AI Strategy
and Tooling
Every company is being told to use AI. Far fewer are being told where not to use it.
CodeWriter helps teams find that line: where AI genuinely saves time, where a tool needs to be built around it to be useful, and where a good old-fashioned script does the same work faster, cheaper, and with a predictable result.
No hype, no jargon. Just an honest look at your workflow and what technology fits it.
AI strategy and tooling at CodeWriter
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Defining how to use AI
Before anything gets built, we look at what your team actually does every day. Together we map out which tasks benefit from a language model, which ones need a human in the loop, and which ones should never leave your own infrastructure. The result is a clear, written guideline your developers and colleagues can follow, instead of everyone experimenting on their own.- Reviewing existing workflows and spotting the real bottlenecks.
- Choosing the right model for the job, instead of the biggest one.
- Guidelines on data privacy, source code, and what may be sent to an external service.
- Training and workshops so your team knows both the possibilities and the limits.
Building AI tools people actually use
A chat box is not a product. An AI feature only pays off when it fits into the tools your team already has, and when the output can be trusted and corrected. We build that layer around the model.- Java and Spring integrations with the Claude, OpenAI, and local models.
- JavaFX and Vaadin user interfaces that make AI output reviewable and editable.
- Retrieval on your own documentation and data, so answers stay grounded in your content.
- Automation of documentation, translation, and content pipelines.
Avoiding the overuse of AI
This is the part most consultants skip. A lot of what is now handed to a language model is a solved problem: a regular expression, a template, a database query, or a scheduled script. Those run in milliseconds, cost nothing per call, and give the same answer every time.- Identifying the tasks where a deterministic script beats a prompt.
- Replacing expensive prompt calls with plain code where the logic is fixed.
- Keeping an eye on token cost, latency, and the energy footprint of every call.
- Making sure results stay reproducible, testable, and explainable to your customers.