erudogix.
AI & LLM development

Your use case.
A model with direction.

LLM fine-tuning, data preparation, and evaluation support. Start with the task you need to improve, then build an approach you can test and understand.

Discuss your LLM project
Illustrative developer reviewing code and application data

Prepare useful data.

Review the task, data quality, and examples the model needs. Structure training and evaluation sets, remove duplication, and document how the data will be used.

Adapt with a purpose.

Choose an appropriate base model and adaptation approach for the agreed task. Fine-tuning can help align behavior, response structure, or domain-specific patterns.

Evaluate the difference.

Compare the adapted model with a baseline using held-out examples. Inspect errors and limitations so decisions rest on observed results rather than assumptions.

From a question to an experiment

A clear process.
Evidence at each step.

Fine-tuning isn’t the starting assumption. We consider prompting, retrieval, and model adaptation against your actual goals and available data.

A useful brief includes the task, sample inputs and ideal outputs, current problems, intended users, and the environment where the model will run.

  1. 01

    Define the task

    Identify the behavior you want, the constraints, and the examples that will show whether the approach works.

  2. 02

    Review & prepare data

    Organize training examples and separate evaluation data. Review dataset permissions and remove sensitive material that should not enter training.

  3. 03

    Establish a baseline

    Test the current model or a simpler approach before deciding whether adaptation adds value.

  4. 04

    Train & iterate

    Run the agreed experiments, review representative outputs, and track configuration and dataset versions.

  5. 05

    Evaluate & integrate

    Compare results, document failure cases, and connect the selected approach to an application or deployment plan.

Where this can help.

Consistent response formats

Explore models that follow a defined response structure for tasks such as categorization, structured extraction, or domain-specific support. Evaluation checks whether the desired behavior improves.

Knowledge assistants

When the main need is access to changing documents, a retrieval workflow may be more suitable than fine-tuning alone. We can help investigate retrieval, response generation, and evaluation together.

A practical project handover

The agreed scope may include prepared datasets, training configuration, evaluation results, integration code, and notes on limitations and operating requirements. Model licenses and infrastructure requirements are reviewed for the selected approach.

What outcomes can you expect?

The goal is a measured improvement on a defined task. Quality depends on the base model, data, and evaluation design; a particular accuracy score or universal improvement is not guaranteed.

Start with the task, not the model.

Tell us what your users need and what your current approach struggles with.

Plan an LLM project

Workspace imagery is illustrative. This service is separate from student dissertation mentoring.