Client: Global Consumer Goods Brand

Challenge: Third-party digital-shelf data was available but difficult to interpret and use, limiting visibility into online marketing and product performance.

Activities title: Key Activities
Activities: Digital-shelf data analysis, structuring and exploring third-party performance data to surface relevant patterns;
Visual analytics design, translating complex performance data into clear and comparable visual views;
Dashboard development, consolidating key digital-shelf information into a more accessible performance view;
Performance insight synthesis, using the analysis to improve visibility into marketing and digital-shelf performance

Expertise: Data & Analytics
Challenges: Poor Data Use & Visibility

Client: Applied Machine Learning Cases

Challenge: A series of applied cases focused on understanding how different machine-learning approaches perform across varied prediction problems, with particular attention to model selection, validation, class imbalance, error trade-offs and when additional model complexity is justified.

Activities title: Key Activities
Activities: Data preparation & feature scaling, preparing structured datasets for modelling and testing the impact of preprocessing choices;
Classification modelling, applying logistic regression and other classification approaches and interpreting predictive performance;
Imbalanced-data handling, assessing class imbalance and its effect on model behaviour and evaluation metrics;
Tree-based & ensemble modelling, working with decision trees, random forests and XGBoost and comparing their performance across cases;
Neural-network modelling, developing and evaluating multi-layer neural networks and exploring transfer-learning approaches;
Model validation & comparison, using train/test validation, confusion matrices and comparative performance metrics to assess alternative models;
Threshold & error-cost analysis, adjusting classification thresholds and examining trade-offs between different error types in operational contexts;
Embeddings & text-generation workflows, exploring representation and generation approaches for unstructured text;
Technical-to-business translation, connecting model performance to practical implications such as cost, ROI, payback and build-versus-vendor decisions.

Expertise: Data & Analytics · AI & Automation

Client: Fraud Analytics Case

Challenge: Transaction data was messy and inconsistently structured, making it unsuitable for reliable fraud analysis without first creating a clean and analysis-ready dataset.

Activities title: Key Activities
Activities: Data-quality assessment, identifying missing, inconsistent and incorrectly structured transaction data;
Data cleaning & transformation, using Pandas to correct data-quality issues and standardise fields;
Transaction data restructuring, converting raw records into a consistent analytical structure suitable for downstream fraud analysis;
Data validation, checking the transformed dataset for consistency and analytical usability

Expertise: Data & Analytics
Challenges: Poor Data Use & Visibility

Client: Amsterdam Rental Market Case

Challenge: Assessing investment attractiveness required combining fragmented market, property-price, regulatory and operating-cost data rather than relying on headline rental prices. The analysis also needed to account for neighbourhood-level economics and identify patterns that simple averages could obscure.

Activities title: Key Activities
Activities: Data preparation & integration, cleaning 10,480 rental listings and combining them with district-level property-price, regulatory and operating-cost data;
Investment economics framework, calculating neighbourhood-level net yields rather than evaluating opportunities on rental income alone;
Regulatory modelling, incorporating local rental constraints into the assessment of investment attractiveness;
Statistical modelling, building and interpreting OLS models to understand rental-price drivers and test alternative explanations;
Residual & behavioural analysis, investigating unexplained pricing patterns and identifying a systematic search-funnel effect between smaller and larger units;
Evidence-led reframing, using findings that challenged the initial assumptions to redirect the analysis toward the factors that better explained investment performance;
Decision-oriented market assessment, translating statistical and regulatory findings into a neighbourhood-level view of where investment economics were strongest.

Expertise: Strategy · Data & Analytics
Challenges: Poor Data Use & Visibility

Client: Digital Transformation Practice

Challenge: Prospect sourcing and qualification depended on time-intensive manual research, making it difficult to scale outreach without either increasing effort proportionally or lowering targeting quality. The solution needed to automate the upstream workflow while keeping human control over who ultimately entered outreach.

Activities title: Key Activities
Activities: End-to-end workflow design, mapping the sourcing, enrichment, qualification and review process into a repeatable automated pipeline;
Automation architecture & integration, building an n8n/Python workflow connecting contact sourcing, external APIs and LLM-based qualification;
Targeting & scoring methodology, translating ideal-customer criteria into structured qualification logic for automated assessment;
LLM-based lead qualification, evaluating ~5K contacts against predefined fit criteria and achieving an 85–95% pass rate among automatically selected leads;
Human-in-the-loop governance, introducing a mandatory review step before any qualified contact could enter outreach;
Reusable acquisition infrastructure, designing the workflow as a repeatable GTM system rather than a one-off prospecting exercise;
Efficiency & quality optimisation, reducing manual research burden while preserving the qualification threshold required for high-quality outreach.

Expertise: AI & Automation · Process & Workflow Design · Technology & Systems · Data & Analytics
Challenges: Operational Bottlenecks

Client: Built Environment Services Firm

Challenge: Strategic priorities were not translating into consistent execution across the organisation. There was no common mechanism linking priorities to measurable objectives, divisional KPIs and ongoing performance review, while implementation also required overcoming resistance within the senior team.

Activities title: Key Activities
Activities: Strategic execution diagnosis, identifying the gap between high-level priorities and executable objectives, KPIs and accountability mechanisms;
Strategy framework design, creating a multi-stage system linking strategic priorities, SWOT analysis, OKRs, divisional KPIs and performance measurement;
Governance & planning model, defining how priorities would move from strategy into recurring planning, review and decision-making routines;
Executive sponsorship strategy, taking the diagnosis and proposed framework directly to managing partners and securing senior backing before wider implementation;
Stakeholder alignment & influence without authority, navigating resistance within the senior team and building sufficient support to implement the new approach;
Framework institutionalisation, embedding the system into firm-wide planning until it became the organisation’s primary strategy and governance mechanism.

Expertise: Strategy · Operating Model · Governance · Adoption
Challenges: Lagging Adoption

Client: Built Environment Services Firm

Challenge: Digital delivery relied heavily on outsourced specialists, while workflows and capability varied across 10+ teams, creating cost, speed and consistency problems. The transformation needed to redesign how work was delivered and embed new tools and ways of working across the organisation.
Activities title: Key Activities
Activities: Transformation opportunity & business case, quantifying the cost and productivity impact of external dependency and securing partner-level approval for company-wide change;
Workflow redesign & standardisation, mapping existing delivery processes across teams and establishing consistent new ways of working;
Technology selection & implementation, partnering with team leads and IT to select and roll out new digital tools across 10+ teams;
Digital operating setup, building reusable templates, databases, automations, procedures and governance standards and migrating teams from legacy workflows;
Capability & adoption strategy, assessing uneven skill levels and establishing a distributed champion network to support implementation across teams;
Workflow-based training & enablement, designing hands-on workshops, demonstrations, exercises, documentation and reusable learning materials around live project work;
Continuous adoption support, establishing peer support and feedback loops to identify user issues and refine the transformation after rollout;
Performance & adoption measurement, achieving ~40% faster output, 100% adoption of the new workflows and ~70% lower outsourcing costs.

Expertise: Strategy · Process & Workflow Design · Technology & Systems · Implementation · Adoption
Challenges: Operational Bottlenecks · Lagging Adoption

Client: Product Teams

Challenge: Product knowledge was fragmented across tools such as Slack, Jira and Notion, making it difficult for teams to retrieve context quickly and creating uncertainty around whether the problem was significant enough to justify building a dedicated product.

Activities title: Key Activities
Activities: Market & problem discovery, testing the fragmented-knowledge problem across four candidate customer segments through 60+ interviews;
User research & JTBD mapping, translating interviews and observed product-manager workflows into personas, friction points and prioritised user needs;
Segment selection & positioning, identifying product teams as the initial target segment and defining the product proposition around a single source of truth across fragmented knowledge;
Product strategy & roadmap, converting validated needs into product vision, user journeys, feature priorities and backlog;
Design-partner acquisition & staged validation, securing a ~€23M UK/NL design partner and testing the proposition progressively through narrative, prototype and MVP stages;
MVP solution design & delivery, defining data flows, API connectors, permissions and sync logic and shipping a functional RAG/API-powered MVP integrating Slack, Jira and Notion;
Evidence-led iteration, using observed behaviour, user feedback and AI-output evaluation to reprioritise features when real usage contradicted earlier interview assumptions.

Expertise: Strategy · Solution Design · AI & Automation · Technology & Systems
Challenges: Unclear Technological Choices · Operational Bottlenecks