LLM Development & Engineering
Fine-tuning, prompt engineering, RAG, and model deployment at scale.
What We Deliver
Large language models represent the most significant platform shift in software since the cloud. But getting them to work reliably in production requires deep engineering expertise across prompting, retrieval, fine-tuning, and deployment. Inovex Systems' LLM engineering team works across the full model stack, designing precision prompt systems, building RAG pipelines that ground outputs in your data, fine-tuning base models on your domain-specific data, and deploying models into scalable production infrastructure. We work with GPT-4o, Claude 3.5/4, Gemini, Llama, Mistral, and other frontier and open-source models, selecting the right model for each use case based on capability, cost, latency, and privacy requirements.
Key Benefits
- Build AI applications that are reliable, not just impressive in demos
- Reduce AI costs through optimised models and efficient inference
- Own your AI capabilities with custom fine-tuned models
- Deploy with confidence using production-grade monitoring and evaluation
Key Offerings
Every capability we offer is designed to deliver real, measurable value to your business.
Prompt Engineering
Systematic prompt design, chain-of-thought frameworks, and structured output engineering that maximises model reliability and output quality.
Fine-Tuning
Domain-specific fine-tuning on your proprietary data to create models that understand your terminology, tone, and task requirements.
RAG Architecture
Production RAG pipelines with optimised embedding, retrieval, re-ranking, and generation stages for accurate, grounded AI outputs.
Model Evaluation
Rigorous evaluation frameworks measuring accuracy, hallucination rate, latency, and cost, ensuring models meet production-grade standards before deployment.
Model Deployment
Scalable model serving infrastructure on AWS, GCP, or Azure, including API endpoints, rate limiting, caching, and monitoring.
LLM Ops
Continuous monitoring of model performance, output quality, drift detection, and automated alerting for production LLM systems.
Our Approach
A proven, transparent process that keeps you informed at every stage, no surprises, just results.
Assessment
Use-case analysis and model selection across frontier and open-source options.
Data Prep
Training data curation, cleaning, and formatting for fine-tuning or RAG.
Engineering
Prompt engineering, fine-tuning, or RAG pipeline development.
Evaluation
Systematic evaluation against accuracy, latency, and cost benchmarks.
Deploy
Production deployment with scalable serving infrastructure.
Monitor
Ongoing output quality monitoring and model improvement.
Industries We Serve
We've delivered llm development solutions across a wide range of sectors and business models.
Healthcare & Clinics
Retail & E-commerce
Finance & FinTech
Education & EdTech
Manufacturing
Logistics & Supply Chain
Frequently Asked Questions
Should I fine-tune an LLM or use RAG?
It depends on your use case. RAG is best when you need the model to answer accurately from specific documents or data that changes frequently. Fine-tuning is better when you need the model to adopt a specific tone, format, or domain expertise consistently. Many production systems use both together.
Which LLMs do you work with?
We work with GPT-4o, Claude 3.5 and Claude 4, Gemini, Llama 3, Mistral, and other frontier and open-source models. We select the right model based on your capability requirements, latency targets, cost budget, and data privacy needs.
Can you deploy an LLM on our own infrastructure?
Yes. We can deploy open-source models like Llama and Mistral on your own AWS, Azure, or GCP infrastructure, keeping all data within your environment. This is ideal for businesses with strict data privacy or compliance requirements.
What is prompt engineering and why does it matter?
Prompt engineering is the practice of designing the instructions given to an LLM to reliably produce the output you need. Good prompt engineering dramatically improves accuracy, reduces hallucinations, and lowers token costs. It is a core part of every LLM project we deliver.
How do you evaluate whether the LLM is performing well?
We build evaluation frameworks that measure accuracy, hallucination rate, response latency, and task completion rate against your defined success criteria. We do not deploy to production until the model meets your quality bar.
Ready to Get Started with LLM Development?
Talk to our team about your requirements and get a tailored proposal within 24 hours.
Related Services
Explore more ways we can help your business.
Cloud, Security & Delivery
We architect, migrate, and manage cloud infrastructure, with built-in security, DevOps, and quality assurance. From zero-trust security frameworks to CI/CD pipelines, we deliver infrastructure that performs under pressure.
AI Integration & Automation
We connect leading AI APIs, OpenAI, Anthropic, Google Gemini, directly into your existing systems and workflows, automating repetitive operations and unlocking intelligent capabilities without rebuilding your stack.
Chatbot & Conversational AI Development
We build production-grade chatbots and conversational AI systems, including RAG-powered assistants that answer accurately from your company's own documents, policies, and data, for customer support, internal knowledge management, and sales enablement.