LLM development services built for production
Calling an LLM API takes an afternoon. Making it answer correctly, consistently and cheaply for thousands of users is the real work. Our LLM development services cover the whole lifecycle — model selection, data pipelines, prompting, fine-tuning, deployment and evaluation — so your feature behaves the same way on day 300 as it did in the demo.
Choosing the right large language model
There is no single best model. Frontier models from OpenAI, Anthropic and Google are strongest at complex reasoning and long documents; smaller and open-weight models such as Llama, Mistral or Qwen are often faster and far cheaper for classification, extraction and routine drafting. We benchmark candidates on your actual tasks and frequently route requests between models — simple queries to a small model, difficult ones to a larger one.
RAG or fine-tuning?
These two techniques solve different problems, and many teams reach for fine-tuning when what they actually need is retrieval.
- Retrieval-augmented generation (RAG) gives the model access to your knowledge — policies, manuals, contracts, product data — at the moment a question is asked. It's the right choice when information changes often or answers must cite a source.
- Fine-tuning changes how a model behaves: its tone, output format or skill at a narrow task. It's useful for consistent style, specialist terminology, or cutting costs by teaching a small model to do what a large one does.
We often combine the two, and we use hybrid search (keywords plus embeddings), re-ranking and metadata filters so retrieval is genuinely accurate rather than merely "semantic".
Private LLM deployment
For banks, healthcare providers, law firms and government bodies, sending data to a public API may not be acceptable. We deploy open-weight models in your own cloud account or on on-premise GPU servers using inference engines like vLLM, or through region-specific managed services such as Azure OpenAI and AWS Bedrock. Data never leaves the environment you control, and our data security team reviews access, encryption and logging before launch.
Evaluation, guardrails and observability
Every project ships with an evaluation set: real questions with expected answers, scored automatically on each change to prompts, data or model version. In production we trace each request, track hallucination and refusal rates, redact personal data and block unsafe outputs. That discipline is what lets you upgrade to a newer model with confidence instead of hoping nothing broke.
Where LLMs fit in your roadmap
LLM integration is the foundation for several of our other services: customer-facing AI chatbots, autonomous AI agents and workflow automation, and content tools built through generative AI development. If you're not sure where to start, our broader AI development services begin with a short discovery phase to find the highest-value use case.
See examples of our work in our case studies, or talk to an LLM engineer about your project.