Alex

For a mobile application provider (LetsVibe), I led an R&D initiative to explore AI-driven enhancements aimed at improving user interaction quality and engagement within their platform.

• The Business Imperative: The client sought to innovate by integrating intelligent features to support users in crafting more effective communications, thereby aiming to increase user satisfaction, positive interactions, and overall app stickiness.

• My Strategic Solution & Implementation: As an AI consultant, I spearheaded the initial research, feasibility assessment, and strategic planning for a novel in-app user support tool. My contributions included:

  • Leading applied R&D into Natural Language Processing (NLP) and advanced sentiment analysis techniques, evaluating models to assess qualitative aspects of user-generated text.
  • Conducting thorough analysis of existing (anonymized) user interaction data to identify patterns and inform AI model development strategies.
  • Developing foundational data processing approaches and establishing initial machine learning baselines to benchmark the potential of more complex AI solutions.
  • Delivering a comprehensive analysis of research findings, including the inherent challenges of evaluating model performance with organic interaction data, and authoring a strategic proposal for future AI feature development. This roadmap emphasized the critical need for high-quality, targeted evaluation data to ensure robust model training and validation, and also included considerations for potential GenAI applications.

• Value Delivered & Capabilities Showcased:

  • Provided the client with crucial insights and a strategic framework for their AI product development lifecycle, enabling informed decisions about future AI/NLP feature integration.
  • Demonstrated expertise in rapid AI concept validation, NLP/sentiment analysis R&D, and data-informed strategic planning for technology providers looking to enhance software with intelligent capabilities.
  • My work helped clarify the technical path and data requirements for developing a potentially high-impact user engagement feature.

• Key Learnings for Technology Providers: This project underscored the importance of early-stage R&D and strategic data planning when exploring novel AI features. Addressing data quality and evaluation challenges upfront is critical for de-risking development and ensuring AI initiatives can deliver tangible product value.

• Key Technologies & Methodologies: Python, Natural Language Processing (NLP), Sentiment Analysis, Machine Learning (Scikit-learn, Pandas), AI Product Strategy, Feasibility Studies, R&D, Data Analysis, Strategic AI Roadmapping.

This personal project, “Nutrify Your Life,” involved developing an end-to-end Retrieval-Augmented Generation (RAG) system to transform an extensive, science-based blog library (1200+ posts) into an interactive, conversational knowledge source.

• The Challenge & Learning Objective: To master the practicalities of building a sophisticated RAG system capable of making a large, unstructured, domain-specific knowledge base accessible via natural language, and to explore architectures directly applicable to industrial use cases.

• Solution & Key Methodologies: I independently designed, developed, and deployed the “Nutrify Your Life” Q&A chatbot. This involved:

  • Systematic web scraping, parsing, and chunking of blog content.
  • Constructing a knowledge base using sentence embeddings and a LanceDB vector database.
  • Implementing an advanced retrieval pipeline with hybrid search (vector + keyword FTS) and Cross-Encoder reranking, including sentence-window retrieval for richer context.
  • Integrating with fast LLMs (via Groq API, e.g., Llama3) for accurate, context-aware answer synthesis with source citation, guided by careful prompt engineering.
  • Developing a user-friendly Streamlit interface, MongoDB-backed monitoring dashboard, and containerizing the application with Docker for deployment on Streamlit Cloud.
  • Rigorous offline evaluation of retrieval (Hit Rate, MRR) and RAG quality (cosine similarity).

• Demonstrated Capabilities:

  • Successfully built and deployed a fully functional AI application, demonstrating end-to-end expertise in GenAI application development, from data ingestion to LLM integration and UI creation.
  • This project solidified my practical skills in the complete GenAI development lifecycle.

• Relevance for Asset Operators & Technology Providers: The RAG methodology & architecture demonstrated here offers transformative potential for industrial applications, delivering significant efficiency gains and democratizing knowledge access. It is directly applicable for:
  • Technology Providers: Integrating intelligent AI assistants into software to provide instant, context-aware support, answer complex user queries based on product documentation, and reduce support overhead.
  • Asset Operators: Creating powerful internal knowledge bases from technical manuals, maintenance logs, safety procedures, and compliance standards, enabling rapid troubleshooting, improved decision-making for field engineers, and streamlined onboarding for new personnel.

• Key Technologies & Methodologies: Generative AI (RAG), Python, Streamlit, LLM APIs (Groq), Sentence Transformers, Vector Databases (LanceDB), Hybrid Search, Cross-Encoder Reranking, Prompt Engineering, Data Scraping/Processing, MongoDB, Docker, Evaluation Metrics (Hit Rate, MRR).

For a leading SaaS provider of industrial diagnostics, I helped enhance their core AI product by designing and deploying a next-generation anomaly detection and MLOps framework.

• The Strategic Challenge: The company’s mission is to provide real-time, reliable diagnostics for complex industrial assets. To maintain market leadership and scale its impact, there was a critical need to enhance the core diagnostic engine with more advanced, scalable, and interpretable machine learning algorithms.

• My Strategic Solution & Implementation: I spearheaded the algorithmic strategy and was a key driver in establishing a robust MLOps framework for the core product. My contributions included:

  • Designing, benchmarking, and deploying a suite of advanced anomaly detection algorithms (causal and non-causal, e.g., Isolation Forest, PCA-clustering) tailored for complex industrial data.
  • Developing a novel mock-fault injection framework for rigorous pre-deployment validation, ensuring the high reliability required for mission-critical systems.
  • Establishing and benchmarking robust MLOps pipelines for efficient model training, scalable deployment, and continuous performance monitoring.
  • Experimenting with different explainability frameworks to pinpoint anomaly drivers, a key feature for end-user adoption and trust.
  • Improving CI/CD practices and transitioning the algorithmic codebase to a newer Python version.

• Measurable Business Value Delivered:

  • Contributed directly to the core technology that enables the company to identify 2 TWh of energy losses annually across its global fleet of 75+ industrial assets.
  • Significantly improved anomaly detection accuracy and model interpretability, reinforcing the platform’s publicly stated 90% diagnostic accuracy and leading to more reliable, actionable fault warnings for end-users.
  • The robust MLOps processes reduced model deployment times and enhanced platform reliability, enabling faster delivery of value to clients.
  • Strengthened the company’s position as an innovator in the industrial AI space by integrating cutting-edge ML techniques into its core product.

• Key Learnings for Asset Operators & Technology Providers: This project underscored the critical importance of robust MLOps, mock-fault validation, and model explainability for building trustworthy and scalable AI in mission-critical industrial systems.

• Key Technologies & Methodologies: Python, Advanced Anomaly Detection Algorithms (AAKR, Isolation Forest, Clustering, Causal Methods), Strategic MLOps Design (Kubernetes, NATS, GitHub Actions, Seldon), Custom Mock-Fault Generation & Validation, Model Explainability (SHAP), Performance Benchmarking, CI/CD (GitHub Actions).