We deliver complex, data-intensive software by combining applied maths/statistics with solid engineering. Our role is hands-on: we assemble cost-effective professional teams, lead delivery (project/program), and keep solutions aligned to business goals through frequent validation.

When clients use ERP/CRM, we augment their stack (not replace it) with practical GenAI add-ons—assistants with citations, document intelligence in workflows, policy-as-code checks, and change-impact analytics. AI features remain explainable and auditable under ISO/IEC 42001 and the NIST AI RMF.

To make delivery repeatable, we use industry tooling only where it adds value: SBOMs (CycloneDX/SPDX) for supply-chain visibility, optional OSCAL evidence for audits, and policy engines (OPA in CI/services; Kyverno for Kubernetes admission) to keep environments consistent.

• Sourcing & squads. Rapid assembly of engineers, applied mathematicians, and delivery leads tailored to your domain and budget.
• Complex delivery. Probabilistic modelling, optimisation, time-series/simulation, streaming data pipelines, system integration, and cloud/K8s operations.
• GenAI in enterprise workflows. RAG assistants with citations, automated document processing, and lightweight policy/playbook checks inside approvals and audits.

We use Gen-AI where it is useful and defensible: to speed up research, reduce routine work, and keep decisions traceable. Our team blends applied maths/ML with domain context and documents every AI step for audit and handover.

Patents. Gen-AI–assisted prior-art search and technical-effect mapping aligned with EPO practice on computer-implemented and AI inventions (focus on “further technical effect”/technical contribution). We collaborate with selected patent counsel to shape protectable claims.

Music & media. Simple tooling that streamlines rights clearance (sync/master/publishing) so usage is licensed rather than risky, with an audit trail by default.

Enterprise/ERP. Agentic Gen-AI helpers and workflow automations that respect regulatory constraints and remain explainable and auditable under ISO/IEC 42001 and the NIST AI RMF. Where needed, evidence can be exported via OSCAL and SBOM standards for supplier assurance.

We help organisations turn questions into clear, defensible answers using modern statistics and data analysis. Our focus is on careful study design, correct methods, and transparent reporting, so that decisions based on data are both practical and trustworthy.

From research question to statistical model

Every good analysis starts with a well-posed question. We work with you to:

• formulate the research problem and translate it into a precise mathematical/statistical model,
• define the statistical hypotheses to be tested,
• choose appropriate study designs (e.g. experiments, surveys, observational studies),
• select the right methods and tests (classical statistics and, where helpful, modern machine-learning/“AI” techniques).

Planning, sample size and data collection

A powerful analysis depends on good data. We support you with:

• sample size calculation to ensure your study has enough power to detect meaningful effects,
• definition of inclusion/exclusion criteria and measurement protocols,
• design of questionnaires, case report forms or lab data structures,
• practical advice on data quality and documentation.

Modern methods for complex data

Depending on the problem, we combine classical statistical techniques with modern methods for large and complex datasets, such as:

• regression, ANOVA, survival analysis, multivariate methods,
• time-series and panel-data models,
• resampling and simulation approaches,
• machine-learning models for pattern detection, prediction and segmentation.

The goal is not “AI for its own sake”, but choosing the simplest method that answers the question well, and explaining clearly how it works and what its limitations are.

Clear results and communication

We place strong emphasis on readable, reproducible results. This includes:

• structured reports with clear conclusions (not just tables and p-values),
• visualisations that highlight the key findings without hiding uncertainty,
• documentation of methods and assumptions,
• support in preparing material for publications, presentations or stakeholder meetings.

Experience across domains

We have experience with both theoretical and applied statistical problems in areas such as:

• medicine (clinical and observational studies, outcomes, risk factors),
• chemistry (experiments, optimisation, stability studies),
• sociology and education (surveys, interventions, longitudinal data),
• sports (performance indicators, rating systems, decision support for clubs).

In all of these fields, our role is the same: to build a bridge between the domain experts and the mathematics behind the data, so that decisions are based on solid evidence rather than intuition alone.

Whether you need help designing a new study, rescuing a difficult dataset, or validating an existing analysis, we offer pragmatic, mathematically sound support tailored to your project and budget.