Nexariza AIBuild Custom LLM Applications That Work on Your Data
Generic AI chat tools do not know your business, cannot touch your systems, and raise real questions about data privacy and cost.
Production LLM applications — copilots, content and analysis tools, private models and AI features inside your product — grounded in your data, evaluated for accuracy and built to run at a predictable cost.
Typical timeline: 2-6 Weeks.
Capabilities
- Custom GPT / Claude / Gemini applications & copilots
- Private & self-hosted LLMs (Llama, Mistral, Qwen)
- Fine-tuning, LoRA & prompt engineering
- RAG grounding on your documents & databases
- LLM evaluation, guardrails & hallucination testing
- Generative AI features embedded in your SaaS or app
What you receive
- Production LLM Application & API
- Evaluation Suite & Accuracy Report
- Guardrails, Logging & Cost Controls
- Full Source Code, Prompts & Model Weights
Technology
OpenAI · Anthropic Claude · Llama · Hugging Face · LangChain · LlamaIndex · vLLM · FastAPI
Frequently asked questions
- Should we use GPT, Claude or an open-source LLM?
- It depends on accuracy, privacy and cost. We benchmark candidate models on your real tasks during scoping, then recommend a hosted API, a private open-source model, or a hybrid — with the trade-offs written down.
- Can you fine-tune an LLM on our company data?
- Yes. Most projects start with retrieval-augmented generation (RAG) because it is cheaper and easier to keep current; we fine-tune with LoRA when you need a specific style, format or domain behaviour that prompting and RAG cannot deliver.
- How do you stop the model from making things up?
- We ground answers in your sources with citations, add guardrails that refuse when evidence is missing, and ship an evaluation suite so accuracy is measured before and after every change.
- Can the LLM run on our own servers?
- Yes. We deploy open-source models such as Llama or Mistral on your cloud or on-premise GPUs with vLLM, so prompts and data never leave your infrastructure.