We integrate large language models into your application and make the calls that decide whether it works in production — prompt engineering, retrieval, or fine-tuning, measured against evals rather than vibes.
Currently accepting new engagementsDropping an LLM into a product is easy. Making it reliable, affordable, and safe enough to put in front of customers is the hard part. LLM Integration & Fine-Tuning is a focused engagement to get language-model capabilities into your product the right way — choosing the simplest approach that meets the quality bar, proving it with evaluation data, and hardening it against the failure modes that erode user trust.
We start from the decision most teams get wrong: prompt engineering and retrieval-augmented generation versus fine-tuning. Each has a different cost, latency, and maintenance profile, and the right answer depends on your data and quality targets — not on what is fashionable. From there we build the evals, guardrails, and cost and latency controls that let you ship with confidence and keep improving after launch.
Related resource — explore our Enterprise AI Model Map: which model to route each workload to, and why.
A clear, evidence-backed decision on prompt engineering, RAG, or fine-tuning for your use case, with the cost, latency, and maintenance trade-offs of each spelled out rather than assumed.
A working integration — retrieval pipeline and vector store, or a fine-tuned model and serving path — wired into your application behind clean, versioned interfaces.
A labeled eval set and automated scoring that quantify output quality, catch regressions, and turn "it seems better" into a number you can track release over release.
Input and output validation, safety filters, fallbacks, prompt-injection defenses, plus caching, routing, and token strategies that keep spend and response times inside budget at scale.
We define the quality bar, review the data available for retrieval or training, and set explicit targets for accuracy, latency, and cost per request before writing a line of integration code.
We build a quick baseline — usually strong prompting plus retrieval — and measure it against the eval set, so the choice to invest in fine-tuning is made on data, not instinct.
We implement the chosen approach and tune it against the evals, refining prompts, retrieval, or training data until quality, latency, and cost all clear their thresholds.
We add guardrails, fallbacks, monitoring, and cost controls, then wire the capability into your product behind stable interfaces so models can be upgraded without disruption.
Start with a free discovery call — a quick chat to pinpoint where AI can create value in your business and map the smartest first step.