Samuel Simeon

• 93% — Reduction in Sales Quote Generation Time
• 12% — Revenue Recovery via Automated Discount Logic
• 100% — Elimination of Manual Discount Calculation Errors
• 800% — Increase in Monthly Quote Processing Capacity

Sales operations suffered from a 12% revenue loss due to “discount leakage” and human error. Fragmented spreadsheets caused a 45-minute delay per quote, stalling the sales cycle and decreasing lead velocity.

I architected a centralized ERP with a Node.js microservice for multi-tier discount logic. I also developed an Angular-based automated engine to unify product catalogs and ensure 100% pricing accuracy across all channels.

Reduced the quote-to-order cycle by 93% (from 45 minutes to 3 minutes) and achieved 99.9% accuracy in automated pricing calculations across 1,200+ monthly quotes.

• 204% — Increase in Lead-to-order Conversion
• 60% — Lower CAC

The platform faced high customer acquisition costs and inconsistent referral attribution. Manual lead tracking led to a low conversion rate and fragmented user data, hindering effective scaling of operations in a competitive hyperlocal market.

I engineered a robust lead-conversion engine and an automated referral tracking system integrated into the Flutter mobile checkout flow. I also utilized Firebase Cloud Functions to automate reward distribution and provide real-time data analytics for marketing spend optimization.

Drove a 204% increase in lead-to-order conversion and lowered CAC by 60%. Scaled the user base to over 8,500 monthly active users while maintaining 99.9% system uptime through a highly resilient and scalable back-end architecture.

• **99.9% ** — System Uptime During Peak High-traffic Events
• 45% — Increased Average Bidder Engagement

Existing auction systems suffered from high latency and poor mobile responsiveness, leading to significant user drop-offs during critical final-minute bidding windows. This resulted in lower final sale prices and reduced trust in the platform’s real-time capabilities.

I managed the end-to-end product lifecycle for this real-time auction SaaS, implementing a low-latency notification system using web sockets. I also optimized the UI for high-velocity bidding to ensure a seamless experience across both web and mobile platforms for all participants.

Achieved 99.9% system uptime during peak high-traffic events and increased average bidder engagement by 45%. The platform successfully aligned with GTM strategies, driving significant business growth.

• 35% — Increase in Delivery Fulfillment Rates
• 50% — Improvement in Customer Satisfaction Scores
• 20% — Reduction in Average Last-mile Delivery Time
• **99.9% ** — System Uptime For Real-time Tracking Services

Regional delivery operations faced significant challenges with “last-mile” security and real-time tracking accuracy. Inefficient manual routing led to high operational costs and inconsistent delivery windows, which directly impacted customer trust and overall service reliability in the hyperlocal sector.

I architected a secure, end-to-end delivery ecosystem utilizing Flutter for the mobile experience and Firebase for real-time data synchronization. I also implemented automated route optimization and a secure digital handshake protocol to ensure package integrity and provide customers with live, transparent tracking updates.

Increased delivery fulfillment rates by 35% and improved customer satisfaction scores by 50% through enhanced security features. The platform maintained 99.9% system uptime during peak demand periods.

• **100% ** — Elimination of Manual Milestone Tracking Errors
• 25% — Improvement In Cross-functional Team Velocity
• 15% — Reduction in Project Operational Bottlenecks

Civil engineering projects were hindered by fragmented communication and manual tracking, leading to significant delays and cost overruns. Stakeholders lacked a unified digital dashboard to monitor real-time progress, resulting in a 15% increase in operational bottlenecks during critical construction phases and directly affecting project timelines and overall budget adherence.

I directed the full-stack development of a niche SaaS platform specifically tailored for civil engineering project tracking and lifecycle management. By implementing automated milestone reporting and a centralized document repository, I enabled real-time collaboration between site engineers and off-site stakeholders. I utilized Agile methodologies to manage the product design and engineering requirements, ensuring the final tool addressed the specific pain points of on-site resource allocation and high-fidelity data integrity.

Digitized complex engineering workflows, achieving 99.9% system uptime for mission-critical data. Leadership drove a 25% reduction in manual reporting time and a 15% drop in bottlenecks, significantly boosting stakeholder transparency.

• 85% — Increase In Operational Throughput for Pre-surgical Workflows
• 90% — Reduction In Expert Resource Costs Per Model Generated

D patient models offer immense clinical value, but adoption is stalled by poor unit economics. Converting 2D scans into 3D assets required hours of manual segmentation using costly proprietary tools. This bottleneck inflated COGS and restricted scale. The business needed an automated software pipeline to eliminate manual engineering overhead and lower production costs per model.

I built a scalable Flask and Angular prototype that replaces manual workflows with an automated digital utility, eliminating human friction and high licensing fees:
• Automation: Ingested raw clinical data using NiBabel and SciPy filtering to eliminate billable data-cleaning hours.
• Mesh Generation: Programmatically converted voxel grids to watertight STL meshes using Marching Cubes and Open3D.
• Validation: Built a Three.js and WebGL client preview layer to accelerate stakeholder approval loops.

The pipeline cut processing from hours to under 5 seconds, reducing operational resource costs by 90%. This unlocked scalable unit economics, allowing the business to pivot from a low-volume offering into an accessible, mass-market SaaS model.

• 100% — Data Privacy Compliance With Zero External API Leaks
• 75% — Reduction In Security Auditing Cycle Times

Enterprise clients need Generative AI but face strict compliance barriers preventing public LLM use (e.g., OpenAI). Sharing IP or financial data with cloud models risks massive data leaks. The business required a private, air-gapped multi-agent platform to perform secure, local data analysis and auditing without compromising enterprise data privacy.

I built a secure, on-premise multi-agent AI platform using local LLMs:

1. Private Inference: Deployed local LLMs via Ollama, isolated from external networks for absolute data privacy.
2. Agentic Workflows: Engineered automated document inspection and sequential reasoning using n8n.
3. Architecture: Leveraged Model Context Protocol (MCP) and vector stores to maintain secure project context.
4. UI: Developed an ingestion dashboard for streamlined reporting.

The private system eliminated data-leak risks, achieving 100% compliance. It accelerated security auditing cycles by 75%, allowing enterprise clients to securely extract deep insights from restricted datasets with zero third-party data reliance.