NirmanAgents.ai is a founder-led AI consulting practice combining the strategic rigour of McKinsey consulting with the rare ability to personally architect and deliver Agentic AI, IoT, AR and Multimodal systems — from roadmap to live operations.
Most AI consultants hand you a strategy deck and leave. Rajiv Pujar does both — he brings McKinsey-trained strategic discipline to define the right problem and business case, and then stays to architect and deliver the system himself. That combination is genuinely rare in the AI consulting market today.
With a background spanning global management consulting at McKinsey and deep hands-on engineering in Agentic AI, IoT sensor networks, Augmented Reality, Voice/IVR systems and Multimodal AI — Rajiv founded NirmanAgents.ai to offer something the market lacks: a senior practitioner who thinks in business outcomes and delivers in production code.
NirmanAgents.ai serves MSMEs taking their first AI step, Enterprises scaling agentic workflows and global startups needing a senior technical partner without a bloated consulting team.
LangGraph, CrewAI, AutoGen, OpenAI Agents SDK — workflow design to production.
Sensor networks, edge AI, digital twins and real-time operational systems.
Field overlays, remote expert assist and AR-guided operational intelligence.
Multilingual voice agents, IVR modernisation and conversational AI systems.
Vision + language + sensor fusion for richer diagnostics and decisions.
Context-aware mobile AI apps with beacon, location and agent integration.
The AI consulting market is crowded with generalists producing slide decks. Here is what NirmanAgents.ai offers that most cannot.
Every engagement is led by Rajiv directly — not passed to a junior analyst after the first call. You get senior thinking, senior accountability and senior delivery from day one through go-live.
We never arrive with a pre-packaged solution. Every engagement begins with an AI Discovery Sprint that maps your actual business problems, data landscape and operational constraints before recommending anything.
McKinsey-trained business case and problem framing combined with the technical ability to personally architect Agentic AI, IoT, AR and Voice systems. No handoff between strategy and delivery — one practitioner holds both.
Pilots are designed to reach production, not just impress in a demo. Every AI blueprint includes operational requirements, integration paths, governance guardrails and a clear handoff or ongoing partnership plan.
A structured process that moves from business problem to deployed AI — without detours into technology theatre.
We map your business landscape, data assets, operational constraints and AI readiness. Output: a prioritised opportunity map, business case for the top use case and a 12-month pilot roadmap. This is the only starting point we recommend.
We design the full technical architecture for your AI pilot — data pipelines, agent orchestration, integration points, IoT/AR/Voice layers where relevant, and KPIs. You get a buildable specification, not a vague roadmap.
We architect and build the system personally or with a vetted partner network — following agile sprints with working software at each checkpoint. No black-box delivery. You see progress every two weeks.
We hand off working systems with documentation, training and operational runbooks — or continue as your embedded AI partner for scaling. Either path is planned and agreed up front.
NirmanAgents.ai brings vertical-specific AI knowledge — not generic frameworks — across 14 industry sectors, with 186 mapped use cases and sector-specific technology patterns.
+ EV & Automotive, Mining & Minerals, Life Sciences, Smart Cities, Transport & Mobility. View all 14 industries →
Short, focused pieces to help leaders and operators understand Agentic AI, IoT integration, AR field systems, Multimodal AI and the governance questions that matter in production.
Why connecting physical sensors to AI reasoning layers is harder — and more valuable — than any chatbot deployment. The three failure points and how to avoid them.
Read article →How augmented reality changes the human-AI interaction model for factories, hospitals and construction sites — and why heads-up intelligence beats screen-based tools in operational environments.
Read article →Millions of IVR minutes are wasted daily. Here's how to modernise them with LLMs without throwing away what works — and where the real enterprise value lies.
Read article →Real diagnostics, real quality inspection and real field intelligence require fusing vision, audio, sensor and text — not just one modality. A practical primer on multimodal system design.
Read article →How to pick a use case that is physically grounded, measurable and operationally bounded — and why starting with the most exciting use case is usually the fastest way to fail.
Read article →GenAI on documents is becoming commoditised. The real differentiation now lives at the edge — sensors, AR and voice in real operational environments where most advisories have no delivery depth.
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