Amol Bhosale

Agent 1: Media & Data Monitoring Agent

- LangGraph, RSS feed, Economic Sources, Registry Ranking, OpenAI docx writer

collect -> rank -> approve -> summarize-> output

Agent 2: Political & Policy Stakeholder Mapping Agent

- LangGraph, Search MCP for data extract from parliament, OpenAI

profile -> policy scope -> cllect -> score -> output

Agent 3: Competitor Alternatives Agent

load_client -> discover -> rank -> gap aalysis -> output

Multi-agent AI system for energy data pipeline:

• · Data Retrieval Agent - Extracts user energy consumption data from the DB in real . Anomaly Detection Agent - Analyses patterns to detect spikes, irregularities, and v

• · Energy-Saving Advisor Agent - Generates personalised saving tips via RAG- powered knowledge. Results: # Automated detection of unusual consumption patterns Personalised, real-world saving recommendations # Real-Time Analysis - Eliminated manual monitoring Helped reduce unnecessary energy usage

Built a custom internal operations platform for a multi-location gym/fitness business to replace scattered spreadsheets and manual coordination.

• Centralised member management, class/trainer scheduling, and team task tracking into one dashboard. • Automated repetitive follow-ups (renewals, attendance gaps, onboarding) so staff stop chasing them manually. • Gave leadership real-time visibility into utilisation, retention risk, and team workload.

Outcome: less front-desk admin time, fewer dropped member follow-ups, and one source of truth instead of disconnected sheets. Anchors a fitness-chain digital transformation case study referenced by the MD.