Machine learning company in London
If a chatbot is the wrong tool, this is the page. As a machine learning company, we build prediction, classification, forecasting and recommendations. A gradient-boosted model has beaten a £40,000 LLM build for us more than once.
This is for data leads and CTOs who need a score, a forecast or a ranking from a machine learning company that will say no to an LLM when classical ML wins.
Classical ML where it beats GenAI- Business metrics, not just accuracy
- Built to run live, not only in a notebook
- Drift monitoring built in
When machine learning is the job, and when an LLM is not
AutoML and APIs cover the easy cases
Churn scores on clean tables, basic classification. Often a platform is enough. Pain starts with messy labels, shifting data, and models that quietly go stale.
Private and regulated data
Health, finance, workforce data. Training and serving need clear boundaries, not a notebook on someone's laptop.
We ship the loop, not just the model
Feature pipelines, monitoring, retraining triggers, rollback. Prediction without ops is a science fair project.
A simple rule beats us when…
If a simple rule beats a model, keep the rule. We are for problems where learning from data clearly moves a KPI.
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How we run an ML build
Data Audit and Feasibility (weeks 1-2)
We map your problem against your data: volume, label quality, feature availability at prediction time, leakage risk, lineage. Discovery includes a paid PoC on your real data when feasibility risk is high. We test the simplest model first to set a baseline.
Output: feasibility report, classical ML vs GenAI recommendation, projected business impact, fixed-scope build quote, or an honest 'your data isn't ready yet, fix this first' if that's the answer.
Feature Engineering and Model Development (weeks 3-6+)
Most of the work lives here. Feature design, leakage checks, train-validate-test splits that respect time and identity, model selection driven by your latency, cost and explainability constraints. Build runs as a small senior team led by Michal Vavra, embedded with your data and operations stakeholders.
Each iteration is gated on a fixed eval set with business-metric guards, not vanity accuracy.
Shadow Testing and Production Deployment (weeks 6-10+)
Before the model drives any decision, we run it in shadow mode against your real production traffic for two to twelve weeks, comparing predictions to actual outcomes. This gives a true accuracy and ROI projection before you spend a penny on interventions. Then we ship behind feature flags with a manual override and rollback path.
We've stopped builds at this stage when the economics didn't justify production. That's what shadow testing is for.
Monitoring, Retraining, Iteration
At launch we wire in prediction-confidence histograms, business-outcome monitoring, drift detection, retraining triggers and alerting. Standard latency dashboards do not catch the model returning perfect 200s while drifting silently. We monitor what actually moves: business metrics first, then statistical drift.
Monthly retainer for monitoring, retraining and iteration, not a lock-in. Take the system in-house whenever you're ready.
What machine learning company projects cost
Frequently Asked Questions
The questions data leads and CTOs ask us in the first call about classical ML, GenAI, drift and run-rate cost.
If your data is structured and labelled (transactions, customer records, sensor data, time series) and you need prediction, classification or anomaly detection, classical ML usually wins on accuracy, cost, latency and explainability. CPU-only inference at pennies per million predictions, deterministic outputs, full feature importance. LLMs win when the input is genuinely unstructured (free-text feedback, documents, multi-modal). We make the call with you in writing during discovery, not after you've spent £40,000 on prompt engineering.
Around 80% of ML models stall in the notebook. The model itself is the easy 20%. The 80% that kills projects is data quality, feature pipelines, model serving, drift detection, retraining, observability and the cross-functional sign-off needed for Legal, Operations and Finance to put it live. We design that scope from week one. The model we put live is the one we've already wired into the production pipeline before we tune the hyperparameters.
It depends on the problem. For most classification and regression on structured data, a few thousand to tens of thousands of labelled examples. Anomaly detection can work with fewer examples of normal behaviour. Time-series forecasting needs enough history to cover the seasonality you care about. We assess your data quality, volume and label coverage in discovery and tell you honestly when the answer is 'fix the data first, then revisit'. We've recommended that more than once.
The business outcome, not the AUROC. We define the impact metric (net revenue, cost saved, decisions automated, false-positive cost) before we pick the model. A churn model that raises retention but loses net revenue is a failure. A fraud model with 99% accuracy on imbalanced data is meaningless. Accuracy, precision and recall are sanity checks. The metric we report on is the one your CFO would recognise.
Yes, models degrade as your data and your customers change. Standard monitoring (latency, error rates, GPU utilisation) does not catch it. We ship every model with prediction-confidence histograms, business-outcome monitoring, drift detection on feature distributions, golden-dataset regression and retraining triggers. Retraining cadence depends on volatility (weekly for fraud, monthly for tabular classification, on-trigger for everything else). Maintenance retainer covers the work, or we hand it over.
Discovery is fixed-fee from £2,000. Production builds typically start at £10,000 and run six to ten weeks. Run rate is a fraction of GenAI: classical ML on CPU costs pennies per million predictions, plus a flat retainer for monitoring and retraining. We quote both numbers in the proposal so the run rate is clear before you commit.
When a rules engine, a SQL query or a simple business heuristic does the job. When your data is too messy, too sparse, or doesn't predict the outcome you care about. When the cost of false positives outweighs the value of correct predictions (we've seen 92% false-positive rates on real churn projects). We'll say so during discovery and point you at the cheaper, more reliable solution. We'd rather not build it than build the wrong thing.
Related
Related AI work
If you actually needed a language model, use LLM development. This page is classical machine learning: forecasts, scores, rankings.