SaaS
Customer-Facing Analytics & API Integration Platform
Intelligent analytics layer with live data API feeds powering client-facing dashboards
View case study →Service
AI-powered Analytics
Natural language access and intelligent summaries built on trusted data foundations.
Typical timeline · 6–12 weeks
AI analytics layer
Live previewExecutive summaries
↑ 12%
Forecasting
94.2%
Natural language querying
8.4×
Performance trend
Challenges
- AI initiatives launched on ungoverned data
- Executives want answers, not another tool to learn
- Forecasting models disconnected from operational reality
Outcomes
- AI layer grounded in certified metrics and lineage
- Automated narrative summaries for leadership reviews
- Forecasting workflows integrated into planning cycles
Engagement process
01
Discover
Map the decision, the systems that hold the data, and where the numbers disagree.
02
Design
Specify the model, the workflow, and the controls: approved data, permissions, and what a person must review.
03
Build
Implement pipelines, semantic models, dashboards, or analytical workflows in releases the team can review.
04
Validate
Check calculations, access, and outputs against agreed tests before a figure is treated as decision-ready.
05
Operate
Hand over documentation and, where you want it, stay on for monitoring and agreed improvements.
Related case studies
Oil & Gas · E&P operator
VOC Monitoring & Automated Alerts
Automated anomaly detection for emissions data across Guyana deepwater operations
View case study →For Claude, ChatGPT, conversational analytics, and production integration, see AI Analytics & Agents.
Deliverables
Executive summariesForecastingNatural language queryingIntelligent reporting assistants
Led by Ayush Dubey, Principal Data Platform & AI Consultant.