Hi, I'm
Lead AI Developer & Software Architect at Hureka Technologies — 14+ years designing AI chatbots, AI assistants, ontology-driven RAG, multi-agent systems, and voice AI. 118+ projects · 750M+ records handled.
Expertise
Dilip Singh is a Lead AI Architect and senior AI developer with 14+ years building production systems for enterprises in India, USA, Canada, and Tanzania. As Lead Software Architect at Hureka Technologies, he designs and ships AI chatbot platforms, AI assistant systems, and multi-agent hubs used in real customer support, healthcare, and media monitoring workflows.
Dilip's AI chatbot and AI assistance work goes beyond simple Q&A — he builds RAG-grounded assistants with per-tenant knowledge bases, human-in-the-loop approval, semantic caching, and multi-channel deployment across web, Slack, Teams, and telephony. Flagship builds include Hureka AI (BYOK support SaaS) and AImind Agent Hub (email, voice, and chat agents on a shared Qdrant brain).
For structured AI retrieval, Dilip designs ontology and knowledge graph architectures — semantic taxonomies, entity relationships, and RAG grounding that make AI assistants and chatbots more accurate than flat document search. His blog at https://dilsyno.com/blog covers ontology-driven content architecture, multi-agent design, and production LLMOps.
Available for freelance consulting, contract development, and CTO-as-a-service. Contact Dilip Singh →
Featured Work
BYOK Multi-Tenant AI SaaS
Bring-Your-Own-Key AI customer support platform. Organizations connect their LLM, upload docs, and AI handles support autonomously with human approval workflows.
Multi-Agent AI Platform
Email, Voice, Chat & Action agents sharing a single Qdrant RAG brain. Multi-LLM support, per-tenant knowledge bases, real-time streaming.
100% Self-Hosted Voice AI
Zero cloud dependency. Pipecat + LiveKit + Whisper + Ollama + pyttsx3. WebRTC delivery with Twilio telephony integration.
Writing
How Google's TPUs actually work — systolic arrays, the MXU, bfloat16, TPU pods and optical interconnects, the XLA/JAX stack, and when to choose TPUs over GPUs for training and inference.
An authoritative deep dive into AI in 2026: neural networks, transformer architecture, LLMs (GPT-4o, Claude Opus 4.8, Gemini 2.5 Pro, LLaMA 4), fine-tuning with QLoRA, RAG, inference optimization, ethics, and real production patterns from Hureka Technologies.
IBM's 0.7 nm chip research is the most significant semiconductor milestone in a decade. Here is what it means for AI hardware, LLM inference costs, edge AI, and the future of computing.
Whether you need a multi-agent AI platform, a self-hosted voice AI, or a full enterprise SaaS — let's discuss your project.
