LLM Integration & Fine-tuning
Embed GPT-4o, Claude, Gemini, or open-source LLMs into your product — and fine-tune them on your proprietary data to achieve domain-specific accuracy, brand voice, and significantly lower per-query token costs.
From API call to production AI feature
Calling an LLM API in a prototype is straightforward. Building a reliable AI feature in a production product is significantly harder — it requires prompt engineering that's stable across model version updates, a caching layer to avoid redundant API calls, rate limit handling with exponential backoff, cost monitoring to prevent runaway spend, and an evaluation framework to catch regressions when you update prompts or upgrade models. We build all of this, not just the API call.
For more complex use cases, we architect multi-agent pipelines where specialized LLM agents handle distinct subtasks — a routing agent classifies the request, a research agent retrieves relevant context, and a synthesis agent generates the final response. This division of labour produces higher-quality outputs than a single monolithic prompt while keeping each agent's scope narrow enough to evaluate and improve independently.
Fine-tuning: when and why it's worth it
Fine-tuning is not always the right answer, and we'll tell you honestly when it isn't. RAG (retrieval-augmented generation) is better for knowledge that changes frequently, since you simply update the document store without retraining. Fine-tuning is the right choice when you need a specific output format the model can't reliably produce from prompts alone, when you need specific domain terminology baked in permanently, or when you're processing enough volume that reducing prompt length via fine-tuning produces meaningful cost savings.
We use LoRA (Low-Rank Adaptation) and QLoRA for efficient fine-tuning that can be done on a single GPU with as few as 500–2,000 high-quality examples. The process starts with a baseline evaluation on the vanilla model, followed by dataset curation and cleaning, fine-tuning runs with hyperparameter sweeps, and a final evaluation against the baseline. You receive the model weights, training code, and dataset — so you can retrain independently as your data grows.