Free benchmarked assessments, three downloadable strategic guides, and our public playbooks on data governance, model risk, and production AI.
This page is our knowledge centre of field-tested insights, frameworks, and technical guides, drawn from our experience building and scaling production-grade decisioning systems. We believe in sharing our expertise openly.
Below are the core methodologies and best practices that inform every client engagement.
Where do you stand against the frameworks that matter? Each assessment produces a benchmarked maturity score and a gap analysis you can share with your team.
Personalised maturity score
Scored across every dimension of your operating model, not a one-number summary.
Gap analysis
Surfaced against the regulator's framework, not against the average peer.
Roadmap recommendations
Prioritised by impact and sequenced for delivery — not a generic best-practice list.
BCBS 239 · SR 26-2 · DAMA-DMBOK
Benchmark your credit-risk data governance against BCBS 239, the U.S. federal banking agencies' SR 26-2 model risk supervisory letter (issued April 2026, replacing SR 11-7), and DAMA-DMBOK. The assessment scores your maturity across 12 dimensions and produces a gap analysis you can share with your audit committee.
EU AI Act · NIST AI RMF · ISO/IEC 42001 · SR 26-2
Where do your AI/ML practices stand against the EU AI Act's high-risk classification, NIST AI RMF, ISO/IEC 42001, and SR 26-2 model risk management (issued April 2026, replacing SR 11-7)? Fourteen questions to a benchmarked score plus a roadmap to close the gaps your next enterprise procurement will ask about.
KSA PDPL · GDPR · UK GDPR · AU Privacy Act · India DPDPA · CCPA/CPRA
Multi-regime readiness in one assessment — KSA PDPL, GDPR, UK GDPR, AU Privacy Act, India DPDPA, and CCPA/CPRA. Twenty-two questions; a benchmarked maturity score and a remediation map across every regime that touches your data.
Standalone playbooks pulled from senior engagement work. Each runs 18–21 pages, written for a senior practitioner audience. Free, delivered by email.
Implementation playbook for credit-risk transformation in regulated lenders — BCBS 239 data lineage, SR 26-2 model risk management, IFRS 9 ECL reconciliation, and the operational patterns we used scaling a national bureau.
Playbook covering dbt architecture for regulated data, governance integration, semantic-layer design, and migration sequencing. Drawn from five years of platform evolution at a national credit bureau.
Production ML playbook — MLOps architecture, feature-store design, drift monitoring for credit scorecards, challenger-model A/B testing, and GenAI guardrails for regulated industries.
A successful data programme is built on a solid strategic foundation. Technology is only an enabler; the real work lies in defining the business case, establishing robust governance, and ensuring the organisation can trust its data.
We deliver value incrementally to build momentum and ensure alignment. Our standard engagement follows a proven, week-by-week pattern.
Read the playbookPoor data quality is not an IT problem; it is a business problem with a direct financial impact. Every $1 in proactive prevention saves $10 in remediation.
Read the playbookData privacy regulations are a baseline requirement. Our framework embeds compliance into your data architecture across four key pillars.
Read the playbookStrategy is meaningless without execution. We build robust, scalable data platforms using best-in-class tools and methodologies from the modern data stack.
Separating data transformation logic is key to building trust and maintainability. In our dbt projects, we enforce a clear, three-tiered structure.
Read the playbookFast dashboards are a sign of a well-designed data model. Our approach to high-performance Power BI solutions follows ten core commandments.
Read the playbookCapturing every business event allows for both real-time insights and perfect auditability. For fraud detection and instant credit approvals, we implement event sourcing.
Read the playbookA model in a notebook provides zero business value. Our focus is on MLOps — the discipline of reliably and efficiently getting models into production and keeping them there.
Our 8-week framework gets high-performing, compliant models into production quickly and safely — from feature engineering to continuous monitoring.
Read the playbookDecoupling feature generation from model training ensures training/serving consistency. Our checklist ensures the feature store actually gets used.
Read the playbookThis is our deepest area of domain expertise. We apply the principles of data engineering and MLOps to the specific, high-stakes challenges of credit and financial risk management.
Scaling a credit bureau isn't just a tech challenge — it's an ecosystem transformation across regulation, data-sharing incentives, and commercial strategy.
Read the playbookCredit scoring is evolving from static, periodic models to dynamic, real-time contextual decisions. The trends we help clients implement.
Read the playbookIf you're facing challenges in data, risk, or AI/ML, the next step is a discovery call. We'll discuss how these principles apply to your specific problems.