What we build
RAG systems, computer vision and automation, taken to production rather than left at prototype.
- 01RAG & LLM Pipelines
- 02Computer Vision
- 03Automation Workflows
- 04Full-Stack Product Builds
- 05Cloudflare-Native Architecture
- 06Desktop Applications
- Stack
- PythonPyTorchOpenCVRAGFastAPINext.jsReactNode.jsElectronCloudflare Workers / D1 / R2
- How we work
- 01
DISCOVER
We define requirements, scope, and technical approach.
- 02
DESIGN
Architecture planning and solution design.
- 03
DEVELOP
Iterative development with regular deliverables.
- 04
DELIVER
Testing, deployment, and ongoing support.
- 01
Clients
Questions
Do you build the model or the product around it?
Both, and usually the product first. Most projects need a reliable pipeline, evaluation and an interface far more than a new model; we fine-tune or train only when off-the-shelf models measurably fall short.
Where does it run?
Wherever the data and the budget say. We deploy on Cloudflare Workers, D1 and R2 by default for web products, on your own servers when data must stay in-house, and as desktop applications with Electron when the work happens offline.
How do you keep an LLM feature from being unreliable?
With retrieval over your own sources, a test set of real prompts checked before every release, and guardrails at the boundaries: structured outputs, validation, and a fallback when the model is unsure.