Founded by Rimjhim (Rémy) Singh - The Queen of RIMJHIM AI KINGDOM
Founded by Rimjhim (Rémy) Singh - The Queen of RIMJHIM AI KINGDOM
RIMJHIM AI KINGDOM is an AI innovation studio founded by Rimjhim (Rémy) Singh where production grade intelligent agents are designed, built, and deployed from scratch. Every system starts with an architecture; reasoning engines, safety layers, multi-tool orchestration, not just a prompt and an API call.
This is where agentic AI meets real-world problems. From autonomous banking assistants to voice-first property agents, every build is production-grade, explainable, and engineered to act, not just respond.
Based in London | Open to collaboration & consultation
Real architectures. Production-grade AI agents.
An AI-native banking agent built on a ReAct Orchestration Engine with a Six-Gate Safety System, verifying every action against FCA/GDPR rules before execution. Replaces manually coded conversation flows with one intelligent reasoning system. Confidence-based routing escalates to human agents when certainty drops below threshold.
An Agentic AI conversational UK property agent that understands natural speech, "2-bed flat in North London under £400k near the Tube", reasoning across multiple constraints simultaneously using Intent-Gating logic, asking clarifying questions like a human estate agent. Separate voice and text reasoning pipelines via FastAPI backend.
Dual-engine system: basic RAG retrieval plus intelligent ReAct agent answering document questions with source attribution and zero hallucinations. Local embeddings reduce cost by 10× vs standard implementations. Automatic fallback from agent to RAG on failure.
Multi-stage pipeline for restaurant review management: sentiment analysis, topic detection, crisis flagging, and brand-voice response generation, all routed through a human-in-loop approval dashboard. 92% sentiment accuracy across 500+ reviews tested.
Agentic email system detecting, understanding context, and drafting replies with CRM integration, thread history extraction, sentiment matching, and human-in-loop approval for VIP contacts. Live personal-production deployment. 87% reduction in daily email handling time.
Autonomous competitive intelligence pipeline running every 4 hours, aggregating 20+ sources, compressing articles into structured summaries via GPT-4, syncing to a searchable Notion knowledge base. 4× article coverage with near-zero manual overhead.
AI Agent in Action - Designed & Built by Rimjhim (Rémy) Singh
Click to watch the full demo
AI/ML Engineer | Technical AI Product Architect
Rimjhim (Rémy) Singh is a London-based AI Engineer, Technical AI Product Architect, an Economist and founder of RIMJHIM AI Kingdom, an AI Innovation Studio focused on building next-generation Agentic AI systems, intelligent automation products, and enterprise-grade AI solutions. She is also the sole architect and developer of this website.
She specialises in Agentic AI, LLM automation, AI agents, NLP, RAG pipelines, and scalable machine learning systems, designing intelligent solutions across the UK, Australia, Asia, and North America. Her work focuses on building AI systems capable of reasoning, planning, workflow orchestration, and real-world decision making.
She holds an MSc in Data Science from the University of Warwick (Russell Group, UK) and a Master's degree in Quantitative Economics. She is a Google Certified AI Professional with additional certifications in Software Engineering, Generative AI, MLOps, Cloud Computing, and Machine Learning from institutions including Hong Kong University of Science & Technology, Duke University, IIT Guwahati, AWS, and the University of Minnesota.
Professionally, she has worked as a Senior AI Engineer for a London-based company where she architected and optimised enterprise GenAI workflows supporting startups across 60+ countries. She has also contributed to an Australian healthcare startup by designing AI-powered healthcare systems and conversational AI solutions. Her experience spans enterprise AI agents, RLHF workflows, predictive systems, automation platforms, and complete AI product lifecycles from concept to deployment.
Her work has delivered measurable impact including 95%+ workflow acceleration, 60%+ productivity improvements, and up to 98% faster document analysis for enterprise teams. Through RIMJHIM AI Kingdom, she has developed multiple full-stack AI systems including conversational banking assistants, voice-first PropTech applications, intelligent automation agents, and AI orchestration frameworks. She has independently designed 10+ AI Agents.
Before transitioning fully into AI Engineering, Rimjhim built a strong academic and research background in Economics, Data Analysis, and Quantitative Research. She previously worked as an Assistant Professor and has published 15+ research articles and academic works across labour economics, industrial organisation, environmental policy, tourism economics, and ML-driven analysis.
She presented research at the University of Oxford, where her paper was later published by Brill. She is also the author of Puzzles of Labour Economics (2022). Early in her career she worked as an Intern with the Ministry of Home Affairs, Government of India.
She is an alumna of Ramjas College, University of Delhi (Economics Honours), where she secured the Overall Rank 1 position. She has also ranked among the top Kaggle participants globally in machine learning competitions and actively contributes to open-source AI communities including Hugging Face.
"AI isn't just about algorithms. It's about impact. Every solution should solve a real problem, deliver measurable value, and unlock new possibilities."
Based in London and building RIMJHIM AI Kingdom as a space for innovation, experimentation, technical excellence, and ambitious AI product development.
Optimized LLM workflows, cutting transcript analysis from 2.5 hours to under 5 minutes
Delivered scalable AI solutions with measurable business impact
Accelerated document analytics through hybrid ML + LLM approaches
Trained cross-functional teams in AI workflows, prompt engineering, and GenAI adoption
End-to-end AI systems from concept to production deployment
Insights on AI engineering, system design, and best practices
TensorFlow, PyTorch, Scikit-learn, Deep Learning, Neural Networks, Computer Vision, NLP, Time Series Analysis
RAG Pipelines, Vector Databases, Prompt Engineering, Fine-tuning, AI Agents, OpenAI API, Anthropic Claude, LangChain
AWS (SageMaker, Lambda, S3), Docker, Kubernetes, CI/CD, Model Deployment, Monitoring, Scaling
SQL, PostgreSQL, MongoDB, Vector Databases (Pinecone, Weaviate), ETL Pipelines, Real-time Analytics
Python, R, JavaScript, SQL, Git, APIs, FastAPI, Flask, Streamlit
Pandas, NumPy, Matplotlib, Seaborn, Power BI, Tableau, Statistical Modeling
Explore Founder's GitHub repositories, contributions, and open-source work
Featured Work:
Interested in collaboration, consultation, or discussing AI innovation?
Location: London, United Kingdom
