Custom Model Training & Fine-Tuning

Open models fine-tuned on your data, so they know your domain, run on your hardware, and stay yours.

Discuss this See example work

Time to value: Feasibility in 2 weeks; trained and serving in 6–10.
Deployment: Can run fully on your infrastructure
Category: Cloud, Integration & AI Infrastructure

A rack of GPU server equipment and cabling lit in green light

The manual version of this

Invictt AI layer

Check it is worth it

Build the dataset

Train

Evaluate against the incumbent

Serve

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Once it's AI-assisted

01/The problem

Frontier APIs are excellent generalists and expensive specialists. When a task is narrow, repetitive and high-volume, a small model you own is usually faster, cheaper and considerably easier to defend in a compliance review.

02/How it works

The operating model

What enters, what the system does with it, and what reaches you.

Check

Check it is worth it

First we try to solve it with prompting and retrieval. If that works, we say so and you save the training budget. Fine-tuning is a tool, not a default.

System

Build the dataset

Usually the real work: extracting, cleaning, deduplicating and splitting your data, with an honest look at label quality.

System

Train

LoRA or full fine-tuning on open models, tracked so every run is reproducible and comparable rather than a folder of checkpoints nobody can tell apart.

Check

Evaluate against the incumbent

Measured head-to-head against the frontier API on your own task, on quality, latency and cost per thousand calls.

Integration

Serve

Deployed on vLLM on your hardware or a private cloud, with quantisation where it does not cost accuracy.

Read the detail behind each stage

Check it is worth it

First we try to solve it with prompting and retrieval. If that works, we say so and you save the training budget. Fine-tuning is a tool, not a default.

Build the dataset

Usually the real work: extracting, cleaning, deduplicating and splitting your data, with an honest look at label quality.

Train

LoRA or full fine-tuning on open models, tracked so every run is reproducible and comparable rather than a folder of checkpoints nobody can tell apart.

Evaluate against the incumbent

Measured head-to-head against the frontier API on your own task, on quality, latency and cost per thousand calls.

Serve

Deployed on vLLM on your hardware or a private cloud, with quantisation where it does not cost accuracy.

03/What's included

What you actually receive

Concrete deliverables, not a statement of intent.

Honest prompting-and-retrieval attempt before any training

Dataset construction, cleaning and label-quality review

LoRA / QLoRA or full fine-tuning with tracked experiments

Head-to-head evaluation against frontier APIs on quality, latency and cost

Quantisation and serving on your own hardware

Retraining pipeline and model versioning

The weights are yours, with no ongoing licence to us

Built with

PyTorch
LoRA / QLoRA
Hugging Face
Weights & Biases
vLLM

Tooling is chosen per engagement. This is what this kind of build typically uses, not a fixed stack we sell.

04/Example work

All work

What this looks like in practice

Reference implementations, including what went wrong.

Reference Implementation2025

A fine-tuned model that replaced an expensive frontier API on the line

A frontier vision API classified fabric defects accurately and cost more than the QA salaries it supplemented. We fine-tuned a small open model on the client's own inspection archive and put it on the line.

Read it

05/Also in this category

Cloud, Integration & AI Infrastructure

What lets everything above run safely, and keep running.

ConnectSecureScale

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MCP integrations

Private MCP Integrations

A safe, audited bridge between your internal systems and your AI tools — so agents can act on real data without a blank cheque.

Inventory → Scope → Operate

DetailsDiscuss this

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Cloud deployment

Cloud-Based AI Deployment

Managed model hosting, frontier API integration, and hybrid architectures that use the cloud where it is the right answer and keep the sensitive parts on-prem where it is not.

Classify the workload → Integrate behind an abstraction → Control cost and exposure

DetailsDiscuss this

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Content generation

AI Content Generation

Text and image production pipelines that hold your brand's voice across real volume.

Encode the voice → Ground the facts → Review

DetailsDiscuss this

Is this the problem you have?

Tell us what it looks like at your end. We will say honestly whether this is the right fit — including when it is not.

Ask about Model training