Custom AI Models

AI models trained on your business

Custom LLM training that teaches a model your terminology, style and decisions, then runs it privately in your cloud.

Assess my data →How it works

1,000+ examples to startPrivate weights you own4-bit fast inference

96% accuracy

▤Curate4,200 pairs⟳TrainLoRA⚖Evaluate94%▲Deployprivate

The difference

Generic models guess. Trained models know.

Generic base model

Generic answers

Classify this claim: water damage, policy HX-2.

This appears to be an insurance claim. Please check your policy.

Which team should handle it?

I don't have access to that information.

Rework

Inside a training run

0% accuracy

Curate4,200 pairs

TrainLoRA

Evaluate94%

Deployprivate

Illustrative accuracy on a held-out test set

01 · Curate

We build the dataset

Your tickets, documents and approved answers become clean training pairs, with personal data removed.

Base model baseline: 72%

02 · Train

It learns your domain

Parameter-efficient LoRA training teaches your terminology and style without retraining billions of weights.

"Water damage" → "escape of water"

03 · Evaluate

We test before trusting

A held-out test set and red-team prompts measure accuracy, tone and safety against the baseline.

94% vs 72% baseline

04 · Improve

It keeps getting better

Quantised for fast inference in your cloud. Reviewed feedback feeds each new training round.

Round 2 · 412 new reviewed pairs

Benchmark

Specialist knowledge, at model speed

Gen7Ven fine-tuned modelsmall, quantised, private

<300ms

Large general modellong few-shot prompt every call

2–4s

Specialist staffmanual review and drafting

5–20 min

Use cases

Where a trained model beats a generic one

Legal and compliance

Drafts in your house style using approved clauses.

Support replies

Answers that match your best agents.

Classification

Claims, tickets and documents routed by category.

Private deployment

Open-source models running inside your own cloud.

Built in

Your model, your data, your cloud

LoRA fine-tuning

Efficient training that needs far less data and compute.

Data curation

We turn your logs and documents into training pairs.

Evaluation suite

Accuracy, tone and safety measured before every release.

Private weights

You own the model, and it runs in your environment.

In depth

LLM fine-tuning services for specialist work

General-purpose models are impressive but generic. Custom LLM training adapts an open-source model to your terminology, document formats and decisions, so it performs like a specialist rather than a well-read generalist. The result is a model you own, can host privately and can keep improving with your own feedback.

Train AI on company data, safely

Training starts with data, and most organisations already have more than they realise: resolved support tickets, approved email replies, contracts, reports and internal guidance. We help you select the most representative examples, clean them and convert them into question-and-answer or instruction pairs. Personal data is removed or pseudonymised before training, and the dataset is versioned so every model can be traced back to exactly what it learned from. Where examples are scarce, we can generate synthetic training data from your documents and have your experts review it before it is used.

Parameter-efficient fine-tuning with LoRA

Rather than retraining every weight in a model, we use parameter-efficient methods such as LoRA and QLoRA, which train small adapter layers on top of a base model such as Llama or Mistral. This needs far less compute and data: meaningful gains are often possible with 1,000 to 5,000 high-quality examples. Adapters are quick to retrain, so the model can be refreshed as your products and policies change. We select the base model by testing several candidates on your tasks, balancing accuracy against size and running cost.

Private LLM deployment you control

Because the model is yours, it can run wherever your data must stay, whether in your own cloud account, a private data centre or a dedicated GPU instance. We quantise trained models to 4-bit or 8-bit precision, which reduces memory needs and allows fast responses on modest hardware. Deployment includes monitoring for accuracy drift, usage and latency, plus a simple process for your team to flag poor answers. Those flags become reviewed examples for the next training round, so the model steadily improves.

Fine-tuning or RAG: choosing the right approach

Fine-tuning and retrieval-augmented generation solve different problems. RAG is best when answers depend on facts that change often, such as prices, policies or stock levels, because the model reads the latest documents at query time. Fine-tuning is best for teaching behaviour: a specialist vocabulary, a house style, a consistent output format or a classification scheme. Many of the strongest systems combine both, with a fine-tuned model reading retrieved facts. Our discovery phase tests both options on your data before recommending one.

Plugs into

Models and tooling we train with

MetaOllamaHugging FacePyTorchWeights & BiasesPythonDockerKubernetesGoogle CloudPostgreSQL

FAQ

Custom AI Models: common questions

How much data do we need to fine-tune an LLM?

Less than most teams expect. With parameter-efficient methods such as LoRA, useful improvements are often possible with 1,000 to 5,000 high-quality examples. Quality matters more than volume: consistent, accurate examples outperform large, noisy datasets. We help you curate them from existing tickets, documents and approved replies, and can generate reviewed synthetic examples where gaps exist.

Why fine-tune when we could use RAG?

They do different jobs. RAG supplies a model with current facts at the moment of answering, which suits changing information such as policies or prices. Fine-tuning changes how the model behaves: its vocabulary, tone, format and judgement in your domain. If you need both accurate facts and specialist behaviour, we combine the two approaches in a single system.

Can we host the fine-tuned model ourselves?

Yes. You own the trained weights, and we can deploy them in your own cloud account, on-premise servers or a dedicated GPU instance. Models are quantised to run efficiently on modest hardware, and we set up monitoring, scaling and update procedures so your team can operate the model confidently after handover.

How long does custom LLM training take?

It depends mainly on the volume and quality of your data. A first model moves through discovery, data curation, training, evaluation and deployment, and data preparation is usually the longest step. Once the pipeline is in place, later training rounds are much faster because new reviewed examples can be added and the adapters retrained.

Which base models do you fine-tune?

We mainly work with open-source models such as Llama and Mistral, chosen by testing several sizes on your tasks. Smaller models are cheaper and faster to run; larger ones handle more complex reasoning. Where a hosted provider offers fine-tuning and privacy requirements allow it, we can also fine-tune commercial models.

Custom AI Models

Teach a model how your business works

Tell us the task and the data you have. We'll assess whether fine-tuning, RAG or both will pay off.

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