Smaller, cheaper AI models trained on your own examples to do one job well and fast.
Stack we work in
Figures as of Oct 2026
Trusted by
When a general model is too slow, too expensive or not accurate enough for a specific task, we train a model on your own examples so it gets that task right more often, at a lower cost per request.
We fine-tune open models such as Llama, Qwen, Gemma, Phi and Mistral with LoRA and QLoRA, distill large models into small ones that run on a single GPU or on-device, and use hosted fine-tuning on OpenAI, Amazon Bedrock or Google Vertex AI where that fits better. Every model is measured against the base model on your own test set before it ships.
Prompting and RAG solve most problems. These are the cases where training a model is worth it.
A large hosted model on every request makes a high-volume feature expensive to run.
The model ignores your format, tone or rules often enough that someone has to check every answer.
Response times from large models are too long for real-time or in-app use.
Security or contracts rule out sending data to an external model API.
The model does not know your terminology, product codes or decision patterns.
Outcomes
Baseline scores for prompting, RAG and fine-tuning options, with a recommendation and cost estimate.
Cleaned, versioned data with documentation of sources, licenses and personal data handling.
LoRA adapters or merged weights, quantized builds where needed, stored in your registry.
Quality, safety, latency and cost compared with the base model and a hosted model.
Inference endpoint on vLLM, SGLang or Ollama with autoscaling and monitoring, defined in code.
Scripts and a runbook to add new examples, retrain and re-evaluate before each release.
Route, tag and prioritize high volumes of messages at a fraction of large-model cost.
Pull fields from invoices, contracts or forms into a fixed JSON schema every time.
Draft product descriptions, replies or summaries in your house style.
Models that understand legal, medical, financial or technical language used in your field.
Private assistants and features that run on laptops, phones or edge hardware without a network call.
Small models tuned to call your tools and APIs reliably inside an agent workflow.
Make sure fine-tuning is the right tool before spending on it.
Train adapters instead of whole models to keep cost and risk down.
Built with
Get large-model quality from a model you can afford to run.
Built with
Prove the tuned model is better, not just different.
Put the model into production and keep it current.
Built with
Prompting and RAG are tested on your task first. If one of them beats fine-tuning on quality and cost, we say so and you keep the budget.
Every tuned model is scored on a task-specific test set against the base model and a hosted model, with latency and cost per 1,000 requests on your target hardware.
Adapters and weights belong to you. Training data is checked for personal data and licence terms before use, and work runs under NDA.
We deploy with vLLM, SGLang or Ollama on your cloud or GPUs and set up a retraining pipeline, so the model keeps improving after handover.
Featured case study
Every business already owns the sensor network it needs. CCTV records everything and tells you nothing — until someone reviews the footage, usually days after the loss.
Read the case study
We define the task, collect real examples and measure how well prompting and RAG already perform.
You receive Baseline scores and go or no-go
We build and clean the training and test sets with your subject experts, and remove personal data.
You receive Versioned dataset
We shortlist base models by license, size, language support and hardware budget.
You receive Model shortlist and cost estimate
We train adapters or distilled models, tracking every run, setting and score.
You receive Tuned model candidates
Candidates are compared with the base model on quality, safety, speed and cost.
You receive Evaluation report
The chosen model is served, monitored and retrained as new reviewed data arrives.
You receive Production model and retraining plan
Tech stack
AI and machine learning
Languages
Cloud and DevOps
Other tools
Engagement
A short fixed-price study that tests prompting, RAG and fine-tuning on your task before you commit.
Data, training, evaluation and serving delivered against an agreed scope.
Engineers who join your team to run training, evaluation and retraining over time.
Cost depends on how much training data exists and how clean it is, the model size, the number of tasks, how strict the evaluation needs to be and where the model will run. GPU time is billed to your own cloud account or estimated separately, and for small models it is usually a small part of the total. As a guide, a fine-tuning pilot on one task usually costs $15,000 to $40,000, and a production fine-tuned or distilled model with serving, evaluation and retraining $40,000 to $120,000 or more (USD).
Start with prompting and RAG. If the model needs facts from your documents, RAG is usually the answer. Fine-tuning helps when the model must follow a format, tone or decision pattern reliably, when you need a smaller and cheaper model, or when data cannot leave your network. Many systems combine a tuned small model with RAG.
A model with roughly 1 to 15 billion parameters, such as Phi, Gemma, Llama 8B, Qwen or Mistral Small. Once tuned for one task, a small model can match a much larger model on that task while running faster and far cheaper, often on a single GPU, a laptop or a phone.
For format and style tasks a few hundred good examples can be enough. Classification and extraction usually need one to a few thousand. Quality matters more than volume, so we spend time cleaning and reviewing data, and can generate synthetic examples that your experts check.
They train a small set of extra weights, called an adapter, instead of the whole model. That cuts GPU memory and cost, and lets one base model serve several tasks by swapping adapters. QLoRA does the same on a compressed (4-bit) model, so larger models can be tuned on modest hardware.
You do. Adapters, merged weights, datasets, training code and evaluation results are delivered to your repositories and storage. We check the base model license (for example Llama, Gemma or Apache 2.0 models) fits your commercial use before we start.
Yes. Small tuned models can be quantized and served on your own GPUs, inside a private cloud network, or on laptops, phones and edge devices with llama.cpp, Ollama or ONNX Runtime.
We build a test set from real cases before training and score the base model, the tuned model and a large hosted model on it. We also check safety behavior and general ability did not get worse. The report shows quality, speed and cost side by side.
Training runs in your cloud account or ours under a data agreement, with personal data removed or masked first. Data is encrypted at rest and in transit, access is limited to named engineers, and we design the process to support GDPR.
A pilot on one task usually takes 3 to 6 weeks, depending on how ready the data is. A production model with serving and a retraining pipeline typically takes 6 to 12 weeks.
Book a call, and we talk through the goal, the systems involved and any constraints under NDA. You then get a short written proposal with scope, timeline and estimate. The first step is a fit check: we baseline prompting and RAG on your task, then recommend whether fine-tuning is worth it.
We monitor quality, latency and cost, add newly reviewed examples to the retraining pipeline on a schedule, and re-run the evaluation set after every retrain. Ongoing support is available as a monthly agreement.
Book a call. We will look at your task and data and tell you whether fine-tuning or a small model will pay off.
