AI Products & Intelligent Systems
I design and build AI-powered systems with the same discipline used in traditional backend engineering — focusing on reliability, observability, cost control, and long-term maintainability. My work goes beyond experimenting with models and instead delivers production-ready AI workflows that support real business operations.
These systems combine large language models, embeddings, vector databases, and structured backend logic to create intelligent automation that remains transparent, auditable, and safe to operate at scale.
RoboResponder — AI Customer Support SaaS
RoboResponder is a SaaS-based AI auto-response and customer support platform designed to handle real business conversations with accuracy and control. It is built for organizations that want AI assistance without losing reliability or human oversight.
- Document-grounded AI responses using Retrieval-Augmented Generation (RAG)
- Vector-based semantic search over company knowledge bases
- Human-in-the-loop escalation when AI confidence is low
- Multi-tenant SaaS architecture with API-first design
- Designed for chat widgets, support portals, and messaging platforms
Jewelry Box POS — AI-Enhanced Point of Sale & Inventory System
Jewelry Box POS is a production-grade point-of-sale and inventory management system built specifically for jewelry retailers. It combines traditional POS workflows with AI-powered assistants to accelerate inventory intake, reduce manual data entry, and improve accuracy across multi-location operations.
- AI Text-to-Product-Name conversion using LLM-based structured extraction (OpenAI)
- AI Jewelry Image Analysis using multimodal vision models (Google Gemini)
- Automated attribute mapping into a rich Product Name Builder (metal, karat, gemstones, weight, size)
- AI-assisted PDF invoice parsing with schema-driven extraction (Gemini)
- Human-controlled AI helpers with full manual fallback and validation
AI Chatbot Assistant for POS Operations
Jewelry Box POS includes an embedded AI chatbot that acts as an in-system assistant, helping staff understand and use POS features without external documentation or support intervention. The chatbot is context-aware and trained on the POS workflow itself.
- Users can ask natural language questions directly inside the POS
- Step-by-step guidance for inventory, customers, billing, and reports
- Reduces onboarding time for new staff and minimizes support requests
- Responses are grounded in actual POS features and permissions
- Designed with clear fallback paths and human control
Example questions supported by the chatbot include: "How do I add a new inventory item?", "How can I add a customer?", "How do I view today's sales report?", and "Where can I update gold rates?".
Data Chat — Conversational Data Intelligence
Data Chat is an AI-powered conversational interface for business data, enabling users to interact with databases, spreadsheets, ERPs, log files, and IoT system data using natural language — no query languages required.
- Natural language access to structured and unstructured data
- Conversational querying of analytics, trends, and patterns
- Works across multiple data sources (SQL, ERP, IoT, logs)
- Designed for CEOs, analysts, and non-technical users alike
- Simplifies data retrieval, insights generation, and reporting
Retrieval-Augmented Generation (RAG) Systems
I build RAG pipelines that allow large language models to answer questions based on private, domain-specific data instead of generic training knowledge. These systems are designed for accuracy, traceability, and predictable behavior.
- Chunking and preprocessing of unstructured documents (PDFs, policies, manuals)
- Embedding generation and vector indexing
- Semantic retrieval with relevance scoring
- Context window optimization and prompt control
- Source attribution and response validation
Embeddings & Vector-Based Search
I implement embedding-based systems that enable semantic understanding of text, allowing applications to search and reason over content based on meaning rather than keywords.
- Vector database design and indexing strategies
- Similarity search and semantic ranking
- Hybrid search (vector + structured filters)
- Performance tuning for large document sets
- Cost-aware embedding lifecycle management
AI Chatbots & Conversational Systems
I develop conversational AI systems that are tightly integrated with backend logic, business rules, and data sources — not standalone chat interfaces. These systems are designed to assist users while maintaining control and clarity.
- Context-aware chatbots backed by structured APIs
- Session memory and conversation state management
- Role-based access and data isolation
- Guardrails, fallback flows, and escalation paths
- Integration with existing web and mobile platforms
Engineering-First AI Philosophy
I treat AI systems as production software — not experiments. Every AI solution I build includes monitoring, error handling, security considerations, and clear failure modes. My goal is to help businesses adopt AI responsibly, where automation enhances human workflows instead of replacing them blindly.