The engagements that became CereBrumha
This is the ledger — where each part of the platform was first built, and for whom. Twenty-five years of enterprise work, grouped by the discipline it taught and mapped to the module it became.
Named with permission where given. Engagements delivered in senior roles at Global Payments, Coforge, Hexaware, LTIMindtree, Genpact, Capgemini, IBM and Oracle.
Became DiscoveryX
Framing the problem before anyone writes code — the discipline that became the DiscoveryX loop.
| Sector | Client | Engagement |
|---|---|---|
| Banking | Deutsche Bank, US & UK | Global data warehouse architecture across London, New York and Singapore for customer, risk and performance |
| Banking | Banco de Chile | Risk analytic engines — credit, market, ALM interest rate and liquidity, FTP — plus budgeting and financial reporting |
| Banking | Barclaycard, UK | Credit card analytics, self-serve reporting and the shared warehouse environment |
| Banking | Citibank, Singapore | Issuer, acquirer and chargeback analytics — enterprise data model design |
| Banking | Wells Fargo | Data strategy and data ecosystem for the capital markets line of business |
| Banking | Banco Mercantil, Venezuela | Analytic strategy across 13 subject areas with a credit card analytics focus |
| Banking | Banco Sicredi, Brazil | Custom fund transfer pricing application using cubic spline algorithms |
| Capital mkt | Northern Trust, USA | Basel II capital calculator integration; reporting and data integration factory plan |
| Media | Fox, USA | Azure cloud and Cortana intelligence architecture — use case definition |
| Media | Warner Brothers | HR analytics for the complete Warner Brothers group |
| Manufacturing | Freeport-McMoRan, USA | End-to-end HR warehouse — benefits, payroll, compensation — plus supply chain inventory and sourcing |
Became BottomX
Model design against real P&L outcomes — the discipline that became the BottomX algorithm layer.
| Sector | Client | Engagement |
|---|---|---|
| Capital mkt | Franklin Templeton, USA | Customer analytics and CRM predictive analytics — strategy roadmap and execution plan |
| Insurance | Travelers Insurance, USA | Galileo platform used to deliver customer analytics and geospatial capability |
| Insurance | Aflac | Cognitive solution for call-centre operations, voice chatbot intervention, HR analytics, fraud and claims |
| Insurance | AIG Investments, USA | HR predictive models and the customer warehouse — design and execution |
| Insurance | Westfield Insurance, USA | Balanced scorecard strategy using strategy maps and strategy themes |
| Insurance | Northbridge | Intelligent document processing for large-scale unstructured data, reducing manual handling |
| Conglomerate | General Electric, USA | Execution strategy for HR predictive analytics and the global HR self-serve platform |
| CPG | Johnson & Johnson / Procter & Gamble | Customer and HR analytics for retail; disease progression models forecasting diabetes patient data; geo-location innovation cases |
| Energy | First Solar, USA | Predictive model maximising solar panel output for increased power generation |
| Utility | Alabama Power, USA | Traditional plus weather big data for generation optimisation; customer segmentation and appliance usage modelling |
| Retail | Online retailer | Market mix modelling product design |
Became FrontX
Agents, summarisation and generative BI in production — the discipline that became the FrontX experience layer.
| Sector | Client | Engagement |
|---|---|---|
| Utility | Avangrid | Enterprise-grade voice-to-text-to-summarisation analytics over contact centre data |
| Hospitality | IHG / Holiday Inn Group | Generative BI for search queries and automated report generation |
| Healthcare | Confidential | Document summarisation framework that auto-predicts practitioner diagnosis |
| Transport | Lytx | Video analytics for driver accident detection |
| Travel | Expedia | Framework for standing up the generative AI centre of excellence and use case articulation |
| Practice | Offering and GTM | Generative AI offering plan across five categories — search, code, generate, summarise, analyse |
| Practice | Solution catalogue | Three solution classes — search on the enterprise warehouse, and industry packs for HR, finance and healthcare |
| Practice | Ecosystem | Reusable model library; partnerships with AI Planet, GoML and Exafluence |
Became TopX
Platform shape, cloud target state and semantic design — the discipline that became the TopX business and semantic layer.
| Sector | Client | Engagement |
|---|---|---|
| Insurance | Lincoln Financial | AWS migration, data science and data integration framework |
| Insurance | Voya Financial | Insurance warehouse and design framework on Azure |
| Insurance | Munich Re | Data warehouse build and related architecture work |
| Capital mkt | Russell Investments, USA | Index fund warehouse and ODS architecture; predictive analytics for customer solutions |
| Capital mkt | Russell Investments | Integrated digital platform for ABOR and IBOR reporting, orchestrating low-code, integration, reporting, API, IDP and task flow |
| Banking | BNP Paribas, Ghana | Analytic strategy roadmap design and recommendation |
| Exchange | Johannesburg Stock Exchange | Stock exchange analytic design strategy |
| Banking | HDFC Bank, India | Customer analytics and interaction management |
| Manufacturing | Philip Morris, global | Data, analytics, MLOps, AI and governance on a lakehouse and service mesh architecture |
| Media | HBO, USA | Information integration strategy on a big data integrated platform |
Became GovernX
Trust as a perimeter rather than a stage — the discipline that became GovernX.
| Sector | Client | Engagement |
|---|---|---|
| Banking | PayPal, USA | End-to-end data quality and metadata management across 16 business areas — risk, customer, finance, lending, marketing |
| CPG | Procter & Gamble, USA | MDM, data sciences, Hadoop archival and BI architecture design across multiple programmes |
| Entertainment | Disney, USA | Data lake and data governance ecosystem on AWS |
| Manufacturing | FMI | HR warehouse designed for this mining major |
Became FrontX
The last mile — reporting infrastructure at enterprise scale, and the discipline behind the FrontX presentation surface.
| Sector | Client | Engagement |
|---|---|---|
| CPG | Johnson & Johnson, USA | Reporting infrastructure for 45,000 users on 300 BI servers — TM1, Cognos, Teradata — plus the data lake build |
| Retail | OXXO, Mexico | Store sales visualisation, store clustering, workforce analytics and store financials on MicroStrategy |
| Airlines | Delta Air Lines, USA | Cognos reporting and BI consolidation strategy; contact centre generative AI analytics |
| Healthcare | IMS Health, USA | Integrated financial reporting strategy for US, APAC and Europe |
| Airlines | A++ Consortium | OBIEE reporting application layer for United, Continental and Lufthansa |
Became BackX connectors
Moving data between systems that were never meant to meet — the discipline that became the connector layer.
| Sector | Client | Engagement |
|---|---|---|
| Capital mkt | SEI Investments | Integration strategy for structured and unstructured data, plus tool advisory |
| Manufacturing | Ceridian, USA | Global warehouse integration across seven lines of business onto a prebuilt analytic platform — cutover architecture |
| Retail | American Greetings, USA | Clickstream analytics on card buying behaviour, underlying warehouse and Business Objects reporting |
| Healthcare | Independent Health | Data integration strategy for CMS data |
| Pharma | ResMed | Supply chain warehouse, inventory analytics, self-serve reporting and MDM — cutover architecture through modernisation; awarded at the Informatica annual meet |
| Hospitality | Caesars Palace | Complete data engineering ecosystem for the casinos; Teradata to Azure ADF migration architecture |
| Airlines | Sabre | Data engineering integration migration from Teradata onto Google |
Became the operating model
How a client team is stood up around the platform.
| Sector | Client | Engagement |
|---|---|---|
| Banking | Citibank, Tampa, USA | Established the visualisation and reporting centre of excellence |
| Retail | Boston Pizza, Canada | Target-state BIDW architecture advisory, execution and BICC formation |
| Banking | Santander, Spain | Built the largest centre of excellence covering big data, warehouse and wider architecture needs |
Became the slot model
How the make-or-buy call is made — the discipline behind every capability being a slot rather than a fixture.
| Sector | Client | Engagement |
|---|---|---|
| Banking | Freddie Mac, USA | Build-and-buy decision strategy for lifting HR analytic models from PeopleSoft EPM to Oracle OBIEE; financial performance reporting for Mega Holding, Taiwan |
| Capital mkt | Russell Investments | Build versus buy assessment for process modelling and process mining tools |
Each discipline was learned in the field, then productised. This is the mapping.
| Discipline learned | Became | What it does in the platform |
|---|---|---|
| Data and domain strategy | discoveryX | The discovery loop — frame, output, discuss, refine, freeze |
| Data science and AI | BottomX | Algorithms, feature vectors, data-product design |
| Generative AI | FrontX | Personas, use-based interfaces, alerts, summaries |
| Data architecture | TopX | Outcomes, flows, agents, A2A interaction, ROI |
| Governance and quality | GovernX | Data quality, lineage, security, observability |
| Visualisation | FrontX | Dashboards and the last-mile presentation surface |
| Integration architecture | BackX connectors | Sources, connectors, cutover and migration patterns |
| Centre of excellence | Operating model | How a client team is stood up around the platform |
| Build versus buy | The slot model | Every capability is ours or a connector — the client's call |
It is sixty-one engagements, productised. Every category in the architecture exists because something broke in production and had to be fixed.
A ninety-minute session produces the agent map, the data products behind it, the persona mockups and the ROI framework — for your process, in your language.
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