SaaS
Customer-Facing Analytics & API Integration Platform
Automated data pipeline with live API feeds powering client-facing intelligence layers
View case study →Service
Agentic AI & Autonomous Analytics
AI agents that query your data, surface anomalies, and deliver narrative summaries - without manual reporting workflows.
Typical timeline · 8–16 weeks
Delivery architecture
Sources
Agents
Monitoring
LLM layer
Automation
01Sources
02Agents
03Monitoring
04LLM layer
05Automation
Delivery progress
Challenges
- Reporting workflows still depend on manual analyst intervention
- AI pilots lack production monitoring and guardrails
- Anomaly detection disconnected from business context
Outcomes
- Autonomous agents monitoring critical metrics around the clock
- Narrative summaries delivered without manual report assembly
- Production-grade LLM integration with audit trails
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.
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Deliverables
Agentic monitoring pipelinesAutonomous anomaly agentsLLM guardrails & evaluationExecutive narrative automation
Led by Ayush Dubey, Principal Data Platform & AI Consultant.