Recommendations, predictions, and ML pipelines for your product.
The gap between a machine learning proof of concept and a production system that works reliably is larger than most people expect. A model that performs well on a held-out test set can perform poorly on real data for reasons that aren't obvious until you look closely: the training data wasn't representative of real usage, the feature engineering assumed data quality that doesn't exist in production, the model was evaluated on metrics that don't map to the business outcome you actually care about. We've seen all of these, and we build with them in mind from the start.
Our starting point for every ML project is a clear definition of what success looks like in business terms — not in model metrics. Accuracy, F1 score, and AUC are means to an end, not ends in themselves. The question that actually matters is whether the model, deployed in the real workflow, produces better outcomes than whatever process it's replacing. We design the evaluation framework around that question before we start building models.
Recommendation systems appear in many forms: products a customer might want to buy, content a user might want to read, connections a professional might want to make, features a user is likely to find valuable based on their behavior. The common thread is using signals about past behavior to predict future preferences.
The right approach depends on the data you have. Collaborative filtering — finding users similar to the target user and recommending what they liked — requires substantial user-item interaction data to work well. Content-based filtering — recommending items similar to things the user has engaged with — works with less interaction data but requires good item features. Hybrid approaches combine both signals and generally outperform either alone when you have sufficient data for both.
Cold start — what to recommend for new users or new items with no interaction history — is the hardest problem in recommendation systems and one that's often underestimated. We design explicit strategies for cold start, typically combining popularity-based recommendations with quick feedback loops to gather the first interactions needed to personalize.
Predictive models answer questions about what's likely to happen: which customers are likely to churn in the next 30 days, which support tickets are likely to escalate, which transactions are likely to be fraudulent, what demand is likely to be for a product next week. These are valuable questions because they let you intervene — reach out to at-risk customers, escalate tickets proactively, flag transactions for review, adjust inventory — before the outcome you're trying to avoid has already happened.
Building a good predictive model starts with feature engineering: identifying what information is available at prediction time (not information that would only be available after the fact), transforming that information into features the model can use effectively, and handling missing values in a way that reflects the real semantics of missingness rather than just imputing zeros. Feature engineering is often where the most value is created and where the most subtle mistakes are made.
We evaluate models on held-out data that reflects the temporal structure of the problem — not random splits that allow the model to implicitly use future information during training. This matters because many prediction problems have temporal patterns that a randomly split evaluation will evaluate correctly while the deployed model performs worse, because in production you're always predicting the future from the past.
NLP capabilities have advanced dramatically with large language models, but NLP doesn't always require an LLM. For classification tasks — categorising support tickets, detecting sentiment in reviews, identifying topics in documents — fine-tuned smaller models or even traditional ML approaches are often more efficient, more predictable, and cheaper to run than LLMs. We evaluate the right approach for each use case rather than defaulting to the most capable model.
For information extraction — pulling structured data from unstructured text, like extracting company names and dates from contracts, or product details from supplier emails — LLMs are often the right choice because the flexibility they bring to understanding varied document formats is genuinely valuable. We build extraction pipelines that validate the outputs and flag low-confidence extractions for human review rather than passing everything downstream without quality checks.
Computer vision applications we've built include document verification, object detection in images, quality control systems, and image classification. The technology range is wide: for standard classification and detection tasks, pretrained models with fine-tuning on your specific data are usually the most practical approach. For specialized tasks with unusual requirements, training from scratch or more extensive fine-tuning may be necessary.
Data labeling is the hidden cost of computer vision projects. A good model requires a substantial quantity of accurately labeled training data, and creating that data takes time and careful quality control. We plan the data collection and labeling effort explicitly as part of project scoping, because it's often the constraint that determines the timeline more than the model development itself.
Getting a model to production requires more than training it. You need a way to serve predictions at low latency, a way to monitor the model's performance over time, a way to retrain it as new data becomes available, and a way to version and roll back models if a new version performs worse than expected.
We use MLflow for experiment tracking and model versioning, FastAPI or serverless functions for model serving depending on the latency and throughput requirements, and purpose-built monitoring for tracking prediction distributions and model performance metrics in production. We design retraining pipelines from the start — not as an afterthought once the model is deployed — because most deployed models degrade over time as the data distribution they were trained on diverges from what they see in production.
Every ML project starts with an honest assessment of the available data: what it contains, what quality it's in, how representative it is of the problem you're trying to solve, and what biases it might encode. We surface data quality issues early rather than building models on data problems that will limit their usefulness or create problems you haven't anticipated.
Data privacy is a consideration in every ML project. Depending on what data the model is trained on and how it's used, there may be regulatory requirements (GDPR, HIPAA, others) that affect what you can do and how. We design with these requirements in mind rather than treating compliance as a separate issue to be handled after the technical work is done.
Do we need a data scientist or can we start with an ML engineer? It depends. Pure modeling work — designing experiments, evaluating approaches, interpreting results — benefits from data science expertise. Building and deploying production systems requires engineering skills. Many good ML projects need both at different stages. We can provide both capabilities and advise on what your specific project needs at each stage.
How much data do we need? It depends heavily on the task. Text classification with a pretrained model can work with hundreds of labeled examples. Training a computer vision model from scratch may require tens of thousands. We'll give you an honest assessment of whether your current data is sufficient and what to do if it isn't.
How do we know if the model is good enough to deploy? By defining acceptance criteria in business terms before you start building — not by looking at model metrics and deciding after the fact. We help you define what good enough means for your specific use case and build the evaluation framework to assess it.
Start by describing the decision or prediction you want to make and the data you have available. From there we can assess the feasibility, design the evaluation framework, and give you a realistic picture of what building a production-ready system involves.
The gap between a machine learning proof of concept and a production system that works reliably is larger than most people expect.
Recommendation systems appear in many forms: products a customer might want to buy, content a user might want to read, connections a professional might want to make, features a user is likely to find valuable based on their behavior. The common thread is using signals about past behavior to predict future preferences.
Predictive models answer questions about what's likely to happen: which customers are likely to churn in the next 30 days, which support tickets are likely to escalate, which transactions are likely to be fraudulent, what demand is likely to be for a product next week.
NLP capabilities have advanced dramatically with large language models, but NLP doesn't always require an LLM. For classification tasks — categorising support tickets, detecting sentiment in reviews, identifying topics in documents — fine-tuned smaller models or even traditional ML approaches are often more efficient, more predictable, an…
Recommendation systems, Predictive models, NLP & text analysis, Computer vision, ML pipelines.
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