AI EngineeringAI Product Development
NotJuzApp builds AI-driven products and integrates large language models into existing applications. Work spans model integration with Claude, OpenAI and Gemini, retrieval-augmented generation over private data, AI agents that call real tools, and the data pipelines and evaluation needed to keep those systems reliable in production.
What's Included
The Engagement.
LLM Integration
Claude, OpenAI and Gemini wired into your product with sensible prompting, streaming responses and cost controls.
Retrieval (RAG)
Grounding model output in your own documents and data, so answers are specific and traceable rather than generic.
AI Agents & Tooling
Systems that call real APIs and act on the results, with the guardrails needed to keep them predictable.
Evaluation & Monitoring
Test sets and logging, so quality is measured rather than assumed once real users arrive.
Technology
Built With.
Scope
What We Deliver.
- LLM Integration
- RAG Application Development
- AI Agent Development
- AI Product Strategy
Also From NotJuzApp
Other Services.
Questions
Frequently Asked Questions.
What is RAG and when should it be used?
RAG, or retrieval-augmented generation, gives a language model access to your own documents at the moment a question is asked, instead of relying only on what the model learned in training. It is the right approach whenever answers must reflect private, specific or frequently changing information.
Can AI be added to an existing application?
Yes. Most AI work NotJuzApp does is added to software that already exists, as a new capability rather than a rebuild. The usual sequence is to identify one high-value use case, integrate a model behind an API, measure the result, and expand from there.
Which AI models does NotJuzApp work with?
NotJuzApp builds with Claude from Anthropic, OpenAI models and Google Gemini. Model choice depends on the task, the cost profile and the privacy requirements of the data involved, and applications are typically built so the underlying model can be swapped later.
How do you keep an AI feature reliable?
Reliability comes from evaluation rather than hope. NotJuzApp builds test sets of representative inputs, logs real production behaviour, and grounds output in retrieved source data where accuracy matters, so regressions are caught before users encounter them.
Need AI Product Development for your product?
Tell us what you are building. We will come back with a scope, an approach and an honest view of what it takes.