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GPT-4o fine-tuning
for customer
live chat.

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Two-dimensional projection of product names, short and full descriptions, and company information embeddings

Behind the visual

A map of the AI’s knowledge.

I implemented the knowledge base in Python as a JSONL dataset, combining product information with company policies and staff training documents.

The below graph shows the position vectors, each with 1536 dimensions, projected onto a two-dimensional plane that preserves 20.6% of the variance.

How I built it

Projects

Selected work.

Projection of product and policy embeddings
01Machine learning·4 chapters

GPT-4o fine-tuning
for customer live chat

I built a Python application that combined the fine-tuned model with retrieval of product and policy information.

74,000+messages parsed
Generated bee and honeycomb relief on a clay roller model
02Automation·Python + Blender

From artwork to
3D-printable rollers

I built a Blender pipeline in python that could convert a folder of designs into marketable 3D assets automatically.

£1,100+in product sales
Maze from the route-memory controller project
03Algorithms·Java

A maze solver
that remembers its route

The final controller could remember a successful route and use it to reach the target more directly on subsequent runs.

85% & 87%coursework marks
Frederick Searancke

Frederick Searancke

Quant Researcher Computer Science, University of Warwick

About me

Technical toolkit

Skills in practice.

Machine learning

  • EBM model training
  • Loss functions & regularisation
  • Overfitting & underfitting diagnostics
  • LLM fine-tuning & RAG

Quantitative research

  • Walk-forward backtesting
  • Hypothesis testing & null models
  • Parameter sweeps & overfitting
  • Volatility & slippage modelling

Software & data

  • Python and Java
  • SQL
  • Data pipelines
  • Feature engineering

Building systems

  • End-to-end project delivery
  • Modular system architecture
  • TCP APIs · IBKR TWS
  • REST API integration