Advanced companies turn proven AI use cases into a competitive advantage by making their models better. Prompting and context engineering lay the foundation. Fine-tuning builds deeper expertise in your domain, your tools and the work your customers care about.
Prompts and context engineering will only take you so far. For companies with established use cases, post-training develops the model itself: stronger domain judgment, more reliable behavior and expertise that grows with your product.
PyTorch
Hugging Face
NVIDIA
MLflowA customer asks about an order. Watch two agents use the same tools and data, and see what changes when one has trained for the job.
General-purpose tool use
Nike Air Force 1 · Size 9
ShippedTrained for these tools and workflows
Nike Air Force 1 · Size 9
ShippedIllustrative scenario, scripted decision summaries and simulated metrics. Fictional storefront; logos identify the tools and framework shown. Both agents receive the same tools, data and validation. Model cost uses an illustrative $2 per million tokens on both sides, covering inference only; training and tool fees are excluded. Actual gains and prices vary.
Explore the signal behind each technique. Follow real data formats through the training loop, then choose what fits your use case.
Choose a technique
Choose a tab to explore its training flow.
Teach the model what a good response looks like.
Natalia sold clips to 48 of her friends in April, and then she sold half as many clips in May. How many clips did Natalia sell altogether in April and May?
48 / 2 = 24 48 + 24 = 72 #### 72
Use reviewed responses, structured outputs or successful tool traces. This viewer shows a real GSM8K training example.
record = {"prompt": question, "completion": answer}Design consideration: The quality and coverage of your examples set the ceiling. Hold out whole tasks and customers when evaluating.
Where do LoRA and QLoRA fit? They change which parameters you train and how much memory you need. They can support several of these objectives; they are not a separate source of supervision.
Read the illustrated guideBuild a repeatable improvement loop around the capabilities, response times and economics that matter to your customers.
From industry insight to a model that performs in your production environment.
Use our data partnerships, your product signals and expert review to curate training examples and independent evaluation sets around real user needs.
Teach your terminology, output formats and domain decisions through supervised fine-tuning and preference optimization on suitable models.
Train and evaluate multi-step workflows with your tool schemas, feedback and task rewards. Improve tool selection, argument accuracy and recovery behavior.
Curate and validate a larger teacher model’s outputs, then train a smaller specialist on those examples. Benchmark how much task quality it retains alongside latency and cost.
Track task success, tool-call accuracy, tokens and time to completion on held-out tasks. Feed reviewed production examples into the next training cycle.
Serve supported custom models through managed APIs or deploy open-weight models on your cloud or on-premises GPUs, aligned with your data and operating requirements.
Define success, curate the data, train a specialist and validate the gains in your application.
A high-impact strike team that diagnoses, architects, and ships. We bring the ML engineers, infra, and domain expertise needed to deliver measurable lift within weeks.
A dedicated research partnership where we co-develop proprietary models alongside your team — from initial hypothesis through production deployment.
A controlled experimentation environment for validating strategies before they touch production. Test against realistic system dynamics and quantify impact upfront.
Bring a use case. We’ll map the data, benchmarks and training path to a stronger model.