CAPABILITY // SRV-02

Machine Learning & Model Fine-Tuning

Generic models give you generic results. We tune models on your data to solve problems your off-the-shelf tools have given up on.

The gap between what a large foundation model can do out of the box and what your business actually needs is almost always bridgeable — it just requires someone who knows where to look. We specialise in taking the raw capability of state-of-the-art open-source models and shaping it with your data, your domain terminology, your edge cases, and your performance requirements. The result is a model that behaves like a specialist trained on your business rather than a generalist trained on the internet.

10M+ rows modelled · <100ms inference speeds
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WHAT'S INCLUDED
✓Fine-tuning Llama 3, Mistral, Phi-3, Qwen, and other open-weight models on your labelled data
✓Text classification, multi-label tagging, named-entity recognition, and information extraction
✓Semantic search and vector embedding pipelines with Pinecone, Weaviate, or pgvector
✓Regression, forecasting, and anomaly detection for operational and financial data
✓MLOps: training pipelines, experiment tracking (MLflow/W&B), model registry, and A/B deployment
✓Custom evaluation frameworks with domain-specific metrics beyond generic benchmarks
✓Continuous monitoring with data drift detection and automated retraining triggers
✓Inference optimisation — quantisation, distillation, batching — for latency and cost targets
WHO THIS IS FOR

Built for teams that need results, not experiments.

Product Teams
Wanting to embed smart features — semantic search, smart categorisation, personalised recommendations — that would take a full ML team years to build from scratch.
Data Teams
Sitting on labelled datasets and predictive questions but without the ML engineering capacity to go from Jupyter notebook to production.
Healthcare & Legal
Needing models trained on domain-specific terminology and compliant with strict data-handling requirements — not general-purpose APIs you can't audit.
E-commerce & Marketplaces
Looking for product classification, review analysis, demand forecasting, or fraud detection tuned to the specific patterns in their catalogue and customer base.
HOW IT WORKS

From first call to production in clear steps.

01
Data Audit & Problem Scoping
We review your existing data — volume, quality, label distribution, class imbalance, noise — and confirm whether the problem is solvable with ML in the current state. If the data needs cleaning or augmentation, we scope that work separately. We define the success metric before writing any code.
02
Baseline & Approach Selection
We run a fast baseline — often a fine-tuned small model or a prompt-engineered foundation model — to establish a performance floor. This tells us whether fine-tuning is worth the investment versus a simpler approach, and which model family is best suited for the task.
03
Training & Experimentation
We run structured experiments: varying model size, training data subsets, hyperparameters, and augmentation strategies. All runs are tracked in MLflow or Weights & Biases so every decision is reproducible and the performance history is transparent.
04
Evaluation Against Real-World Criteria
We evaluate on your holdout data and, where possible, on a sample of real production inputs. We report precision, recall, F1, and latency — and we discuss the trade-offs honestly. A model that is 94% accurate overall but fails on your most important edge class is not ready for production.
05
Deployment & Monitoring
We deploy the model as a low-latency API endpoint (FastAPI on your infrastructure, or serverless via Hugging Face / Modal), wire up input and output logging, and set up drift monitoring. We define the retraining trigger criteria and document the pipeline so your team can run it independently.
IN DEPTH

The details that separate good from great.

When fine-tuning is worth it — and when it isn't

Fine-tuning adds significant cost and complexity compared to prompt engineering or RAG. It is worth it when: your domain has specialised terminology or formatting that confuses general models; you need consistent structured output at high volume where prompt-engineering is brittle; latency requirements rule out large API-hosted models; you need a small, cheap model you can run locally or at the edge; or you have proprietary data you cannot send to external APIs. It is usually not worth it when a well-crafted prompt plus retrieval already gets you to 90%+ accuracy, or when your training data is too small (under ~500 high-quality examples for most NLP tasks).

MLOps: the gap between a trained model and a useful product

A model that works in a notebook is not a product. The engineering work to make a trained model reliable in production — versioning, reproducible training pipelines, A/B testing infrastructure, input validation, output monitoring, rollback capability — is often 3× the work of the training itself. We build this infrastructure as part of every deployment project. Model registry in MLflow, deployment via Docker containers with health-checked endpoints, input schema validation with Pydantic, and a monitoring dashboard that alerts when the distribution of incoming data shifts away from the training distribution.

Inference cost and latency optimisation

Running a 70B-parameter model at inference is expensive and slow. For most production use cases, a 7B model fine-tuned on domain data outperforms a 70B general model at 10× lower cost and 5× lower latency. We systematically evaluate the accuracy/cost trade-off for your specific task. When needed, we apply quantisation (GPTQ, AWQ, or bitsandbytes), knowledge distillation to a smaller student model, or speculative decoding to push latency down to sub-100ms without meaningful accuracy loss.

FAQ

Questions we get asked before every project.

Do we need a large dataset to get started?
Not always. The minimum depends heavily on the task. For text classification into well-defined categories with clear examples, we can achieve strong results from 200 to 500 labelled examples using few-shot fine-tuning or low-rank adaptation (LoRA). For complex NER or information extraction tasks, 1,000 to 5,000 annotated examples is a more realistic floor. For regression or forecasting on tabular data, it depends on the number of features and the signal-to-noise ratio. We always tell you honestly during the data audit if we think the dataset is too small, and what it would take to get to a viable size.
Can you improve an existing model that isn't performing well enough?
Yes — this is a common engagement. We start by auditing the failure modes: are the errors clustered in specific data subsets? Is the training data noisy or mislabelled? Is the evaluation metric misaligned with the business objective? Often, the issue is not the model architecture but the data quality or the loss function. We fix the root cause rather than just retraining on the same problematic data.
Where will the model run — on our servers or a cloud API?
Either. We evaluate the options: cloud-hosted APIs (OpenAI, Anthropic, Together AI) are zero-infrastructure and fast to integrate but carry per-token costs and data-leaving-your-perimeter implications. Self-hosted on AWS/GCP gives you full control, predictable costs at scale, and data sovereignty. Edge deployment (on-device or on-prem) eliminates latency and cloud dependency entirely. We recommend the right architecture for your volume, latency requirements, budget, and compliance constraints.
How do you handle data privacy when training on sensitive information?
We operate under a strict data handling protocol. Training data is processed only in agreed environments, never stored beyond the project term, and never used to improve any other model. For regulated industries, we can work entirely within your private cloud under a BAA or equivalent agreement. We can also train on anonymised or differentially private data subsets where the underlying records must stay confidential.
What is the difference between fine-tuning and RAG? Which do I need?
Fine-tuning adjusts the model's weights on your data, making it better at a specific task or domain by 'baking in' knowledge. RAG retrieves relevant documents at inference time and feeds them to the model as context, keeping the model weights unchanged. Fine-tuning is better for style, format, tone, and tasks that require consistent structured output. RAG is better for factual question-answering over a knowledge base that changes frequently — you update the index, not the model. Most production systems benefit from both: a fine-tuned model that is fluent in your domain, combined with RAG for up-to-date factual grounding.
How long does a machine learning project typically take?
A focused classification or extraction model — well-scoped task, clean labelled data, clear evaluation criteria — typically takes 3 to 6 weeks from data audit to a deployed, monitored endpoint. A full forecasting or recommendation system with feature engineering, pipeline infrastructure, and A/B testing typically takes 8 to 16 weeks. MLOps infrastructure buildouts (if you have existing models that need production-grade deployment) typically take 4 to 8 weeks separately.
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