Organisations with repeatable decisions and historical data
Use patterns in operational, customer, transaction, device, or market data to improve prioritisation and planning.
Prediction, recommendation, classification, forecasting, optimisation, and anomaly detection
Machine learning development
Rokad develops machine-learning systems from problem and data assessment through modelling, deployment, monitoring, and business workflow integration.
Designed for / 01
A focused delivery model for the organisations that need it.
Machine learning is effective when the target, data, decision, feedback, and operating environment are well defined. Rokad builds predictive, ranking, recommendation, classification, forecasting, anomaly, and optimisation systems with reproducible training, evaluation, deployment, and monitoring.
Product teams adding predictive capability
Embed scoring, recommendation, ranking, forecasting, detection, or personalisation into a software product.
Teams moving models from notebooks to production
Establish reproducible data, training, evaluation, serving, monitoring, ownership, and release processes.
Challenges / 02
The problems this service is built to solve.
The prediction target is poorly defined
A technically accurate model may not improve the decision, workflow, timing, or economic outcome that matters.
Training data does not represent production reality
Leakage, missing labels, bias, changing processes, sparse events, and delayed outcomes undermine model performance.
A model exists without an operating system
Serving, feature consistency, monitoring, feedback, retraining, rollback, and human use have not been designed.
Capabilities / 03
What Rokad can deliver.
01
Problem framing, target definition, baseline, and economic evaluation
02
Data discovery, quality, labelling, feature, and leakage assessment
03
Classification, regression, ranking, recommendation, forecasting, anomaly, and optimisation models
04
Experiment tracking, reproducible training, validation, and model comparison
05
Batch, real-time, edge, or embedded inference integration
06
Monitoring, drift, performance, fairness, feedback, retraining, and rollback
07
Product interfaces, decision workflows, APIs, and managed operation
Solution components / 04
The system behind the visible product.
Decision definition
Who uses the prediction, when it arrives, which action follows, what errors cost, and how success is measured.
Data and feature pipeline
Sources, labels, joins, timing, transformations, quality checks, feature consistency, lineage, and reproducibility.
Model and serving
Baselines, algorithms, validation, calibration, inference, performance, scale, versioning, and fallback behaviour.
Learning operations
Production outcomes, drift, feedback, thresholds, review, retraining, approval, rollout, and model retirement.
Use cases / 05
Where this capability creates practical leverage.
Forecasting and planning
Estimate demand, capacity, inventory, workload, revenue, risk, or operational volume with uncertainty and feedback.
Recommendation and ranking
Prioritise products, content, actions, leads, cases, or opportunities using behaviour, context, and constraints.
Risk and anomaly detection
Identify unusual transactions, device behaviour, quality patterns, operational events, or security signals for review.
Classification and routing
Categorise documents, requests, customers, events, or records and direct them into the appropriate workflow.
Architecture and integration / 06
Designed to fit the wider technology environment.
Training-serving consistency
Use aligned feature definitions, timestamps, transformations, schemas, and validation across historical training and live inference.
Baseline and fallback
Compare models against simple rules and preserve deterministic or previous-model behaviour when confidence or system health is insufficient.
Feedback and delay
Account for when true outcomes arrive, how user actions affect labels, and how model influence changes future data.
Quality and control / 07
Production requirements are part of the build.
Measured behaviour
Representative evaluation data, quality criteria, failure modes, and release thresholds are defined before expanding production use.
Controlled actions
Permissions, policy checks, approval gates, audit trails, fallbacks, and escalation paths govern consequential AI behaviour.
Observable operation
Inputs, outputs, retrieval, tool calls, latency, cost, model versions, and quality trends are monitored appropriately.
Delivery / 08
A controlled path from requirement to operation.
Discover
Clarify the business outcome, users, workflows, constraints, dependencies, risks, and measurable acceptance criteria.
Architect
Define the system boundaries, data, integrations, security, operating model, delivery sequence, and technical decisions.
Build and validate
Deliver in controlled increments with stakeholder review, automated testing, documentation, and production-quality engineering.
Deploy and improve
Launch safely, establish observability and support, then improve the system using operational evidence and user feedback.
Typical deliverables
ML feasibility, target, data, baseline, and risk assessment
Data, feature, training, validation, and inference architecture
Reproducible model training and experiment pipeline
Production batch, API, streaming, edge, or embedded inference
Evaluation, drift, quality, feedback, and release controls
Model card, technical, governance, and operating documentation
Engagement models / 09
Use the delivery structure that matches the work.
Fixed-scope delivery
A defined outcome, scope, acceptance criteria, milestones, and commercial structure for a bounded project.
Dedicated product team
A stable cross-functional team delivering an evolving roadmap with shared product and engineering ownership.
Embedded specialists
Specialist engineers working inside an existing product, technology, data, design, or operations team.
Managed evolution
Ongoing reliability, security, maintenance, feature delivery, and roadmap execution after launch.
Related capabilities / 10
Continue through the wider product and technology system.
01
MLOps
Operate training, models, features, evaluation, deployment, and monitoring.
02
Computer vision
Apply machine learning to image and video tasks.
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AI integration
Embed predictive capabilities into products and workflows.
01
Software development
Custom platforms, backends, integrations, operational systems, and software modernisation.
02
Technology consulting and research
Architecture, feasibility, strategy, due diligence, vendor evaluation, and execution planning.
03
Managed technology services
Ongoing maintenance, cloud, security, reliability, support, and continuous engineering.
FAQ
Machine learning development
Scope, ownership, assumptions, delivery, security, and long-term operation are clarified before work begins.
01How do we know whether we have enough data?
How do we know whether we have enough data?
We assess sample volume, target frequency, label quality, time coverage, feature availability, leakage, representativeness, and the performance of simple baselines before committing to complex modelling.
02Can Rokad improve an existing model?
Can Rokad improve an existing model?
Yes. We can review target definition, data, features, validation, leakage, calibration, serving, monitoring, workflow integration, and business impact rather than optimising a metric in isolation.
03How are models monitored after deployment?
How are models monitored after deployment?
Monitoring can cover data quality, feature drift, prediction distribution, latency, errors, outcomes, performance, calibration, fairness, system health, and business metrics.
04Can models run in real time or on devices?
Can models run in real time or on devices?
Yes, where the data, latency, hardware, model size, update, privacy, and reliability requirements support real-time, edge, mobile, or embedded inference.
AI development
Build the learning system around the decision—not only the model.
Rokad can assess the target, data, baseline, workflow, economics, and operating requirements before production development.
Contact / 05
Bring us the difficult technology problem.
Tell us what you need to build, improve, procure, deploy, or operate. We will respond with a practical next step.