Faster trusted decisions
Connect operational questions to governed metrics, timely refresh, and clear drill-down paths.
Seriba service practice
Data, AI & Operational Analytics
Build trusted data pipelines, operational dashboards, forecasting, and AI-assisted decision support around governed organisational information.
Lower reporting pressure
Serve dashboards and exports without repeatedly scanning live transaction tables.
Responsible AI assistance
Apply models only where access, evidence, evaluation, cost, and human review can be controlled.
Begin with trusted definitions and decision needs
Dashboards fail when indicators, denominators, targets, time windows, geographic roll-ups, and source ownership are unclear. We define the decision, metric contract, lineage, quality checks, refresh expectation, and responsible owner before visualisation.
Protect transaction systems while serving analytics
Event streams, background jobs, read models, materialised views, warehouses, caches, and scheduled aggregates separate reporting pressure from live operational databases. Refresh and page-loading expectations are designed around data volume and programme urgency.
Apply AI with clear boundaries
AI-assisted analysis can summarise trends, retrieve governed knowledge, explain metrics, draft narratives, and surface exceptions. Sensitive data is minimised, access is enforced before retrieval, outputs retain source context, and high-impact decisions remain reviewable by responsible staff.
Capabilities
Data architecture, pipelines, and quality controls
KPI, dashboard, and semantic-layer design
Warehouse, read-model, and caching strategy
Forecasting and anomaly detection
Governed retrieval and AI-assisted insight
Data access, lineage, retention, and model monitoring
Delivery model
How this engagement moves from requirement to operating capability
Frame decisions
Define users, decisions, metrics, sources, targets, freshness, quality, privacy, and success criteria.
Engineer data
Build models, pipelines, aggregates, semantic definitions, tests, lineage, and access controls.
Deliver insight
Create dashboards, alerts, narratives, exports, forecasting, or AI-assisted workflows.
Evaluate and operate
Monitor quality, usage, performance, cost, drift, permissions, and decision value.
What you receive
Concrete outputs your team can use after the engagement.
Available for dashboard remediation, data-platform design, warehouse implementation, indicator governance, forecasting, or a controlled AI pilot connected to existing systems.
- 01Decision and indicator framework with metric definitions
- 02Source inventory, data model, and lineage map
- 03Production data pipelines and quality controls
- 04Operational dashboards, reports, exports, and refresh model
- 05AI use-case, privacy, evaluation, and guardrail design
- 06Monitoring, ownership, documentation, and analyst handover
Discuss a data and analytics engagement
Share the objectives, operating environment, timeline, and current systems. We will help define a practical scope and delivery path.