LLM Development Services
Your prototype works.
Production is where it gets hard.
Moving from a working LLM demo to a system that handles real enterprise data, meets latency SLAs, satisfies compliance requirements, and stays cost-efficient at scale — that is engineering, not prompt magic. We have built these systems. We can build yours.
Discuss Your LLM Project →Our Approach to LLM Development
Most LLM projects fail in one of two places: choosing the wrong architecture for the problem, or underestimating the infrastructure required to run reliably in production. We start by understanding what you are actually trying to solve.
We evaluate whether you need fine-tuning, RAG, prompt engineering, or a combination. We pick the model that fits your latency and cost envelope. We design for your data governance requirements from day one, not as an afterthought.
Our LLM engagements cover the full lifecycle: dataset curation and cleaning, fine-tuning or prompt pipeline construction, evaluation frameworks, deployment infrastructure, and production monitoring. We do not hand off a model — we hand off a running system with observability built in.
Whether you are building on OpenAI, deploying Llama 3 on-premise for GDPR compliance, or fine-tuning Mistral for a domain-specific task, we have worked across the full stack.
What We Deliver
Specific capabilities, not vague services.
Fine-Tuning Pipelines
Domain-specific fine-tuning using LoRA, QLoRA, and full parameter training. We handle dataset curation, training orchestration, evaluation, and iteration until the model performs on your data.
Prompt Engineering & Optimization
Systematic prompt design, chain-of-thought frameworks, and few-shot strategies that maximise model performance without the cost and complexity of fine-tuning.
LLM Application Development
End-to-end applications on top of LLM APIs and open-source models — chatbots, summarisers, classification systems, code assistants, document analysts, and more.
Production Deployment
Serving infrastructure using vLLM, Triton, or managed APIs with autoscaling, latency optimisation, and cost controls. On-premise and cloud deployment strategies.
Evaluation & Red-Teaming
Automated evaluation frameworks to measure accuracy, consistency, and safety. Adversarial testing for edge cases before and after deployment.
Monitoring & Observability
Production monitoring for model drift, latency degradation, cost anomalies, and quality regression. Dashboards and alerting built into every LLM system we ship.
Technologies We Work With
Framework-agnostic. We pick the right tool, not the trendy one.
Common Questions
When should we fine-tune an LLM vs use prompt engineering?
Prompt engineering should always be tried first — it is faster and cheaper. Fine-tuning is justified when you need consistent style or format, domain-specific knowledge not in the base model, significantly lower latency, or cost reduction at scale through smaller models. We help you make this decision based on your actual requirements, not hype.
Can we run LLMs on-premise without sending data to cloud providers?
Yes. Open-source models like Llama 3, Mistral, and Phi-3 can be deployed entirely on your infrastructure using vLLM or llama.cpp on your GPU servers, giving you full data control while maintaining production-grade performance. This is common in regulated industries like manufacturing and healthcare.
How long does an LLM development engagement take?
Dataset preparation and cleaning typically takes 1–2 weeks. A LoRA fine-tuning run on a 7B–13B model takes hours to days depending on dataset size and hardware. Full project timelines including evaluation and deployment range from 4–10 weeks depending on scope and complexity.
What is the cost difference between a fine-tuned model and GPT-4 API calls?
At high volumes, a fine-tuned smaller model running on dedicated infrastructure typically costs 80–90% less per inference than GPT-4. The upfront investment in fine-tuning typically pays back at roughly 500K–1M API calls, depending on query complexity and compute costs.
Do you work with regulated industries where data governance matters?
Yes. We have specific experience building LLM systems for manufacturing and education with strict data residency requirements. We deploy on-premise or within your VPC, ensuring no training or inference data leaves your environment.
Ready to move from prototype to production?
Tell us about your LLM project. We will tell you honestly what it takes to get it live.
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