Custom LLM applications built and evaluated properly.

Language-model applications built around a defined task, evaluated on real examples and engineered for dependable production use.

Discuss your use case AI overview

A model is not an application.

Getting an impressive demo from a language model takes an afternoon. Getting reliable results on your real inputs, at a sensible cost, with structured outputs your systems can use, takes engineering.

We build complete LLM applications: prompt and context design, retrieval, structured outputs, tool use, evaluation, integration, deployment and monitoring. We help you choose between hosted models, open-weight models and fine-tuning based on evidence rather than fashion.

What LLM development covers.

The complete application, not just the prompt.

Use-case design
The task, inputs, outputs and success criteria defined with the people who own them.
Model selection
Hosted and open-weight models compared on your examples for quality, speed and cost.
Structured outputs
Validated data formats your other systems can rely on.
Retrieval and tools
Access to your documents and approved actions, with permissions.
Evaluation suites
Representative test sets that measure quality before and after every change.
Fine-tuning
Model adaptation when evaluation shows it will close a real gap.

Measure first, then improve.

We build a representative set of examples, including awkward ones, with the expected result for each. That baseline lets us compare models and approaches objectively and catch regressions later.

It also reveals whether a problem is really the model, or poor retrieval, unclear instructions or missing data. We fix the part that is actually failing.

Representative test sets with expected results
Model comparison on quality, latency and cost
Regression checks on every change

Evaluation run

Test case	Expected	Result
Clean PDF invoice	Extract	Pass
Photographed delivery note	Extract	Pass
Two orders in one email	Split	Pass
Missing account number	Flag	Pass
Foreign currency total	Convert	Fix

Model AModel B

Illustration of the kind of interface we build, not a client system.

Production-ready from the start.

In production, an LLM application needs validated outputs, limited tools, sensible timeouts, fallbacks and logging. We engineer those in, and document the model versions and evaluation results so later changes can be compared fairly.

Where volumes or data sensitivity justify it, we can run open-weight models on infrastructure you control. See private AI.

Schema-validated structured outputs
Fallbacks and human review queues
Cost and latency monitoring

extract_order.py

result = client.extract(    document=email.attachments[0],    schema=PurchaseOrder,           # validated structure)if result.confidence < 0.85 or not rules.check(result):    review_queue.add(result, reason="needs a person")else:    erp.create_order(result.data)

Illustrative code, written for this page.

How a project works.

A clear sequence with review points, so you always know what is happening and what is needed from you.

Step 1
Find the use case
We identify a task with a clear input, a repeatable decision and a way to check the result.
Step 2
Prove it
A focused pilot tested on your real examples, with an honest view of accuracy, cost and limits.
Step 3
Build it properly
Integrations, permissions, monitoring and human review where the stakes call for it.
Step 4
Operate & improve
Evaluation, cost tracking and improvements as your data and needs change.

Working with a business in Worthing or Sussex?

Glenndilen is based in Worthing. Discovery, pilots and handover can happen in person locally, with the build and support handled remotely. The regional page explains how an engagement works.

AI in Worthing & Sussex Discuss your use case

Questions, answered.

Anything else you would like to know? Ask Glenn directly

Is fine-tuning the same as giving the model our documents?

No. Retrieval lets the model look up relevant information at the time of a request. Fine-tuning changes model behaviour through training examples. They solve different problems, and we evaluate which one your requirement needs.

Can you train a model from scratch?

Custom model training can be scoped as a research and development project. Feasibility depends on the target capability, data, compute and evaluation budget, which we define before committing.

Can you improve an LLM application we already have?

Yes. We review prompts, retrieval, tool use, test coverage and deployment to find the source of poor quality, latency or excessive cost.

Related services.

Tell us about the task you want to improve.

Describe the work, the documents or systems involved and what good would look like. Glenn will suggest a sensible first step, including when AI is not the answer.

Tell us about your project Browse our work

Prefer email? hello@glenndilen.co.uk

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We use your details only to reply to this enquiry. Privacy. Prefer email? hello@glenndilen.co.uk

Custom LLM applications built and evaluated properly.

A model is not an application.

What LLM development covers.

Use-case design

Model selection

Structured outputs

Retrieval and tools

Evaluation suites

Fine-tuning

Measure first, then improve.

Production-ready from the start.

How a project works.

Find the use case

Prove it

Build it properly

Operate & improve

Working with a business in Worthing or Sussex?

Questions, answered.

Related services.

AI development

Knowledge systems

Private AI

Technical consulting

Tell us about the task you want to improve.

Tell us what you have in mind

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