LLM Fine-Tuning Development Services in the USA
HERE AND NOW AI helps US companies adopt Generative and Agentic AI with production-grade engineering and a clear focus on ROI, security and compliance.
Your data, your domain, your model — tuned for enterprise-grade accuracy.
Off-the-shelf models know the internet; they don't know your domain. We fine-tune large language models on your data — instruction tuning, LoRA/QLoRA parameter-efficient training, and domain adaptation of open-weight models like Llama, Mistral and Gemma — so the model speaks your terminology, follows your formats and stays inside your compliance boundaries.
From venture-backed startups to established enterprises, we deliver AI systems that meet US expectations for data security, reliability and measurable business impact.
Fine-tuning is an engineering discipline, not a button: we start with an honest RAG-vs-fine-tuning assessment (often the answer is both), build clean training datasets from your real interactions, train with rigorous held-out evaluation, and ship the tuned model behind a production endpoint — on your cloud, on-premise, or serverless — with monitoring and versioning.
$7,400
one-time · 6–10 weeks
$455/mo
hosting, monitoring & tuning
LLM Fine-Tuning that deliver measurable value
Domain accuracy generic models can't match
Tuned on your vocabulary, formats and edge cases, the model stops sounding generic and starts sounding expert.
Smaller, faster, cheaper
A fine-tuned small model often beats a giant general model on your task — at a fraction of the latency and cost.
Your data stays yours
Training runs in your cloud or ours under strict controls; the resulting model weights belong to you.
Measured, not assumed
Every run ships with an evaluation suite that scores the tuned model against the base model on your real tasks.
A clear path to production
Feasibility & strategy
We assess whether fine-tuning, RAG or both fits your goal, and define measurable success criteria.
Data curation
We build and clean the training set from your documents, transcripts and interactions — quality over volume.
Train & evaluate
Parameter-efficient training with held-out evals against the base model on your actual tasks.
Deploy & monitor
We serve the tuned model on your infrastructure with versioning, drift monitoring and rollback.
We provide dedicated overlap hours across all US time zones, from Eastern to Pacific.
LLM Fine-Tuning in other regions
LLM Fine-Tuning in the USA — questions answered
Yes. HERE AND NOW AI delivers llm fine-tuning to businesses across the USA, including New York, San Francisco, Austin, Seattle, Boston, Chicago. We provide dedicated overlap hours across all US time zones, from Eastern to Pacific.
They solve different problems. RAG injects fresh knowledge at question time; fine-tuning changes how the model behaves — tone, format, domain reasoning, terminology. Knowledge that changes weekly belongs in RAG; skills and style belong in fine-tuning. Many production systems we build use both, and we assess this honestly before any training run.
Less than most teams expect. With parameter-efficient methods like LoRA, a well-curated set of 500–5,000 high-quality examples often outperforms tens of thousands of noisy ones. We help you extract and clean that dataset from support tickets, documents and past interactions.
Open-weight models such as Llama, Mistral, Gemma and Qwen — which you can own and host anywhere — plus provider fine-tuning APIs where they fit. We recommend the smallest model that meets your quality bar, because it's cheaper and faster in production.
Build your llm fine-tuning with a team that ships
Book a free consult with our team serving the USA. We'll scope your highest-ROI use case and a clear path to launch.