Forthscale engineers applications, integrations, and the model-serving environments behind them.
Work starts with a scoped build.
No magic. No black boxes. No AI theater.
Choose a model path, connect AI to the systems your team already uses, and build the application that uses it.
Illustrative types Forthscale is prepared to engineer. Not past deployments.
Design, size, deploy and run the platform that serves your models, or use an external API when that fits better.
Illustrative types Forthscale is prepared to engineer. Not past deployments.
Start from the problem and its constraints, then choose the model, the hosting and the amount of automation that fit. If a hosted API, a smaller model or a plain script does the job, that is the recommendation. Anything in production should be measurable, replaceable and owned by someone accountable.
Engineering principles by Naor Weissmann, founder of Forthscale.
If the decision is still open, start with a bounded assessment of models, hosting or architecture.
A defined build, such as an integration, an application or an inference platform, with scope agreed before it starts.
After launch, Forthscale can keep operating, evaluating and improving what was built. Coverage is set in the contract.
Forthscale has operated production infrastructure since 2012 across cloud, Kubernetes, CI/CD, observability and SRE. That experience informs how the company engineers and supports AI systems.
A short description of what you want to build, decide or keep running is enough to start.