Artificial intelligence, machine learning models and neural computation

Service Capability

AI & Machine Learning

ML model engineering, production deployment, MLOps pipelines, and integration of predictive systems into enterprise applications — engineering-first, not research-first.

What we do

Most ML problems are not model problems. They are engineering problems: feature pipelines that don't run reliably, models trained in notebooks that never reach production, predictions that degrade silently over months without anyone noticing, and data scientists working in isolation from the software engineers who build the systems that would consume their outputs.

Our ML engineering practice focuses on the production side of the ML lifecycle. We build the feature engineering pipelines, model training infrastructure, serving layers, and monitoring systems that make ML actually useful in an enterprise environment. We work alongside data science teams to bridge the gap between experimentation and production — not to replace researchers, but to make their work deployable.

We also have practical experience integrating large language models into production applications — retrieval-augmented generation systems, document processing pipelines, and LLM-powered workflows — grounded in what actually works in production rather than what demos well.

Problems we address

  • — Models are built and validated in notebooks but never make it into production applications
  • — Deployed models have no monitoring — nobody knows if they've drifted or degraded since launch
  • — Data scientists lack the platform engineering support to build reliable training pipelines and serving infrastructure
  • — Experiment tracking is ad hoc — there is no reproducible record of which features and hyperparameters produced the best model
  • — LLM integrations were built as prototypes and cannot be deployed reliably or at scale
  • — Re-training a model requires a manual, undocumented process only one person in the organisation understands

Capabilities

Feature Engineering & Feature Stores

Reliable, versioned feature pipelines built with Apache Spark and managed via Feast or a comparable feature store, ensuring training and serving use the same feature computation logic.

Model Training Pipelines

Automated, reproducible training pipelines with experiment tracking via MLflow or Weights & Biases. Hyperparameter tuning, cross-validation, and model versioning as code rather than manual processes.

Model Serving (REST / gRPC)

Production model serving using BentoML, Seldon, or custom FastAPI/gRPC endpoints deployed on Kubernetes. Includes latency SLA definition, A/B testing framework, and shadow mode deployment for safe rollouts.

MLOps & CI/CD for ML

End-to-end ML pipelines that treat model training and deployment as software delivery — with automated testing, integration with CI/CD systems, staged promotion, and rollback capabilities.

Model Monitoring & Drift Detection

Data drift, concept drift, and prediction distribution monitoring with automated alerting. Dashboards showing model performance over time and trigger-based re-training workflows when quality thresholds are breached.

LLM Integration for Production

Retrieval-augmented generation (RAG) systems, LLM-powered document processing, prompt engineering and evaluation frameworks, and cost-optimised LLM routing — built to run reliably in production, not just in demos.

Our approach

Engineering-first, not research-first

We approach ML as software engineering with probabilistic outputs. This means production readiness is a constraint from day one — not something to think about after the model achieves a good AUC on a holdout set. Architecture decisions are made with serving latency, scalability, and operational cost in mind throughout.

Baseline first, optimise second

We start with the simplest model that could provide business value, get it to production, and then iterate. A logistic regression in production delivering real decisions is worth more than a neural network still being tuned in a notebook. This approach also gives us baseline performance data that makes subsequent improvements measurable.

Integration with your data platform

ML systems are only as good as the data they consume. We build ML infrastructure that connects cleanly to your existing data platform — using the same feature tables, the same orchestration layer, and the same monitoring patterns as your data engineering work, rather than creating a separate, isolated ML silo.

Honest about what LLMs can and cannot do

LLMs are genuinely useful for specific tasks. They're unreliable for others. We help organisations identify where LLM-powered automation actually reduces cost or improves accuracy, and where it introduces unacceptable hallucination risk or latency.

Delivery process

  1. 1

    Problem scoping

    Define the business decision being supported, the metric that will measure success, and the constraints (latency, explainability, fairness) that apply. Establish whether ML is actually the right tool for the problem.

  2. 2

    Data audit

    Assess available training data for volume, quality, label reliability, and leakage risk. Identify the feature engineering work required and any gaps that need to be closed before training can begin.

  3. 3

    Baseline model & feature pipeline

    Build a simple baseline model with production-quality feature pipelines and experiment tracking. Establish performance benchmarks before any optimisation work begins.

  4. 4

    Serving layer & production deployment

    Build the serving infrastructure, integrate with consuming applications, and run shadow mode or A/B tests before full rollout. Document the serving SLA and failure modes.

  5. 5

    Monitoring & iteration

    Deploy drift detection and performance monitoring. Establish re-training triggers and cadence. Hand over the full MLOps workflow to your team with runbooks and training.

Technologies

Languages & Frameworks

Pythonscikit-learnPyTorchTensorFlow

Experiment Tracking

MLflowWeights & Biases

Serving & Deployment

BentoMLSeldonRay ServeFastAPIKubernetes

Feature Engineering

Apache SparkFeastTecton

Distributed Training

RayHorovodAWS SageMaker

LLM Tooling

LangChainLlamaIndexOpenAI APIpgvector

Industries served

Financial Services Healthcare Retail Manufacturing

Related case studies

Healthcare

Healthcare Data Platform

Predictive analytics layer built on top of a unified data platform, enabling readmission risk scoring and resource planning models deployed in production clinical workflows.

Frequently asked questions

It depends on whether your use case is differentiating. If you're solving a problem that many organisations face — fraud detection patterns, standard churn prediction, document classification — a vendor model or fine-tuned open-source model is often faster and cheaper than building from scratch. Custom model development makes sense when your data is unique, your problem has specific constraints that general models don't handle, or the model's behaviour is a competitive advantage. We help you make this decision during the scoping phase rather than assuming build is the right answer.
LLMs are well-suited for tasks where natural language understanding, generation, or summarisation provides value and where some level of output variability is acceptable. They work well for document extraction, internal knowledge base Q&A, drafting structured outputs from unstructured inputs, and code assistance. They are poorly suited for tasks that require consistent arithmetic, precise factual recall, or zero hallucination tolerance. We start every LLM engagement with an evaluation framework that measures actual output quality on your specific data before committing to a production architecture.
A baseline model with production-quality serving infrastructure can typically be deployed in six to ten weeks, assuming the training data is accessible and reasonably well-prepared. More complex models with significant feature engineering requirements, or use cases requiring explainability frameworks or regulatory documentation, take longer. The biggest variable is data readiness — organisations that invest in a solid data platform first see ML projects complete significantly faster and with fewer surprises.
It depends entirely on the problem and the signal-to-noise ratio in your data. Tabular classification problems can produce useful models with a few thousand labelled examples. Deep learning tasks typically need orders of magnitude more. We assess data volume and quality during the scoping phase and will tell you honestly if the current dataset is insufficient — rather than building a model and discovering the limitation later. We also assess whether transfer learning or data augmentation techniques can reduce the data requirement.
We design ML systems so your team can own and maintain them after handover. This includes automated re-training triggers, documented runbooks for manual re-training, monitoring dashboards your data scientists can interpret, and clear ownership of the feature pipeline and serving infrastructure. We deliberately avoid creating ML systems that only the people who built them can operate — dependency on us for ongoing model maintenance is not a sustainable outcome for either party.

Get your ML models into production

Whether you need to operationalise existing data science work or build an ML system from scratch, our team can help you deliver production-quality ML reliably.

Talk to our ML team View case studies