Empowering organizations with innovative AI solutions that drive efficiency, transparency and trust.
Every capability below is drawn from work the Hub has delivered, prototyped or architected, not from a catalogue. The common thread is deterministic-first design: the decision path stays auditable, and generative components are used where they add value rather than where they are easiest to insert.
Pipelines that turn unstructured PDFs into structured records through parsing, OCR and layout analysis, loaded into relational stores. Rule-based extraction is preferred wherever it will hold, so results remain explainable and reproducible.
Deduplication and canonicalisation of people, organisations and departments across large inconsistent datasets, combining string similarity, phonetic matching and normalised reference data.
Embedding-based routing of free-text queries against defined intents, with similarity scoring, entity-aware filtering, calibrated confidence thresholds and the ability to abstain rather than guess.
Retrieval architectures connecting scientific, policy and organisational corpora to language models, designed around source grounding, provenance and traceability rather than fluency alone.
Orchestrated multi-agent flows with defined roles, graph-backed retrieval and streaming interfaces, benchmarked against single-agent baselines before they are trusted with a task.
Security posture and vulnerability reporting in which intent resolution, query planning and evidence assembly are fully deterministic, and the language model is confined to wording an already-assembled result.
Cloud and local data estates across relational and graph stores, including migrations, validation queries, access control and self-healing retrieval workflows.
Secure message exchange, data validation and connector coordination across national administrations, delivered against specifications, quality assurance and service-level commitments.
Bias auditing, explainability evidence and trustworthiness assessment aligned to ISO/IEC 42001, the EU AI Act and GDPR, produced as artefacts a reviewer or auditor can act on.
Independent assessment of proposed solutions for methodological validity, technical feasibility, data dependencies, interoperability and organisational fit, ahead of commitment.
Reproducible deployment of AI and security services, event-driven integration patterns and continuous integration workflows with automated regression protection.
Analytical workflows over science, technology and innovation data, from operationalisation and preparation through analysis, validation and interpretation.
Training is treated as two obligations. The first is to the model: trained on data we can account for, evaluated against the failure that actually matters, and released with its limitations written down. The second is to the organisation receiving it, which has to operate the result long after we have gone.
A working research programme runs alongside delivery. Some of it is peer-reviewed and externally funded, some is early and openly incomplete. Both are listed here, because a research claim that hides its unfinished parts is not worth reading.
Indicators for measuring AI capability, adoption and impact, with emphasis on validity and interpretability in science, technology and innovation systems.
Quantitative analysis of scientific activity, collaboration and emerging technologies, including methodological assessment of indicator validity and responsible evaluation practice.
How AI changes scientific knowledge production, evidence generation and scholarly communication, examined through validity, reliability, transparency and reproducibility.
The consequences of generative systems for evidence production, research quality and the evaluation of research itself.
A framework treating explanation as an artefact produced during inference rather than a visualisation reconstructed afterwards, built on epistemic subspaces covering semantic validity, temporal volatility, interaction continuity and coherence, with declared instantiations across cyber, legal and medical domains.
Agent-based approaches to literature analysis, structured appraisal and other research-intensive knowledge tasks, with evaluation designed in.
Learned interfaces between binary embeddings on constrained hardware and full-precision reasoning in the cloud, assessed on accuracy, latency and energy against quantisation baselines. Early stage.
Constrained-optimisation treatments of drift between training epochs, measured in weight, output or attribution space, aimed at regularising the trajectory without biasing the converged solution. Exploratory.
Synthetic corpus generation and time-frequency detection of clinically significant events, evaluated on how early a warning arrives and how often it is wrong.
Assessment of AI systems across transparency, accountability, robustness, reproducibility, provenance, human oversight and contestability, mapped to European and OECD frameworks.
Turning academic evidence and methodological expertise into policy-relevant and industry-relevant concepts across European research and innovation programmes.
The protection consequences of desynchronisation between national registries, where records persist as active long after the underlying activity has ceased.
Forty-eight documented capabilities across thirteen families. Depth is stated as the highest level evidenced in each family, and where that level is working knowledge rather than delivery experience, it says so.