AI Engineering
AI that solves real product and operations problems.
Agents, retrieval and evaluation, shipped inside real products with measurable outcomes, clear guardrails and maintainable implementation.
In short
Norah Labs builds production AI systems: LLM agents with tool use and human approval gates, retrieval-augmented generation (RAG) grounded in your own knowledge, and the evaluation, guardrails and monitoring that keep them reliable. We integrate AI into existing products rather than rebuilding them, and work with hosted, open-weight or local models.
AI Engineering
What we build
Intelligent automation
Automate repetitive tasks, streamline workflows and reduce manual overhead with agents that act through your tools.
AI-powered features
Assistants, recommendations, search and content generation integrated directly into your products.
Custom ML models
Models for classification, prediction, computer vision and NLP, tailored to your data.
How we approach AI
Problem-first
We start with the problem, not the technology. AI is the solution, not the starting point.
Practical integration
We integrate AI into existing systems, so you do not need to rebuild everything.
Transparent and ethical
We build AI that is explainable, fair and respects user privacy, with approval gates where stakes are high.
Continuous improvement
Models drift. We set up monitoring, evaluation and retraining so quality holds after launch.
Where it pays off
Customer support
Assistants that handle common queries and escalate complex issues to people.
Content and copy
Marketing copy, product descriptions and long-form content generated at scale.
Data analysis
Extract insight from documents, transcripts and unstructured data automatically.
Personalization
Recommendation engines, adaptive interfaces and tailored user experiences.
Tools we work with
- Large language models
- GPT-4ClaudeLlamaCustom fine-tuning
- Machine learning
- TensorFlowPyTorchScikit-learnXGBoost
- AI platforms
- OpenAI APIAnthropic APIHugging FaceReplicate
- Vector databases
- PineconeWeaviateChroma
- Frameworks
- LangChainLangGraphLlamaIndexRAG pipelines
Who it is for
Companies exploring AI
Curious but cautious? We help you identify high-return AI opportunities.
Teams ready to scale
Manual processes slowing you down? We automate intelligently.
Technical organizations
Need AI expertise? We integrate with your engineering team.
FAQ
AI Engineering: common questions
What is retrieval-augmented generation (RAG)?
Retrieval-augmented generation (RAG) is a technique where a language model first retrieves relevant passages from a trusted knowledge source and then writes its answer from them, usually with citations. It reduces made-up answers and lets you update knowledge without retraining the model. Norah Labs builds RAG systems with source citations, as in Tabibu Health.
What does Norah Labs build in AI?
We build LLM agents that use tools, RAG systems grounded in your data, AI features inside existing products, custom machine-learning models, and the evaluation, guardrails and monitoring around them. Examples include Tabibu Health, an AI health companion, and Atlarix, a local-first agent workstation.
Which AI models can you work with?
Hosted models such as OpenAI and Anthropic, open-weight models such as Llama, DeepSeek, Qwen, Kimi and MiniMax, and local models. We design systems so the model can be swapped without rewriting the product, so you are not locked in to one provider.
How do you make AI products safe and reliable?
We build guardrails and safety checks into the first version, evaluate outputs before release, and place human approval gates where the stakes are high. After launch we monitor quality and tune the system as the data changes.
Can you add AI to a product we already have?
Yes. We integrate AI into existing systems rather than rebuilding them: a retrieval layer over your documents, an assistant inside your app, or an agent connected to your internal tools and APIs.
Start a project
Have something hard to build? Let's build it.
Bring the problem. We'll bring engineers, designers and a practical plan.