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FinOps automation across seven environments
Automated scheduling and rightsizing that took roughly $280k a year out of the cloud bill.
Who it was for: A large regional bank running seven cloud environments.
The problem. Seven environments, most of them idle outside working hours, and no visibility into which team was spending what. Cost conversations were happening after the invoice rather than before.
What I did. Established a cost baseline per environment, then automated resource scheduling so non-production stopped paying for nights and weekends. Rightsized the workloads that were provisioned for peaks that never came. Built daily and weekly cost reporting so stakeholders could see the trend without asking for it.
The result. Around $280k of annual run-rate removed, with ongoing reporting that keeps the saving from creeping back.
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🚀 Case Study: Production-Grade Autonomous Lead Gen
Basic Zapier flows and fragile prompt chains break as soon as you scale. I recently replaced a client’s brittle setup with a stateful, type-safe Multi-Agent system that autonomously prospects, scores, and drafts outreach for B2B leads.
⚙️ The Stack:
LangGraph + PydanticAI + MCP (Model Context Protocol) + Claude 3.5 Sonnet
⚡ How It Works:
Stateful Orchestration (LangGraph): Manages execution with automatic checkpoints—if an external API fails, the workflow resumes without losing context.
Type-Safe Agents (PydanticAI): Enforces strict data schemas so outputs never hallucinate or break downstream code.
Native Integrations (MCP): Direct, secure read/write access to internal CRMs without messy webhooks.
📈 The ROI:
40+ hours/week saved on manual research and lead scoring.
100% data accuracy (zero JSON parsing errors).
Human-in-the-loop gating for final outreach approval.
💡 Need resilient, enterprise-ready AI infrastructure for your business? Check out my profile to book a Technical Audit & AI Automation Blueprint.
My tech stack looks completely different today than when I first started, and rightly so.
This is largely influenced by industry demands and better tools.
Two years ago, Claude wasn't doing half of what it does now. Today, it’s a core part of how so many of us work.
Even as tools change, the core principles stay the same:
⭐ Personalize everything to the team's real workflow.
⭐ Keep your data clean and structured early.
⭐ Design for edge cases before you launch.
⭐ Choose simple, efficient setups over complex ones.
⭐ Always focus on delivering the actual result.
Businesses will always adopt new tools as they grow, but real value will always come down to efficiency.
#AI #Automation #CRM
Data Analyst | Excel | SQL | Power BI
I help businesses clean, organize, and analyze data using Excel, SQL, and Power BI. I can create dashboards, reports, charts, and business insights from sales and operational data. I am a dedicated beginner focused on delivering accurate and professional work on time.
Yes. The data analysis helps to analysis real issues and give very good visual feedbacks. #powerbi dashboads