Machine Learning Development London
Machine Learning Development Company in London
Most companies sit on data they are not using effectively and still make decisions manually that a well-designed model could predict, classify, detect, forecast, or improve. Gravity Base builds production machine learning systems for prediction, classification, anomaly detection, forecasting, recommendations, and personalisation. We focus on problems where learning from data can clearly move a business KPI, with pipelines, serving, monitoring, retraining, and documentation built into the solution.
- 30+Predictive Models Engineered
- 15+Production ML Pipelines
- 50+Business Data Signals Analyzed
- 100+Intelligent Models Optimised
Get Custom Machine Learning Development Services in London
Most companies sit on data they are not using effectively and still rely on manual decisions that could be improved through predictive models. Machine learning can uncover patterns traditional analytics misses and turn those patterns into proactive business decisions, but only when the model provides a clear advantage. If a simple rule solves the problem better, we recommend keeping the rule. Gravity Base focuses on machine learning projects where learning from real business data can clearly improve a measurable KPI.
Predicting Churn Before It Happens
You want to know who's about to leave, not read about it in last quarter's report. Classification and regression models on your structured data (customer records, transactions, product usage, CRM events) predict churn, conversion, and escalation before it happens. We pair every churn model with a business-metric guard, because a model that boosts retention but loses net revenue is a failure we design against, not a coincidence we discover later.
Scoring Leads and Assessing Credit Risk
You want lead scoring and credit decisions grounded in your actual data, not a generic rules engine. We build classification and regression models — XGBoost, gradient boosting, logistic regression, or neural networks — chosen by your data and the latency-cost-explainability triangle, not by what's fashionable. The output is a score your sales and risk teams can act on immediately.
Classifying and Catching Fraud
You want fraud caught before the chargeback, not after. We build classification models on transaction, behavioral, and telemetry data that flag fraud in real time; reference XGBoost fraud models we've built to this pattern have run two years saving $1.5–2M annually, on classical ML that costs pennies per million predictions. CPU-only where it can be, because you shouldn't pay GPU prices for a tabular problem.
Forecasting Demand Accurately
You want to stop over-ordering and stop running out at the same time. We build time-series forecasting systems that account for seasonality, drift, and the silent edge cases standard accuracy metrics miss, so planning and procurement decisions are based on where demand is actually heading.
Detecting Anomalies in Real Time
You want unusual behavior flagged as it happens, not discovered in a monthly audit. We build anomaly-detection systems on sensor, payment, or telemetry data that account for seasonality and drift instead of relying on static thresholds, so the alerts you get are ones worth investigating.
Modeling Time-Series Signals
Your operational signals change constantly: demand, seasonality, external conditions, customer activity. We build time-series systems that combine historical data with current signals for more responsive forecasting, so planning, logistics, and maintenance decisions can adjust before conditions fully shift.
Building Recommendation Engines
You want recommendations, search ranking, and pricing that actually move a metric, not a black-box suggestion widget. We build collaborative filtering, content-based ranking, and hybrid models with cold-start handling and online learning loops, and we design the eval set and A/B framework before we build the model, so "better recommendations" is something you can measure, not something we claim.
Personalising Product, Content and Pricing
You want personalisation that's tested, not assumed. We extend the same recommendation infrastructure to product, content, search, and pricing personalisation, validated through the same A/B framework, so every personalisation decision is backed by a measured result before it ships to all your users.
Contact Us For Machine Learning Consulting Services
Contact UsGet Custom Machine Learning Development Services
Gravity Base delivers machine learning development focused on measurable business outcomes, production reliability, and the technical requirements of UK enterprises. Our machine learning services combine KPI-first model design with feature engineering, real-time and batch serving, drift monitoring, and recommendation systems — built as a full production loop, not a one-off model.
Production ML Pipelines
Feature pipelines handling ingestion, cleaning, and versioning that run on a schedule, not on someone's laptop, helping banks turn raw transaction data into fraud-model inputs and retailers turn sales history into demand-forecasting inputs.
Model Training & Benchmarking
Classification, regression, and time-series models, XGBoost, gradient boosting, or deep learning where the data calls for it, benchmarked against a baseline you understand, helping lenders score credit risk and telecoms score churn on their own data.
Real-Time & Batch Serving Infrastructure
Validated models deployed for real-time scoring or batch processing, with latency targets, versioning, and rollback, helping payment platforms flag fraud instantly and manufacturers batch-score maintenance risk overnight.
Monitoring & Drift Detection Systems
Production performance tracked against live outcomes, with alerts when a model degrades instead of a quiet decline nobody notices, helping fraud and demand-forecasting teams catch a shift before it costs them the result the model was built to protect.
Recommendation & Personalisation Engines
Collaborative filtering, content-based ranking, and hybrid models validated through an A/B framework built before the model, helping ecommerce and content platforms personalise product, search, and pricing decisions with a result they can measure.
How Our Machine Learning Services in UK Work
Gravity Base runs every ML engagement through the same lifecycle: data feasibility, optional shadow testing, feature pipeline, model training and validation, serving infrastructure, and monitoring with retraining. We answer whether your data can support the model in week one, before anyone commits to a build. From there we build the pipeline, train and benchmark the model, deploy it for real inference, and hand over documentation your team can own. Monitoring and drift detection run for the life of the model, not just at launch.
- 01
Data Feasibility Assessment
Gravity Base starts with a fixed-fee assessment, from £2K, answering whether your data can support the model you want, before anyone trains anything. We examine volume, labels, leakage, bias, and the target variable, and recommend ML versus GenAI where that's the more honest fit. This stage produces a defensible build quote and a projected monthly run rate, delivered in week one.
- 02
Shadow Testing (Optional)
Before any decision gets automated, we run the model's predictions against real traffic for two to twelve weeks without letting it act. This shows you exactly how the model would have performed on live data, so the move from "trained model" to "automated decision" is backed by evidence, not a demo.
- 03
Production Data Pipeline
Gravity Base builds ingestion, cleaning, feature engineering, and versioning that runs on a schedule, not on someone's laptop. We use Airflow, dbt, Postgres, Spark, and Kafka for the pipeline, Great Expectations for data-quality checks, and a feature store, Feast, Tecton, or Hopsworks, when features need to be shared and reused reliably across models.
- 04
Model Training and Benchmarking
We engineer the model against your data and prediction objective: XGBoost, LightGBM, gradient boosting, logistic regression, or neural networks for tabular and classical problems, with PyTorch, TensorFlow, Hugging Face, JAX, Keras, or Lightning where deep learning is the right call. Every model is benchmarked against a baseline you can understand, on a holdout set that reflects reality, using the metrics that matter to the business rather than to a leaderboard.
- 05
Serving Infrastructure
Gravity Base packages the validated model for batch or real-time inference, deployed with latency targets, versioning, and rollback for when a new model underperforms. Serving runs on FastAPI, Docker, and Kubernetes, with BentoML, NVIDIA Triton, or Ray Serve where packaging or GPU throughput demands it, and on AWS SageMaker, GCP Vertex AI, Databricks, Azure ML, Snowflake, or NVIDIA DGX Cloud depending on where your data already lives. Typical production builds start around £10K and run six to ten weeks to a live model.
- 06
Monitoring and Drift Detection
Performance gets tracked in production against live outcomes, using MLflow, Prometheus, Grafana, and Evidently, with alerts when the model degrades rather than a quiet decline nobody notices. Data drift, concept drift, and changing customer or transaction behavior get caught here before they cost you the result the model was built to protect.
- 07
Retraining and Documentation Handover
A monthly retainer covers drift detection, scheduled or condition-based retraining, and on-call support, keeping the model current as your data and business change. Every build ends with documentation covering how the model works, what it assumes, how it fails, and how to retrain it, written so your team can own it after we leave, with 100% model and code ownership staying with you throughout.
Your Questions Answered by Our Experts
We pair every model with a business-metric guard so you measure revenue impact, not just accuracy, and we ship the full loop — pipelines, monitoring, retraining, rollback — not just a trained model. You keep 100% model and code ownership, and we'll tell you when a simple rule beats a model instead of selling you one anyway.
