Harness the power of Large Language Models with production-grade applications. From RAG systems and fine-tuning to agentic workflows — we build LLM solutions that are accurate, reliable, and tailored to your enterprise.
Large Language Models have fundamentally changed what's possible with software. They can understand context, generate human-quality text, answer questions, summarize documents, and reason about complex problems. But turning these capabilities into reliable, production-grade applications requires deep expertise in architecture, data, and engineering.
At Aethox AI, we specialize in building LLM applications that work in the real world. Whether you need a Retrieval-Augmented Generation system that grounds responses in your enterprise data, a fine-tuned model that speaks your industry's language, or an agentic workflow that automates complex tasks — we have the experience to deliver solutions that are accurate, secure, and scalable.
We work with the full spectrum of language models — from OpenAI's GPT series and Anthropic's Claude to open-source models like Llama and Mistral — and help you choose the right one based on performance, cost, privacy, and deployment requirements.
Large Language Models unlock capabilities that were impossible just a few years ago.
LLMs understand context, intent, and nuance — enabling truly natural interactions between users and systems.
Automate the creation of reports, summaries, emails, and documentation with human-quality writing.
RAG systems ground LLM responses in your enterprise data for accurate, sourced, and up-to-date answers.
Serve global audiences with LLMs that understand and generate content across dozens of languages.
Automate knowledge work, reduce manual effort, and let your teams focus on strategic, high-impact tasks.
LLM applications scale to serve thousands of users simultaneously without proportional cost increases.
End-to-end LLM development capabilities — from data grounding to autonomous agents.
Retrieval-Augmented Generation systems that ground LLMs in your enterprise data for accurate, sourced responses.
Fine-tune foundation models on your domain data for superior accuracy and specialized performance.
Scalable vector storage and similarity search infrastructure that powers fast, accurate semantic retrieval.
Systematic prompt design and optimization that maximizes model performance, reliability, and safety.
Multi-step LLM agent systems that plan, reason, and execute complex business processes autonomously.
Secure, production-grade API integration with OpenAI, Anthropic, Google, and open-source model providers.
The frameworks and platforms that power our Large Language Model applications.
A proven, transparent process that takes you from idea to production with confidence.
We analyze your use case, data sources, and requirements to define the right LLM architecture and approach.
We design the RAG pipeline, select models, plan data ingestion, and architect the system for production.
We build, test, and iterate on the LLM application in agile sprints with rigorous evaluation and demos.
We launch, monitor, and optimize — providing ongoing support, prompt updates, and model improvements.
Common questions about our LLM development services and how we deliver value.
RAG retrieves relevant information from a knowledge base at query time and feeds it to the model, making it ideal for dynamic, up-to-date knowledge. Fine-tuning trains a model on specific data to change its behavior or style. RAG is better for factual accuracy and knowledge that changes; fine-tuning is better for style, format, and domain-specific reasoning. We often combine both for optimal results.
We work with the full spectrum of language models including OpenAI's GPT series, Anthropic's Claude, Google's Gemini, and open-source models like Llama, Mistral, and Falcon. We help you choose the right model based on your requirements for performance, cost, latency, privacy, and deployment constraints — and we can switch models as the landscape evolves.
Yes. We specialize in private LLM deployments where your data never leaves your infrastructure. We can build systems using open-source models hosted on your servers, or configure cloud-based solutions with strict data governance, encryption, and compliance controls. RAG architectures also allow us to keep your data in a secure vector database that the model references without storing it in the model itself.
We use multiple techniques to minimize hallucinations: RAG systems that ground responses in verified data, prompt engineering that constrains model output, guardrails that validate responses, and evaluation pipelines that test for accuracy. For critical applications, we implement human-in-the-loop review and confidence scoring. No system is perfect, but our approach significantly reduces the risk of inaccurate outputs.
Costs vary based on scope and complexity. A RAG proof-of-concept can start around $15,000, while production LLM applications with custom integrations, fine-tuning, and agentic workflows typically range from $40,000 to $200,000+. We provide detailed, transparent pricing after our initial consultation and offer flexible engagement models to fit your budget and timeline.
Let's discuss how Large Language Models can transform your business with intelligent, language-driven solutions.