Agent and LLM consulting, for the models you call and the models you run.
We integrate Claude and GPT, run open-weight models on hardware you control, fine-tune them on your data, and measure every option on your own traffic.
One question comes first: what does this job need?
Many projects need good instructions and access to the right documents, and no trained model at all. When that is the answer, we say so.
Four kinds of work
Claude and GPT integration
Hosted models in production: tool use, MCP, structured output, caching, routing between model sizes and fallback between providers.
Private and local LLMs
Open-weight models such as Llama, Qwen, Gemma, DeepSeek, gpt-oss and Nemotron, served on your servers or private cloud.
Fine-tuning and training
LoRA and QLoRA, full fine-tunes, preference tuning, reinforcement-style tuning and distillation, chosen by the data you have.
GPU infrastructure and benchmarks
NVIDIA GPUs on premise or rented, or Google Cloud TPUs, chosen by measuring throughput, latency and cost on your traffic.
Choosing the approach
Instructions and tools
Start here. Clear instructions, worked examples and tool definitions solve more than most teams expect, and they cost nothing to change.
Retrieval
When the answer lives in your documents or database and changes often. The model reads the relevant records at the moment it answers.
Fine-tuning
When the model must follow a format, a tone or a decision pattern every time, and you have enough examples of the work done right.
Training
Rarely the answer. It pays for a language, a field or a vocabulary the base models barely know, backed by a large body of text you own.
Agents
When the job takes several steps across your systems. The model plans, calls tools and checks the results within permissions you set.
How an engagement runs
A written recommendation, priced on its own
Which approach, which model, where it runs and what it will cost to operate, with the evidence behind each choice.
The candidates measured on your traffic
Two or three models or engines run on your own prompts, with quality, latency and cost per request side by side.
The system, in stages you approve
Integration, serving, fine-tuning or the agent itself, each stage quoted and accepted on its own.
Support after go-live
Model updates, evaluation reruns and cost reviews under a monthly agreement, with the response time written into the contract.
Questions buyers ask
Do we need to fine-tune a model?
Often not. We test instructions and retrieval first, and recommend fine-tuning only when an evaluation on your data shows it closes a gap they cannot.
Can our data stay inside our network?
Yes, with an open-weight model on your own servers or private cloud. With Claude or GPT, we set up the vendor's data options and tell you what each one covers.
Who owns a fine-tuned model?
You do: the tuned weights, the training data and the evaluation set, within the terms of the base model's licence.
Which model should we use?
The one that passes your evaluation at the lowest running cost. We build the evaluation from your own cases before any model is compared.
Bring one task you want a model to do.
Tell us what it is and what a good answer looks like. The work starts with an evaluation built from your cases.
Talk to us