AI Data & Models

Model Engineering

Most enterprise use cases are best served by a capable general model with good retrieval. Some are not: narrow domains, high volume, strict latency or data that cannot leave your boundary. We handle those.

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The business problem

Fine-tuning is reached for too early

Teams fine-tune to fix problems that were retrieval or prompt problems, then own a model they must maintain, evaluate and re-tune whenever the base model moves. The cost is ongoing and rarely budgeted. Meanwhile the genuine cases for customisation, cost at volume, latency, offline operation and domain language a general model handles badly, get overlooked.

What we do

Establish whether it is warranted, then do it properly

We start by testing whether retrieval, prompting or a different base model solves the problem, because that is usually cheaper to build and to own. Where customisation is genuinely warranted, we build the dataset, run the training, and evaluate against a held-out set from your own cases. Serving is designed for your latency and cost envelope, with versioning and rollback, because a model in production is an artefact you have to be able to retreat from.

Model Engineering

Capabilities

Fine-Tuning
Model Customization
Embeddings
Model Optimization
Model Serving
Open-Source Models

Common use cases

Cut inference cost at volume by moving most traffic to a smaller specialised model.
Handle domain language a general model consistently gets wrong.
Run entirely within your own boundary where data cannot reach a third-party API.
Meet a latency requirement a hosted frontier model cannot satisfy.

How we deliver

Audit

Establish what data exists, who owns it, and what condition it is actually in.
Pipeline

Build the ingestion, transformation and quality checks the models depend on.
Benchmark

Compare approaches on your data rather than on a public leaderboard.
Serve

Deploy behind a stable interface with versioning, monitoring and a rollback path.

Technology

Python
dbt
Apache Airflow
Snowflake
Databricks
PostgreSQL
pgvector
PyTorch
Hugging Face

Security & governance

Data lineage is recorded end to end, so it is always answerable where a value came from and which model version produced a given output. Personal data is minimised, classified and retained under an explicit policy rather than by default. Training and evaluation sets are versioned alongside the code that uses them.

Engagement models

AI Project

We take responsibility for designing and delivering a defined AI solution.

Dedicated AI Team

Long-term dedicated engineering capacity built around your stack and delivery model.

Managed AI

We operate, monitor and continuously improve production AI systems.

Why TeamExtension.ai

We will usually talk you out of it

Customisation is warranted less often than it is proposed, and we would rather say so than sell it. When it is warranted, the work is unglamorous: dataset construction, honest evaluation and a serving path you can operate. That is the part that determines whether it holds up.

Selected clients

Frequently asked questions

How much data do we need to fine-tune?

Far less than commonly assumed for style and format, often a few hundred well-chosen examples. Teaching genuinely new knowledge is a different problem and is usually better solved with retrieval.

Will a fine-tuned model beat a frontier model?

On a narrow task, sometimes, and usually at much lower cost and latency. On general reasoning, no. The realistic pattern is a smaller model handling the bulk with escalation for hard cases.

What happens when the base model is updated?

Your fine-tune does not move with it, so you re-evaluate and often re-train. That ongoing cost is part of the decision and we make it explicit before you commit.

Can we run open-source models ourselves?

Yes, and for data residency or isolation it is often the reason to. It means operating inference infrastructure, which has real cost, so it should be a deliberate choice rather than a default.

Related capabilities

AI Data & Models

AI Data Engineering

The pipelines, quality controls and vector stores that AI systems depend on to be correct.

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AI Infrastructure & LLMOps

LLMOps

Deploy, monitor, evaluate and cost-control language models once they are carrying real traffic.

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AI Applications & Knowledge

RAG Development

Retrieval systems that ground answers in your own content, with citations and measurable accuracy.

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Discuss Your AI Initiative

Fine-tuning, embeddings, optimization and serving, including open-source and self-hosted models.

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