CHIRAG NATESH VIJAY GERMANY
I turn signals into systems. Explainable ML at Bosch, agentic AI and RAG pipelines on GitHub, and Cellvara, my AI decision intelligence startup for pharma and biotech teams.
WHO I AM
I'm an AI engineer and founder based in Germany. Over the last six years I've shipped machine learning in industry at Bosch eBike Systems, done signal processing research at the University of Passau, published IoT work with ACM, and patented an agriculture drone back home in Bengaluru.
An AI system should verify its own output, explain itself, and stay honest. Confidence without evidence isn't intelligence.
These days I split my week between applied AI work at Hilti, a second master's at Neu-Ulm, and building Cellvara. The hardest problems in AI right now aren't purely technical. They're about trust, workflows, and getting real teams to actually adopt this stuff.
CURRENT VENTURE
Pharma and biotech SMEs know AI could help them. Most just can't afford six-figure consultants to figure out where, how, and whether it's safe. Cellvara is an AI consulting copilot that turns that uncertainty into a structured answer: the right use case, the evidence behind it, and a compliance-aware path to implementation.
Consultant chat that reasons over the company's own profile, uploaded documents and reusable organisational memory. Not vibes.
Explainable steps, traceable reasoning and human-in-the-loop governance, designed around R&D, QA, regulatory and pharmacovigilance workflows.
Shaped through customer discovery with pharma professionals, the Cyber Valley AI Incubator and UnternehmerTUM / TUM Startup Launchpad.
SELECTED WORK
Twelve builds across agentic AI, RAG, multimodal systems and applied ML. Every card says what it really is: open source, prototype, research or hackathon build.
Ask a question, get an answer you can trust. A multi-agent RAG system where verification agents fact-check every response against the source documents before it reaches you. Hybrid BM25 + vector retrieval with self-correction loops.
DEMONSTRATESReliable RAG & verification architecture
Drop in your CV and a job description, get a personalised mock interview and a coach-style report. Evaluator and coach agents score your answers on clarity, depth and relevance through a contract-first API.
DEMONSTRATESAgent orchestration, structured outputs & evaluation
A healthcare triage assistant that knows its limits. Specialist agents run the consultation while deterministic red-flag checks catch emergencies and force escalation. The safety logic never depends on the model behaving.
DEMONSTRATESSafety-aware agents & structured generation
Educational demo, not a medical device
Supply-chain intelligence for CPG teams, built in one weekend at the TUM.ai × Spherecast Makeathon 2026. A function-calling agent queries live procurement data, finds ingredient substitutes and explains every tool call across 1,000+ products and 40 suppliers.
DEMONSTRATESConversational analytics over a real data model
An AI co-founder for the messy zero-to-one phase. Multi-agent pipelines take a raw idea and produce problem framing, market research, ICP personas, business models, go-to-market strategy and a generated pitch deck.
DEMONSTRATESEnd-to-end multi-agent product pipelines
Record a meeting, walk away with the minutes. Transcribes speech, cleans up messy domain jargon, and turns an hour of talking into structured summaries and action items.
DEMONSTRATESSpeech AI, domain adaptation & workflow automation
Point it at a photo of your plate. Specialist agents identify the ingredients, break down the nutrition and suggest recipes. Vision-language AI applied to something everyone does three times a day.
DEMONSTRATESMultimodal AI & coordinated specialist agents
A three-hour YouTube lecture, answered in seconds. Extracts the transcript, summarises it, and answers questions grounded strictly in what was actually said. Its limitations are documented in the README, not hidden.
DEMONSTRATESVideo intelligence & retrieval pipelines
Feed it a vacancy, your CV and your GitHub profile. Role-based CrewAI agents research the position, tailor your story to it, and generate polished application materials as ready-to-send PDFs.
DEMONSTRATESDocument automation & role-based agent teamwork
What would it actually take to run fraud detection in production? A full system spec engineered around latency, recall, throughput, drift monitoring and security. The unglamorous constraints that decide whether ML survives contact with reality.
DEMONSTRATESProduction ML thinking & measurable system design
Deep residual networks classifying chest X-rays across COVID-19, pneumonia and healthy cases. Transfer learning in a domain where careful evaluation and error analysis genuinely matter.
DEMONSTRATESMedical imaging & transfer learning
An LSTM that learned jazz. Trained on chord and note sequences, it improvises original solos one note at a time. Early proof that sequence models can swing, and that AI can be playful too.
DEMONSTRATESGenerative sequence modelling
THE PATH
RESEARCH & IP
Explainable Boosting Machines that translate vibration signals into third-octave acoustic bands and perceived loudness at under 5% MAE, with global and local interpretability that engineers can act on. University of Passau × Bosch eBike Systems, 2024.
XAI · EBM · SIGNAL PROCESSINGModular smart-infrastructure architecture for retrofitting homes and campuses with low-cost edge devices and MQTT/REST orchestration. Published at the 4th International Conference on Vision, Image and Signal Processing (ACM VISP).
DOI: 10.1145/3448823.3448864 ↗Autonomous precision-agriculture drone using CNN-based computer vision to detect diseased plants and dispense treatment only where it's needed. Less blanket spraying, better resource use. Indian Patent Office, application 201941047691.
COMPUTER VISION · UAV · AGRITECHTOOLBOX
IN THEIR WORDS
Pulled straight from LinkedIn. People I've led, shipped with and studied beside.
An exceptional leader who balances strategic vision with hands-on support. He kept the team focused, motivated and aligned, and knows exactly how to bring out the best in a team.
His ETL pipelines slashed data preprocessing from hours to just 5–10 minutes, and his improvements to our Explainable AI sound-rating model raised accuracy by roughly 55%. Technically strong, proactive and a great collaborator.
Exceptional expertise in data science, responsible machine learning and AI. Beyond his technical strengths, he is an outstanding and reliable team player, always willing to help colleagues.
He used Explainable AI to rate the sounds of different e-bike drive units, making complex models understandable and useful. Above all, he is incredibly talented and a great team player.
Impressive problem-solving skills and deep expertise in Python, machine learning, deep learning and IoT. What I admire most is his willingness to patiently help others until everyone is on the same page.
Proficient across Python, machine learning, deep learning and IoT. His problem-solving abilities were a valuable asset to the outcome of every project we worked on together.
WANT THE FULL STORY?
SAY HELLO
Hiring for AI or ML roles? Curious about a Cellvara pilot? Or just want to talk agents, RAG and startups? My inbox is open.
or write to chiragatgermany@gmail.com