Canadian AI consulting, founded 2018

Nimon Systems is a small, senior-heavy team. There is no layer of account managers between you and the engineers who write the code. The people you talk to during discovery are the same people who design the architecture, implement the system, and stay involved through launch and beyond. This keeps conversations direct and feedback loops short, which matters enormously when the work is technical and the decisions have downstream consequences you need to live with for years.

We tend to structure projects as a series of short milestones rather than a single fixed-scope waterfall. A typical engagement begins with a discovery and architecture phase of a week or two where we work through the requirements, draft an initial data model and API contract, and agree on the technology choices. After that we move into implementation in iterative increments, and every increment produces working software that you can run, click through, and give feedback on. We find that this rhythm consistently produces better results than lengthy design documents because software problems reveal themselves only when the code is running.

Our default stack leans toward Golang and Python on the backend with PostgreSQL for relational data, React and Next.js on the frontend, and Firebase or self-hosted services for authentication and storage depending on the deployment environment. For AI work we pair commercial model APIs from OpenAI and Anthropic with open-source alternatives running on infrastructure we control. For geospatial projects we use PostGIS, GeoServer, GeoNode, and the mapping libraries that best fit the interaction model. None of this is dogmatic — if your existing stack dictates different choices, we are comfortable working within those constraints and have done so on multiple engagements where the client had strong preferences or existing investments we needed to respect.