Predictive machine learning
We build classification, forecasting and ranking models around an explicit decision and error cost. Baselines and holdout tests show whether complexity adds genuine value.
AI & Machine Learning
AI and machine learning that survives contact with production
We turn a defined business decision into a testable AI system, covering data preparation, modelling, application integration and the controls needed after release.
Capabilities
What this service delivers
Generative AI applications
We create retrieval, extraction and generation workflows with citations, structured outputs and permission-aware context. Evaluation sets expose unsupported answers before users do.
Computer vision
Image classification, detection and document-vision pipelines are designed for the actual capture conditions. We account for label quality, drift and human review of uncertain cases.
Data and feature pipelines
Repeatable ingestion, validation and feature computation keep training and serving logic aligned. Lineage and quality checks make bad inputs visible rather than silently corrupting predictions.
MLOps and monitoring
We package models, automate controlled releases and monitor service health alongside model behaviour. Retraining is triggered by evidence, not treated as an arbitrary calendar task.
AI integration
Models are connected to existing products through stable APIs, queues or batch jobs. We design fallbacks, latency budgets and review paths so the wider workflow remains dependable.
Technology stack
Tools used for the work
PythonPyTorchscikit-learnHugging FaceFastAPIMLflowPostgreSQLDockerKubernetes
Process
How the engagement runs
01
Frame the decision
We define the user, decision, acceptable errors and operational constraint before selecting a model. A simple baseline provides a useful commercial and technical comparison.
02
Audit and prepare data
We inspect provenance, coverage, leakage risks and labelling consistency. The output is a reproducible dataset and a candid record of gaps that limit performance.
03
Experiment and evaluate
Candidate approaches are tested against versioned examples and segment-level metrics. Reviews include difficult cases, calibration and the cost of false positives and negatives.
04
Integrate and operate
The chosen system is deployed behind controlled interfaces with observability and fallback behaviour. Production feedback is captured for diagnosis and carefully governed improvement.
01
Start with the decision, not the model
A model score is not a business outcome. Good AI architecture begins by identifying who consumes a prediction, when it arrives, what action follows and how a mistake is corrected. That framing often reveals that ranking, rules or assisted review is more suitable than full automation.
We compare learned approaches with understandable baselines and preserve a deterministic route for cases outside the model’s competence. This makes value and risk inspectable, while avoiding an expensive demonstration that never fits the day-to-day process.
- Defined user and decision point
- Measurable acceptance criteria
- Costed error classes
- Human review and fallback route
02
Data quality is an engineering concern
Historical data reflects old policies, missing events and inconsistent definitions. Random train-test splits can leak future information or place records from the same entity on both sides, producing impressive but misleading results. We select temporal or grouped validation where the deployment setting demands it.
Training code, feature logic and data snapshots are versioned together. Schema checks, distribution tests and lineage records help distinguish a model problem from a changed source system, and sensitive fields are minimised rather than copied into every experimental dataset.
03
Production AI needs observable boundaries
Serving architecture depends on latency, volume, privacy and update frequency. Some predictions belong in a nightly batch; others need an online service with cached features and strict timeouts. Generative systems also require context controls, output validation and protection against instructions embedded in retrieved material.
Evaluation continues after launch because inputs and user behaviour change. We monitor technical failures, input drift and task-specific quality signals separately, then investigate samples before retraining. A competent build makes uncertainty visible and provides an ordinary software path when the model is unavailable.
FAQ
Common questions
How much data is needed for a custom machine learning model?
It depends on the task, signal quality, class balance and acceptable error rate. We begin with a data audit and baseline; transfer learning or rules may reduce the amount required, but no responsible estimate can be made from row count alone.
Can you add generative AI to our existing product?
Yes, when there is a defined user task and suitable information source. We integrate through APIs and design retrieval, permissions, citations, evaluation and fallback behaviour around the existing application.
How do you measure whether an AI system works?
We use task-specific offline tests and operational measures tied to the decision being supported. Overall accuracy is rarely enough, so results are examined by segment, error type, confidence and human-review outcome.
Do we need to move our data to a new platform?
Not necessarily. Data can often remain in current warehouses or operational systems, with governed pipelines supplying only what the model needs; residency, latency and access controls determine the design.
Can an existing model be improved?
Usually, but the cause of poor behaviour must be diagnosed first. Label defects, leakage, changed inputs and workflow design often matter more than trying a larger algorithm.
Related services
Connected engineering capabilities
Plan a ai & machine learning development engagement.
Share the problem, current system and constraints. We will respond with the questions needed to define a credible next step.