AI Development Experience
Production AI delivery across computer vision and generative assistants.
These engagements span applied computer vision, personalization workflows, and GPT-powered conversational systems. The focus is pragmatic: models that integrate into real products and deliver measurable user value.
Project Snapshot
Overview
Plumscope AI work includes domain-specific model training, multimodal experimentation, and deployment-ready APIs. Case studies below cover food recognition, hydration event detection, personalized fitness planning, devotional conversational AI, and practical WordPress platform engineering.
Delivery Numbers
At a Glance
Case Studies
Selected AI Engagements
Case Study 1: Arabic Food Recognition and Ingredient Detection
Client Location: Kuwait
Objective: Automate food tracking and nutritional logging for traditional Arabic meals using dish images.
Solution: Built a deep learning pipeline that classifies Arabic dishes and detects core ingredients to support meal logging and nutritional estimation, similar to image-based nutrition APIs.
Technologies: Python, TensorFlow/Keras, OpenCV, custom Arabic food image dataset, model training and evaluation workflows, FastAPI deployment layer, cloud storage.
Impact: Enabled near real-time meal identification, improving nutrition journaling consistency in wellness applications.
Case Study 2: AI-Based Gulp Detection from Video
Client Location: United States
Objective: Detect and count water gulps from camera video for hydration tracking in wellness and fitness products.
Solution: Designed a CV-first analysis pipeline for throat motion patterns and gulp event classification, with a planned multimodal expansion to include audio signals for higher detection confidence.
Technologies: Python, OpenCV, Mediapipe pose estimation, TensorFlow/PyTorch, frame-by-frame video analysis, Jupyter experimentation, audio signal processing (future phase).
Impact: In-progress system targeting real-time hydration monitoring without wearables, with strong relevance to sports tech and elder care use cases.
Case Study 3: FitFlex - AI-Powered Fitness and Diet Assistant
Client Location: United States
Objective: Provide personalized workout and diet plans based on goals, preferences, and body parameters.
Solution: Delivered an interactive web platform that collects user attributes and dynamically generates routines and diet plans through AI-assisted logic and responsive frontend workflows.
Technologies: React.js, Node.js/Express, MongoDB, OpenAI integrations, TailwindCSS.
Impact: Improved personalization over generic plans while keeping the product responsive and ready for chat-based coaching extensions.
Case Study 4: Krishn.ai - Generative AI Devotional Experience
Client Location: India
Objective: Create a personalized devotional assistant inspired by Lord Krishna for emotionally supportive spiritual conversations.
Solution: Built a GPT-based assistant with contextual memory and tone-controlled response styling to maintain devotional consistency across ongoing sessions.
Technologies: OpenAI GPT-4, LangChain, React.js, FastAPI, TailwindCSS.
Impact: Established a distinctive AI product that combines spirituality and conversational UX, with a path toward richer multimodal experiences.
Case Study 5: Raviraj Engineering - Custom WordPress Platform Engineering
Client Location: India
Objective: Build a maintainable industrial web platform with stronger product discoverability, structured admin workflows, and reliable inquiry capture.
Solution: Delivered a custom WordPress theme, CPT-based admin model (Products, Certificates, Recognitions, Sliders, Contact), and a custom media utility plugin to improve publishing operations.
Technologies: WordPress, custom PHP theme development, custom plugin development (Media AI Meta Cleaner), Bootstrap, JavaScript, Site Kit, WP Mail SMTP, Sucuri, caching/performance stack.
Impact: Created an owned content system for ongoing industrial catalog growth with better operational reliability and clearer lead-handling pathways.
Execution Path
AI Delivery Approach
Domain Framing
Define target behavior, annotation boundaries, and measurable success criteria before model experiments begin.
Model Experimentation
Iterate across architectures and data strategies with tight evaluation loops using practical product metrics.
API Productization
Package model inference through FastAPI or Node service layers with predictable contracts for app integration.
Continuous Improvement
Monitor behavior in production, capture edge cases, and feed model retraining and prompt tuning cycles.
Technical Scope
Stack and Capability Areas
Computer Vision
OpenCV and pose/keypoint pipelines for image and video-based recognition workflows.
ML Frameworks
TensorFlow, Keras, and PyTorch for experimentation, training, and inference optimization.
Generative AI
OpenAI GPT integrations and LangChain orchestration for contextual assistant behavior.
Product Integration
FastAPI and JavaScript service layers connected to React frontends and cloud-backed storage.
Outcome
Key Outcomes
- Delivered AI systems that map directly to daily user workflows in wellness, fitness, and devotional products.
- Combined research-oriented experimentation with deployment-focused architecture for practical release readiness.
- Established reusable AI delivery patterns across vision, multimodal, and conversational domains.