Success Stories.

Antimicrobial resistance is a critical global threat. Traditional detection methods (antibiograms) take 24+ hours—a dangerous delay for sepsis patients. Faster molecular tests are often cost-prohibitive.

Developed a machine learning framework using the MIMIC-III database to predict antibiotic susceptibility.

• Methodology: Applied six ML algorithms to a minimal set of demographic and lab data (age, sample type, Gram staining).
• Technical Handling: Addressed data imbalance (resistant vs. non-resistant) using SMOTE (Synthetic Minority Oversampling Technique).
• Outcome: The Extreme Gradient Boosting model achieved 84.53% accuracy, providing a rapid, cost-effective tool for clinical decision support.

Hospital infection data was siloed in complex databases. Clinicians could not access real-time trends (e.g., "pneumonia cases in Wing A") without submitting manual requests to data analysts, delaying intervention.

Built a secure Conversational AI interface that translates natural language questions into SQL queries.

• Architecture: Integrated an LLM agent with a secure SQL database containing de-identified patient data.
• Function: Doctors can ask questions via text and receive instant, data-backed answers regarding infection trends and antibiotic resistance profiles.

A plastic surgery clinic relied on paper forms for infection tracking, leading to transcription errors and a lag in identifying outbreaks.

Digitized the entire data collection pipeline using low-code tools for immediate impact.

• Implementation: Deployed smart Google Forms with conditional logic for data entry.
• Visualization: Connected data to Power BI for real-time dashboards that visualize patient journeys and pinpoint procedural failures (Root Cause Analysis).

Managing task sequencing, prioritization, and working memory can be a significant barrier for individuals with ADHD. Standard productivity apps often create more friction than they solve.

Engineered a self-hosted, conversational "digital co-pilot" to externalize executive function and automate personal knowledge management.

• Architecture: Built a private automation server using n8n and Docker that interfaces with a Telegram bot.
• Integration: The system uses SSH tunnels to securely read and write directly to a local Obsidian vault (Markdown filesystem).
• Function: The bot acts as a proactive guide, analyzing the day's plan to prompt the "next logical step" and automatically logging completed activities into the daily journal. This reduces cognitive load and context switching.

A learning platform faced bottlenecks in course creation. Producing tests, scripts, and flashcards was a manual, weeks-long process for Subject Matter Experts (SMEs).

Engineered an end-to-end GenAI pipeline to automate content production.

• Ingestion: System ingests unstructured PDFs and textbooks.
• Generation: An LLM-based engine extracts key concepts and programmatically generates lesson scripts, multiple-choice quizzes, and flashcards.

An online education provider treated all students the same, lacking insight into why some passed and others failed.

Created a "360-degree" student analytics engine to drive accountability.

• Analysis: Used Principal Component Analysis (PCA) to segment students into behavioral archetypes (e.g., "Mockup Masters" vs. "Flashcard Fanatics").
• Operational Strategy: Transformed raw behavioral data into clear KPIs (Key Performance Indicators).
• Intervention: Used these KPIs to set activity targets, specifically directing underperforming students to increase volume on high-yield tasks (e.g., "Complete 50 more practice questions this week").

A career coach's business model was unscalable due to the time required for manual 1-on-1 CV reviews.

Built a custom AI bot trained on the coach's specific methodology and brand voice.

• Process: The bot analyzes resumes against a rules engine (formatting, action verbs, metrics) and inserts specific comments directly into the document.

Admissions teams were overwhelmed by unqualified leads asking basic questions, slowing down response times for high-potential students.

Deployed an intelligent WhatsApp chatbot for 24/7 lead qualification.

• Logic: The bot answers FAQs and qualifies leads based on intent/timeline before handing off to human advisors.

A client needed to update an existing course to align with a new bibliography spanning 12,000 pages. The estimated cost and time to have subject matter experts manually read and cross-reference this volume against current lessons were prohibitive.

Engineered a semantic comparison engine to automate the audit.

• Ingestion Strategy: Split the massive bibliography into granular text nodes.
• Automated Cross-Referencing: Fed these nodes into an LLM alongside the transcripts of existing video lessons.
• Gap Detection: The system programmatically compared the two sources and generated a report, identifying exactly which concepts were new, which existing lessons were now obsolete, and where the gaps lay.

The firm managed numerous retainer contracts but lacked visibility into the actual cost of delivery. Without tracking lawyer hours against specific contract values, many clients were silently operating at a negative margin, draining firm resources without detection.

Engineered a true profitability dashboard using Power BI.

• Data Aggregation: Extracted and unified data from timesheets and contract management systems to calculate the effective hourly rate realized for each client.
• Visualization: Created clear visual indicators identifying accounts where the cost of labor exceeded the retainer fee.

The organization suffered from extreme data fragmentation. Critical business data was scattered across seven isolated silos: CRM (ActiveCampaign, Kommo), marketing (Meta Pixel, Elementor), education (three separate LMS platforms), and finance. There was no single view of the business, making cross-functional analysis (e.g., "How does student performance in LMS A affect refund rates in Finance?") impossible.

Architected and deployed a massive, centralized Data Warehouse acting as the company's Single Source of Truth.

• Data Engineering: Built complex ETL (Extract, Transform, Load) pipelines to unify data streams from ActiveCampaign, Kommo CRM, Elementor, Meta Pixel, 3 distinct LMS providers, and two financial systems into a single PostgreSQL database.
• Visualization Engine: Deployed and configured a self-hosted Metabase instance to democratize data access.
• Holistic Intelligence: Developed a suite of interconnected dashboards covering the entire value chain: Corporate OKRs, Financial Health, Marketing Attribution, Student Performance, and Sales Team Productivity.

A D2C brand wasted budget on generic ads because they relied on basic "Page View" pixel tracking.

Implemented a granular Google Tag Manager (GTM) strategy.

• Strategy: Defined and tracked custom events: ViewCategory, AddToCart, InitiateCheckout.
• Execution: Enabled dynamic product retargeting (showing the exact item left in cart) and exclusion audiences (stopping ads for purchased items).

Senior partners needed instant answers about lead volume, case status, and client acquisition costs. However, this data was locked in a Google BigQuery data warehouse, accessible only via SQL. Every question became a ticket for the data team, creating a bottleneck that delayed decision-making.

Built a secure Natural Language Query (NLQ) interface allowing lawyers to "chat" directly with the firm's data warehouse.

• Architecture: Deployed an LLM-driven agent with read-only access to the BigQuery schema.
• Translation Layer: The system translates non-technical questions (e.g., "Which marketing channel brought the most high-value leads last month?") into complex, syntactically correct SQL queries.
• Safety & Accuracy: Implemented a self-correction loop where the AI validates the SQL syntax before execution to prevent errors.

Marketing teams struggled to repurpose high-value intellectual property (books, manuscripts, white papers) into daily social content. The nuance of the original text was often lost, and manual repurposing was too slow.

Architected a Retrieval-Augmented Generation (RAG) pipeline to use the company's static documents as a grounding source.

• Ingestion Architecture: Built an automated flow (n8n) to ingest PDFs and books, chunking the text and storing it in a vector store for semantic retrieval.
• Ideation & Generation: The system retrieves relevant context from the book to generate high-fidelity topic angles.
• Human-in-the-Loop Governance: Designed a strict Check-Point system in Airtable. The AI generates ideas, but the workflow pauses for a human manager to approve the specific topic before the expensive content generation phase begins.

Publishing content across five platforms (WordPress, Medium, X, LinkedIn, Facebook) required five separate login sessions and manual formatting, leading to version control errors and copy-paste fatigue.

Transformed Airtable into a Headless CMS that orchestrates the entire publishing lifecycle.

• Unified Dashboard: Centralized the drafting, editing, and approval process within Airtable.
• Automated HTML Injection: Engineered the workflow to generate raw HTML for newsletters and Medium articles, bypassing the limitations of their native editors.
• Orchestration: Built a State Machine logic where changing a record's status to Ready for Publishing triggers API calls to post content simultaneously across all platforms.

Automation is useless if the team doesn't trust it. The marketing team was hesitant to adopt AI tools due to fear of complexity and loss of control.

Developed and documented the Standard Operating Procedures (SOPs) for the entire automation suite.

• Documentation: Authored comprehensive technical documentation detailing roles, prerequisites, and troubleshooting steps, including videos on Guidde.
• Adoption Strategy: Designed the workflows to mimic existing behaviors (e.g., "Change Status to Approve"), ensuring a quick learning curve for non-technical staff.