Claude Successful story for company or something else?

We started incorporating Claude into our development workflow about 4–5 months ago, initially with small projects and experiments. What started as a limited use case has grown significantly: today, the entire company uses Claude for code reviews, development, and day-to-day engineering work.

At the same time, we want to avoid becoming overly dependent on a single US-based proprietary AI provider. Our goal is therefore not to replace Claude immediately, but to develop a viable alternative based on open-weight models that we can operate and scale ourselves.

The plan is to start with a bare-minimum, scalable system. As a proof of concept, we want to deploy a less capable but sufficiently powerful model, Alibaba's Qwen3.8-27B, and demonstrate that the overall concept is technically and operationally feasible.

The POC is deliberately narrow. It should demonstrate that:

• the complete system can be deployed and operated end to end;
• capable AI coding agents can run on our own infrastructure;
• developers can hand off simple, clearly defined tasks to the internal AI system;
• the system can reliably support up to two concurrent developers running Qwen3.8-27B.

More demanding development work will continue to use external models such as Claude for the time being. As our infrastructure and implementation mature, we can progressively move more workloads to our internal system.

The longer-term objective is to scale this architecture to the most capable and efficient open-weight models available. At the moment, the models we are evaluating include Z.AI's GLM5.3-Flash and Alibaba's Qwen3.8-Flash-Next.

This approach allows us to start small, validate the architecture with a realistic workload, and then scale the system as open-weight models continue to improve.

The goal of the POC is therefore not to build a replacement for Claude on day one. It is to prove that we can build the foundation of an internal AI development platform that can eventually handle a meaningful share of our engineering workload while reducing our dependency on external proprietary models.