Services are organised as repeatable work packages that combine applied AI with policy analysis methods used in EU-level work. Each package defines inputs, outputs, and assurance measures. Technical implementation details are kept internal, but deliverables are designed to be auditable, reusable, and suitable for inclusion in formal analytical workflows.
Large and fragmented evidence bases where policy teams need rapid access to relevant material, traceable citations, and defensible synthesis.
Legal acts and guidance, evaluations and studies, consultation responses and stakeholder submissions, technical annexes, internal notes where available, and curated public sources where appropriate.
Evidence maps that organise findings by theme and question. Claim-to-source tables that link substantive statements to specific source passages. Structured briefs and Q&A packs that distinguish between sourced evidence, interpretation, and open questions.
Substantive statements are grounded in retrieved sources and flagged where evidence is weak, inconsistent, or absent. Outputs support analysis and drafting, but do not substitute for legal interpretation or formal legal review.
Unclear obligations, inconsistent definitions, overlapping scopes, and practical enforcement or implementation gaps across instruments.
Primary legislation and implementing acts, guidance and interpretative materials, enforcement documentation and monitoring frameworks, relevant case law where appropriate, and stakeholder evidence on operational bottlenecks.
Obligation registries that define actors, duties, triggers, timelines, exceptions, and dependencies. Cross-instrument matrices that compare coverage and consistency. Gap taxonomies that distinguish scope gaps, definition gaps, enforcement gaps, data gaps, and implementation gaps. Prioritised gap logs linked to supporting evidence.
Outputs are presented as traceable comparisons and operational implications. Where interpretation is uncertain, uncertainty is documented explicitly and the need for legal validation is flagged. The service does not provide legal advice and does not assign compliance determinations.
Desk research tasks that require multi-step reasoning, cross-checking, and consolidation across sources, where manual approaches are slow and difficult to keep consistent.
Public repositories and databases, client corpora and annexes, prior studies and evaluations, and structured research notes. Where required, work is delivered in environments that minimise data transfer and preserve control of intermediate artefacts.
Research logs and source trails. Evidence packs structured around policy questions and hypotheses. Comparative summaries across jurisdictions, instruments, or stakeholder positions. Structured briefing notes designed for review, reuse and updating as new evidence becomes available.
Human validation gates are defined at the start of each engagement. Outputs distinguish retrieved evidence from model-generated synthesis and record assumptions and uncertainty. Where evidence is ambiguous, the ambiguity is preserved and documented rather than resolved by inference.
Turning qualitative and quantitative evidence into modelling-ready inputs for cost, burden, enforcement, and policy option comparison analysis.
Prior impact assessments and evaluations, administrative data, stakeholder evidence and surveys, market studies, and technical reports with quantitative parameters relevant to policy options.
Parameter registers with provenance and citations. Assumptions logs that record definitions, baselines, scenarios, and uncertainty. Sensitivity drivers and documented calculations suitable for inclusion in Better Regulation-aligned modelling. Coverage maps that show which parameters are evidenced, estimated, or not available.
All parameters include provenance and an evidence-strength note. Estimates are clearly separated from evidence-based inputs. Where data gaps exist, they are documented and handled through explicit scenarios rather than implicit assumptions.
Where structured data analysis is required in addition to document evidence, analytical methods can be applied case-by-case. This includes NLP-based extraction and classification, topic discovery and clustering for large qualitative corpora, supervised models for triage and coding, and time-series analysis to support monitoring and change detection. The method choice is driven by the decision need, the availability and quality of data, and the assurance level required.