Miteshkumar P.

You get an AI architect and a full engineering team in one hire. I've been building production AI since 2015, and the 15+ person team I lead at Brainy Neurals has shipped 70+ AI systems in computer vision, RAG chatbots, document AI and process automation for clients in the US and Europe. AI Rescue engineer, Rescue developer. I'm Mitesh Patel, and my career started in C++ at eInfochips, squeezing computer vision models onto edge devices. After leading AI teams as a Senior Machine Learning Engineer at Quantiphi, I founded Brainy Neurals and grew it from a one-person shop into the team you'd be working with. I take every discovery call myself and design the approach, and then my engineers build it and ship it into production. Brainy Neurals is ISO 27001 certified and an NVIDIA Inception member. Our engineers hold NVIDIA certifications, and our project history spans 15+ industries, including construction, manufacturing, healthcare, pharma and legal. COMPUTER VISION We build detection, segmentation, tracking, OCR and depth systems for images and live video, then tune them with TensorRT and DeepStream to run in real time on edge devices like NVIDIA Jetson or on your own servers. On factory lines, these systems flag defects, missing parts, wrong counts and label errors before a product ships. In construction, we built 4D progress monitoring that aligns 360-degree site captures with the BIM model, and symbol detection on electrical drawings for a US estimating firm. Other projects include traffic monitoring with number plate recognition on Jetson and depth-based motion detection that cut video storage by 50 to 80 percent. We also built HoopSights, our own basketball video analytics product. When your model needs labeled data, PixelAnnotation, the annotation company I co-founded, does the labeling with its own quality checks, so you deal with one point of contact for both data and model. RAG AND CHATBOTS We build internal knowledge assistants for your staff and customer support chatbots for your website, both connected to your CRM or database when an answer needs live data. Your chatbot answers only from your own data, and every answer shows the page or record it came from so your team can check it. When your data doesn't hold the answer, the question goes to a person on your team. Shipped work includes a trilingual legal contract analyzer and clinical trial eCRF automation built on RAG with a Neo4j knowledge graph. For a higher education client, we built a multi-agent business simulation platform. Before launch, we build a test set of real questions with you and measure answer accuracy against it, so you see the number before you rely on the system. DOCUMENT AI PDFs, scans, drawings, invoices and contracts become structured data, including pages with complex tables or mixed languages. Our document AI work includes tender analysis that classifies bid documents, medical research paper analysis, dimension extraction from civil engineering drawings for a Swedish client, and PharmaSynapse, our own platform for authoring pharmaceutical batch records. Every field is checked against your business rules, and fields with low confidence go to a human reviewer before anything reaches your ERP or database. BUSINESS PROCESS AUTOMATION We connect LLMs and AI agents to the tools you already use through APIs and n8n workflows, with a human approval step wherever a wrong action costs money. Typical targets are inquiry triage, report drafting, data entry between systems, quote preparation and approval routing. Shipped examples include invoice reconciliation for food delivery platforms and an end-to-end marketing content engine built on API automation. HOW WE WORK WITH YOU You can engage us for an architecture review, a 2 to 4 week proof of concept, a fixed-scope build or a dedicated AI team that joins your sprints. 1. Every fixed-scope build starts from written acceptance criteria and a fixed price per milestone. 2. A proof of concept carries a pass bar agreed in writing, so you know whether the approach works on your data before you fund the full build. 3. Backend, web, mobile, database and DevOps engineers work alongside the AI team, so your model ships inside a working product with APIs and dashboards. 4. You see working software at every milestone, and the project code is yours once paid. 5. NDAs are standard, and we can deploy inside your cloud or on your own servers, air-gapped when data cannot leave your building. Our stack covers Python, PyTorch, OpenCV, YOLO, NVIDIA DeepStream, TensorRT, Triton, PaddleOCR, PyMuPDF, Docling, OpenAI and Claude models, LangChain, vector databases, Neo4j, Kafka, n8n, Docker and AWS. Send me a message with the problem you want solved and a sample of the data you have today. I'll reply with the approach I would take and the first step I'd recommend, whether that's a short proof of concept or a fixed-scope build.