Service
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.
Frequently Asked Questions
Answers to the technical questions we hear most often about Machine Learning.
We start with the decision or task you want to improve, the data available, and the cost of getting it wrong. We compare simpler approaches, existing models, and custom development. If a rules-based solution or an existing service meets your needs, we will recommend that rather than build a model unnecessarily.
There is no single minimum. Data quality, coverage, and the availability of reliable examples matter as much as volume. We assess your data against the use case and identify gaps before training. Depending on what we find, we may recommend adapting an existing model, collecting more examples, or narrowing the initial scope.
We agree success criteria with you before experimentation, including which errors matter most to the business. We test on data kept separate from training, compare results with a baseline, and examine failure cases. Our technical research paper documents the evidence, limitations, and recommendations, including when further work or human review is needed.
Yes. We can use retrieval-augmented generation (RAG) to help an assistant find relevant content and reference it in answers. We assess document quality, access permissions, and how answers will be evaluated. Retrieval does not eliminate incorrect answers, so we also design checks and escalation paths appropriate to the use case.
We agree what data can be used, where it can be processed, and who can access it before development begins. This includes reviewing any external model provider, its retention settings, and its data-use terms. Where needed, we assess private deployment options and ways to remove or minimise sensitive information.
Yes. We can scope deployment and ongoing monitoring alongside the research work or as a follow-on engagement. This can include integration with your applications, checks for changes in data and model performance, and a retraining process. We agree responsibilities and support needs so your team knows what running the model will involve.
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Let's discuss how machine learningcan help your business achieve better business outcomes & growth.