VerticalX – HorizontalX-TechnologyX
Why anything is pre-built at all?
The economic argument for VerticalX:
A fraud detection agent for a bank is not conceptually different from the next bank’s fraud detection agent — the data shapes differ, the thresholds differ, the integrations differ, but the algorithm, the agent design and the governance model are the same object.
So we build it once, properly, and configure it thereafter. The second deployment in a vertical costs a fraction of the first.
VerticalXV
Industry solutions
Nine live. Twenty in the roadmap. Twenty to thirty use cases in each.
Each vertical ships with a primary use case, the algorithms behind it, the data products it consumes, the personas it serves and the CDAO outcome it produces.
Banking and financial services
Real-time fraud detection. Sub-50ms transaction scoring using graph neural networks and gradient boosting, with explainable alerts. Delivers a fraud data product owned by the CoE, three to five million dollars in annual loss prevention, and a sharp fall in false positives.
Insurance
Automated underwriting. Risk scoring, straight-through processing and third-party enrichment. Cuts underwriting time substantially and gives the data office a natural expansion path into pricing AI.
Travel
Revenue management AI. Dynamic pricing driven by demand signals, an events calendar and competitor parity. Delivers revenue intelligence to the commercial team and measurable RevPAR improvement.
Energy
Asset failure prediction. IoT sensor streams converted into failure probability for turbines and transformers. A predictive maintenance product with a material reduction in unplanned downtime.
Shipping and logistics
ETA and route optimisation. Vessel arrival prediction and dynamic routing accounting for weather, congestion and fuel cost. Establishes operations AI inside the CoE and takes cost out of every voyage.
Manufacturing
Yield and defect classification. Computer-vision defect detection combined with process-parameter root-cause attribution. Reduces scrap and opens the door to supply chain AI.
Retail
SKU demand forecasting. Store-SKU level forecasting adjusted for promotion, markdown and seasonality. The CDAO ends up owning the demand intelligence platform, with overstock down and COGS improved.
Health
Wellbeing and attrition AI. Wearable biometrics and engagement NLP converted into a wellbeing index per person. A people analytics product for the CHRO — and the alignment play between the data office and HR.
Hospitality
Personalised upsell engine. Ancillary offer personalisation by guest profile and stay context. Revenue per guest up, and the CoE’s first customer-experience intelligence product.
Additional verticals in build: utilities, telecom, public sector, media, pharmaceuticals, automotive, agriculture, education, real estate, professional services and construction.
VerticalXA
Horizontal solutions
Five functions that exist in every company, regardless of industry.
These are the classic BPO functions — the ones with the highest manual load, the clearest process boundaries and the fastest route to a measurable number.
HR and people
- Attrition risk scoring — a flight-risk product for the CHRO, replacing the spreadsheet process
- Skills gap mapping — the learning and development priority engine, which also accelerates the CoE’s own capability build
- Workforce demand planning — a headcount model owned by the data office and trusted by HR
Finance
- Cash flow forecasting — thirteen-week accuracy above ninety percent, with CFO sign-off
- Payables and receivables anomaly detection — fraud and duplicate detection with real-time alerts to finance operations
- Budget variance AI — root-cause attribution to cost centre, with CFO reporting automated
Marketing and sales
- Multi-touch attribution — the CoE replaces the agency model and lifts media efficiency
- Propensity to purchase — real-time scoring served into the CRM
- Customer lifetime value prediction — the LTV product that drives acquisition investment
Contact centre
- Real-time agent assist — retrieval and generation cutting average handle time; usually the fastest ROI proof point in the portfolio
- Call volume forecasting — fifteen-minute interval modelling that takes cost out of staffing
- Escalation prediction — a mid-call risk score that pulls in a supervisor before the customer is lost
Procurement
- Supplier risk scoring — a supplier intelligence product for the CPO
- Spend classification AI — automatic taxonomy of tail spend, typically surfacing millions in addressable savings
- Contract anomaly detection — rogue spend detection with audit-ready output
TechnologyX
The agents that keep the platform trustworthy
Enterprises do not only buy vertical agents.
A fraud agent wins the deal. What keeps it alive in production is whether the data behind it stays clean and fresh, whether the prompts survive a model upgrade, whether anyone can trace what changed, and whether somebody is watching the agents themselves.
TechnologyX is thirty-five technical agents across eight families. Every one of them is a slot — build it with us, or connect the tool you already own.
Family 1 — Data Quality
Profiler · Rule Generator · Anomaly Sentinel · Reconciliation · Duplicate Resolver · Null-Cause Tracer
Trust in the data itself. The profiler scans a new source and infers types, ranges, nulls and cardinality; the rule generator then writes the quality rules from that profile rather than from a human guess. Every one of these runs on every load, not on a quarterly audit.
Family 2 — Observability
Pipeline Watcher · Freshness Monitor · Cost Sentinel · Alert Deduper · Incident Narrator
Trust in the pipeline. Catches the jobs that succeed but produce nothing, the datasets that go stale before a consumer notices, and the queries that quietly run away with the compute budget. The alert deduper collapses two hundred downstream alarms into the three root causes that produced them.
Family 3 — Testing
Test Generator · Data Contract Tester · Regression Runner · Synthetic Data · Coverage Auditor
Trust in the change. Where AI writes a large share of the code, tests have to be generated at the same rate. The coverage auditor ranks untested paths by blast radius rather than by line count.
Family 4 — Lineage and Impact
Lineage Mapper · Impact Analyser · Orphan Hunter · Schema Drift
Trust in the change you are about to make. Column-level lineage parsed from the code itself, because documentation is always out of date. The impact analyser answers the only question an engineer asks before a change: if I alter this column, what breaks.
Family 5 — Security and Governance
PII Classifier · Access Reviewer · Policy Enforcer · Audit Trail Assembler · Secret Scanner
Trust in who can see what — continuously, not as an annual attestation exercise. The audit trail assembler turns an audit from a project into a query.
Family 6 — Engineering Productivity
Migration Agent · Code Documenter · Refactor Agent · Connector Builder · Query Optimiser
Trust that the backlog moves. Legacy platform to cloud, legacy orchestration to modern, legacy SQL translated with equivalence proved rather than assumed. This family is twenty-five years of migration practice, productised.
Family 7 — Model Operations
Drift Detector · Eval Harness · Prompt Version Guard · Hallucination Auditor
Trust in the models. Drift detection with a retrain trigger attached rather than a dashboard. Prompt regression testing for the day the underlying model is upgraded beneath you.
Family 8 — Meta
Agent Supervisor
Trust in the agents themselves. It monitors every other agent in the estate: which fired, which was overridden by a human, which is drifting, which is costing more than it saves.
Thirty-four agents running unsupervised is not automation. It is an unmanaged fleet. This is the layer nobody else is selling.
Build or buy — every agent is a slot
No enterprise replaces everything at once, so we do not ask them to.
| Family | Ours | Or connect to |
|---|---|---|
| Data Quality | Agents 1–6 | Great Expectations, Soda, Informatica DQ |
| Observability | Agents 7–11 | Monte Carlo, Datadog, Bigeye |
| Testing | Agents 12–16 | dbt tests, Great Expectations, Soda |
| Lineage and Impact | Agents 17–20 | Collibra, Alation, OpenLineage |
| Security and Governance | Agents 21–25 | BigID, Immuta, Varonis |
| Engineering Productivity | Agents 26–30 | No credible incumbent |
| Model Operations | Agents 31–34 | Fiddler, Arize, WhyLabs |
| Meta | Agent 35 | Nothing on the market |
Fill a slot with our module, or connect the tool you already own and let CereBrumha orchestrate it. The decision stays with you — and it can change later without rebuilding anything.
Greenfield or brownfield
Greenfield. No existing estate. DiscoveryX frames it, VerticalX supplies the engine, BackX builds it, TechnologyX keeps it honest.
Brownfield. An existing estate with platforms already licensed and agents already half-built. CereBrumha connects to what exists, becomes the navigation layer over it, and fills only the gaps that are actually gaps.
Most enterprises are the second case. The platform was designed for it.
Where to start
Pick one process. Give us ninety minutes.
You will leave with the agent map, the data products behind it, the persona mockups and the ROI framework — for that process, in your language, with your constraints already applied.
[Book a DiscoveryX session] · [Download the solution catalogue]