Machine Learning
We research, design, and build custom machine learning models tailored to your domain. From problem framing and architecture design through to training, evaluation, and delivery, every engagement is grounded in experimental rigour.
Common Challenges We Solve
Off-the-shelf models miss the nuance of your domain - generic solutions leave accuracy gaps that translate to real cost or risk
No clear research methodology for selecting and validating model approaches
Fine-tuning foundation models without structured experimentation wastes time and budget
Decisions about model architecture are often made without documented evidence
Vendor lock-in from third-party ML APIs with no path to owning and optimising your own model
Custom Models, Built For Your Domain
We design and build ML models tuned to your domain's data, constraints, and accuracy requirements – not repurposed generic solutions.
Every engagement concludes with a technical research paper covering model design, experiments, results, limitations, and recommended next steps.
Problem Framing and Literature Review
Scope the ML problem, survey related work, and establish baselines before writing a line of training code.
Custom Model Architecture Design
Design model architectures matched to your data modality, scale, and accuracy targets - from classical ML to deep learning.
Data Pipeline and Feature Engineering
Build reproducible pipelines to prepare, augment, and version training data for reliable experimentation.
Training, Experimentation and Evaluation
Run structured experiments with tracked metrics. Compare approaches rigorously before committing to a final model.
Fine-Tuning and Transfer Learning
Adapt foundation models (LLMs, vision models) to your domain using fine-tuning, LoRA, RAG, and prompt engineering.
Technical Research Paper
A written deliverable covering model design, experiments, results, limitations, and recommended next steps.
Methodical execution.
A structured, collaborative approach designed to deliver predictable outcomes and lasting value at every phase of the engagement.
Discover
Identify the ML problem, assess feasibility, review existing data, and align model success criteria with business outcomes.
Design
Survey the literature, select candidate architectures, and design the experimentation plan and data pipeline.
Build
Train, evaluate, and iterate on model candidates with tracked experiments. Commit to the final architecture based on evidence.
Enable
Document the model, hand over the research paper, and equip your team to interpret results and act on model outputs.
Govern
Establish bias checks, explainability reporting, and responsible AI controls appropriate to your domain and risk profile.
Evolve
Retrain as data shifts, extend the model for new use cases, and layer on deployment and monitoring as requirements grow.
The modern data stack, mastered.
We are platform-agnostic but highly opinionated. We deploy the right tools for your specific workload and scale.
Machine Learning
PyTorch
TensorFlow
HuggingFace
scikit-learn
MLflow
Chroma
Pinecone
LangChainLanguages
PythonDon't see your stack listed? Our experience grows with every client — we regularly work with tools beyond this list and adapt quickly to the technologies your team already relies on.
Frequently Asked Questions
Answers to the technical questions we hear most often about Machine Learning.
Pre-built APIs suit commodity tasks where generic accuracy is acceptable and cost at low volume is not a concern. Custom models are worth the investment when domain accuracy gaps matter, your data is proprietary, or long-term cost at scale makes per-call API pricing unsustainable.
A structured document covering: problem framing, dataset description, model architecture decisions, experiments run, evaluation metrics, results, limitations, and recommended next steps. It gives your team and stakeholders an evidence base for all model decisions.
It depends on the approach. Fine-tuning a foundation model can work with hundreds of labelled examples. Training from scratch on tabular data may need thousands of rows. We assess your data situation during discovery and recommend the right approach before any commitment is made.
Fine-tuning is usually faster and cheaper when a foundation model covers your modality and the domain shift is moderate. Training from scratch makes sense for highly specialised domains where pre-trained representations do not transfer well. We document this decision and the supporting evidence in the research paper.
Yes. Retrieval-augmented generation (RAG) is one of our core patterns for grounding LLMs in proprietary knowledge. We design and build RAG pipelines using vector databases such as Pinecone and Weaviate alongside fine-tuned or prompted foundation models.
Yes. Deployment, monitoring, and MLOps infrastructure are a follow-on consulting offering. We can productionise the model, set up automated retraining pipelines, and implement performance monitoring. Raise this with us during the engagement and we will scope it alongside the R&D work.
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Let's discuss how machine learning can help your business achieve excellence and drive growth.
