Enterprise AI Engineering · Applied AI & Machine Learning

    Applied AI & Machine Learning for business decisions.

    Forecasting, prediction, optimisation, recommendation, anomaly detection, computer vision and NLP systems engineered as decision systems: from data and features to models, the decisions they drive and the monitoring that keeps them right.

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    Why ML projects disappoint

    The model is rarely the hard part. The decision is.

    Most machine learning effort in enterprises goes into models that are never wired into a decision, never monitored, or measured on a metric that does not reflect what a mistake costs. We engineer the decision system, not just the model.

    • A forecast nobody acts on

      The model is accurate and the planning process still runs on last quarter's spreadsheet. A prediction only has value when a decision, a threshold and an owner are designed around it.

    • A model that was right last year

      Customer behaviour, suppliers and sensors change. Without drift monitoring and a retraining path, accuracy decays quietly and the business finds out from the outcome.

    • The right accuracy, the wrong error

      A fraud model tuned for overall accuracy can be cheap on false negatives and expensive on false positives, or the reverse. The metric has to reflect what each kind of error costs the business.

    In brief

    Applied AI & Machine Learning engineering builds systems that make a specific business decision better and keep making it better: forecasting, prediction, classification, anomaly detection, recommendation, optimisation, computer vision and NLP. It is the right approach when the question has a numeric or categorical answer, your historical data reflects the situations the model will face, and the decision repeats often enough to justify a model. It solves the gap between an accurate notebook and a monitored system that changes an order, an alert, a price or a route inside the real process.

    How the system fits together

    From data to a monitored business action.

    Six stages, and the two that most projects skip are the last two: the decision logic that turns a score into an action, and the monitoring that tells you when the model stops being right.

    1. 01

      Data

      Historical records, events and sensor streams, with lineage and quality checks.

    2. 02

      Features

      Reusable, versioned feature pipelines so training and serving see the same inputs.

    3. 03

      Models

      The simplest model that meets the bar, benchmarked against a baseline and a rules heuristic.

    4. 04

      Decisions

      Thresholds, confidence bands and business rules that turn a score into a decision.

    5. 05

      Business action

      The order placed, the alert raised, the price set, the case routed, inside the existing system.

    6. 06

      Monitoring

      Drift, latency, error cost and outcome tracking feeding back into retraining.

    Capabilities

    What we build.

    Grouped by the kind of decision they support. Each is delivered with the feature pipeline, decision logic, monitoring and retraining path it needs to stay in production.

    • Predict

      • Forecasting
      • Predictive modelling
      • Time-series modelling
      • Predictive maintenance

    • Classify and detect

      • Classification
      • Anomaly detection
      • NLP
      • Computer vision

    • Optimize and decide

      • Optimization
      • Recommendation systems
      • Decision intelligence
      • Advanced analytics

    Does this problem actually need Generative AI?

    Usually not. Most business decisions are prediction, detection or optimisation problems, where a trained model is more accurate, cheaper and easier to evaluate than a language model. This is the checklist we apply before choosing an architecture.

    Which kind of AI fits which business problem
    The problemUsually the right approachWhat to watch
    Generate or summarize languageGenerative AIFluent output over unstructured input; evaluate for groundedness and factuality.
    Retrieve information from documentsRAG / Generative AIRetrieval quality decides answer quality; measure retrieval separately.
    Forecast demandMachine LearningTime-series and regression models; evaluate on error cost, not just accuracy.
    Predict equipment failureMachine LearningSurvival and classification models on sensor history; false-negative cost dominates.
    Optimize schedulingOptimization / MLConstraint solvers and operations research, often with ML-predicted inputs.
    Detect anomaliesMachine LearningStatistical and learned detectors tuned to an acceptable alert volume.
    Interpret images or videoComputer VisionDetection, segmentation and classification models; multimodal LLMs for open-ended description only.
    Execute multi-step workflowsAgentic AIPlanning and tool use across systems; needs permissions, state and human oversight.
    Combinations are common. A demand forecast (ML) can feed a scheduling optimiser, and a document-extraction step (Generative AI) can feed a classifier. The table decides the component, not the whole system.
    Google's Rules of Machine Learning opens with the advice not to be afraid to launch a product without machine learning, and to start with heuristics when the data and the problem do not yet justify a model.
    Source: Google Developers, Rules of Machine Learning: Best Practices for ML Engineering (undated guide). Practitioner guidance rather than a study. It supports the view that the simplest system that solves the problem should be tried first.

    Evaluation

    How do you evaluate a machine learning system?

    By choosing the metric from the use case and the cost of each kind of error, not by reporting every metric a library prints. Precision and recall mean nothing until the business has said which mistake is more expensive.

    Evaluation metrics chosen by use case
    Use caseMetrics we useThe business question the metric answers
    Classification (fraud, churn, triage)Precision, recall, F1, ROC/AUC where classes are imbalancedCost of a false positive versus a false negative, set with the business
    Forecasting and regressionMAE, RMSE, and error by horizon and segmentCost of over-forecasting versus under-forecasting
    Anomaly detectionPrecision at a fixed alert volume, recall on known incidentsAlert fatigue and missed incidents
    Recommendation and rankingOffline ranking metrics, then online uplift against a control groupRevenue or engagement per exposure, not click-through alone
    Computer visionPrecision and recall per class, localisation qualityInspection throughput and the cost of an escaped defect
    All production modelsDrift in inputs and outputs, inference latency, retraining cadenceHow long a degraded model runs before anyone notices
    Drift and inference latency are tracked on every production model regardless of type. The acceptance threshold for each metric is agreed against a measured baseline during Discover.

    Answer first

    Questions we answer before modelling starts.

    What is applied AI?

    Applied AI is the use of machine learning and related techniques to make a specific, repeatable business decision better: forecast a quantity, classify a case, detect an anomaly, recommend an option or optimise a plan. It is measured by the quality of the decision, not the sophistication of the model.

    Applied AI systems are trained on your own historical data and integrated into the process where the decision is made. That integration, plus the monitoring that keeps the model honest, is most of the engineering.

    Machine learning vs Generative AI: which do we need?

    If the answer to your question is a number, a category, a ranking or a schedule, you need machine learning. If the input is language, documents or images and the output is text, an extract or assistance, you need Generative AI. Many systems need both, in different components.

    The table above is the checklist we apply. The most common mistake is using a language model for a prediction task because it is available, and paying for it in latency, cost and untestable behaviour.

    How much data do we need?

    Enough history to cover the situations the model will face, including the rare ones that matter most, with labels or outcomes you trust. That is a question about coverage and quality, not row counts, and a short data review usually answers it before any modelling begins.

    When data is thin, the honest answer is often a rules-based baseline first, instrumented to collect the outcomes a model will later learn from.

    When is a rules-based system better than machine learning?

    When the decision can be written down as rules the business already trusts, when the data is too sparse to learn from, or when every decision must be explainable line by line. Rules are also the baseline every model must beat to justify its cost.

    We frequently ship a rules engine as the first production release and add a model only where the rules demonstrably fall short.

    How do you handle model drift?

    By monitoring input and output distributions against the training data, alerting when they shift beyond agreed thresholds, and having a tested retraining and rollback path ready before the first deployment. Drift is expected; being surprised by it is the failure.

    The operating side of this, from model registry to automated retraining, is covered under MLOps.

    How MLOps keeps models honest in production

    Metrics we design for

    What we instrument on every ML system.

    • Business error cost

      The expected cost of the model's mistakes, using the false-positive and false-negative costs the business agreed.

    • Lift over baseline

      Improvement against the current process or a simple heuristic, measured on held-out data and then in production.

    • Drift

      Shift in input distributions and prediction distributions, with thresholds that trigger review or retraining.

    • Inference latency

      Time to a prediction at the volume and percentile the decision needs, batch or real time.

    • Decision adoption

      Share of predictions that actually reach a decision and are acted on inside the workflow.

    • Retraining and rollback time

      How long it takes to retrain, validate and promote a model, and to roll one back.

    These are the metrics we instrument and report. Targets are agreed per system against a measured baseline; we do not publish universal improvement percentages.

    Next step

    Assess whether your problem needs machine learning, and what it would take.

    Bring the decision you want to improve, the data you have and the cost of getting it wrong. We will tell you whether it is a prediction, an optimisation or a language problem, and what a proof of value with a real baseline looks like.

    Assess My AI / ML Use Case Send us the use case in writing

    A 30-minute working session with a senior AI engineer. Bring the workflow, the data it depends on and the constraint that has stopped it so far.

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