?
You do. The code, the infrastructure and the documentation are handed to your team, with runbooks and training so you can maintain it with or without us.
How do you keep AI costs under control?
By measuring cost per task from the start, using the smallest model that meets the quality bar for each step, caching repeated work, and reviewing the numbers whenever volume or models change.
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