Data & NLP Pipelines

Pipelines that clean, structure and make sense of messy data. Master data cleaning and clustering, NLP work such as sentiment analysis, named entity recognition and chunking, plus classic machine learning and neural networks built from scratch where the problem calls for it.

Turn messy master data into clean, clustered records you can actually group and act on.

Extract structure from free text: entities, sentiment, topics and meaningful chunks.

Apply the right method for the problem, from statistics and classic ML to a purpose-built neural network.

Look at the data first

Understand what is actually in the data, how dirty it is and what question it needs to answer, before selecting any method.

Clean and structure

Build the cleaning, clustering, extraction and chunking steps that turn raw records and free text into something usable downstream.

Model only where it earns its place

Statistics and classic machine learning solve more problems than people expect. Deeper models are used when the problem genuinely requires them.

What kind of data work have you done?

Cleaning and clustering master data to derive product groups, building a neural network from scratch to recognise handwritten digits, and NLP work inside chat and text systems including sentiment analysis, named entity recognition and chunking.

Do you only do deep learning?

No. A good share of the work is statistics and classic machine learning, which is often the correct and more maintainable answer. Neural networks are used where the problem actually needs them.

How does this connect to your LLM work?

Directly. Chunking, entity extraction and retrieval quality are what decide whether a RAG system gives a useful answer. The data pipeline is usually where an LLM system is won or lost.