If you’ve been following India’s AI policy landscape recently, you may have come across the term “NeGD empanelment”, and wondered what it actually means, why it matters, and why only a handful of companies made the cut. This post answers all of that. And yes, Innefu is one of the six.
First, What Is NeGD?
NeGD stands for the National e-Governance Division. It operates under MeitY, the Ministry of Electronics and Information Technology, and sits at the nerve centre of India’s Digital India programme.
Think of NeGD not as a department that builds things, but as the authority that enables others to build things, correctly, consistently, and at scale. Its mandate spans the design, development, and deployment of e-governance systems across central ministries, state governments, and public agencies.
The challenge NeGD is solving is a real one. Ministries and departments across the country have been exploring AI independently, each procuring vendors separately, building solutions in silos, duplicating infrastructure, and producing results that cannot be standardised or shared.
The cost is not just financial. It’s the cost of inconsistency, of incompatibility, and of deploying AI without the governance frameworks that government systems demand. NeGD’s answer to this problem is the empanelment framework.
What Is the AI/ML Empanelment, And Why Does It Exist?
The Empanelment of Partner Agencies for Deployment of AI/ML Resources and Discovery of Rates for AI-Specific Manpower under Digital India is NeGD’s way of building a pre-vetted, pre-approved panel of AI/ML agencies that any government ministry or department can engage directly, without going through a lengthy, repetitive procurement process every single time.
The logic is elegant. Instead of every ministry independently evaluating vendors, negotiating contracts, and hoping they’ve chosen the right partner, NeGD does that evaluation once, rigorously, on behalf of the entire government. The companies that clear the bar are empanelled. The ministries then simply reach into the panel.
This creates three things that have been largely absent from government AI adoption until now:
Standardisation: Every empanelled agency has been evaluated against the same criteria, technical capability, prior government deployment experience, data sovereignty compliance, and responsible AI practices. There is a common baseline.
Speed: Procurement timelines that previously stretched to months can now compress dramatically. The vetting has already been done.
Accountability: Empanelled agencies are not just vendors. They are authorised partners, held to the standards NeGD has defined, including auditability, explainability, and adherence to India’s data localisation norms.
Overall Scope of the Empanelment
The empanelled agencies shall provide manpower for end-to-end AI/ML application support, including but not limited to:
- End-to-end AI project execution, from problem definition to deployment;
- Building reusable AI components and APIs;
- The implementation of NeGD’s AI Services and LLM infrastructure;
- Participating in running pilot programmes and POCs;
- Conducting Responsible AI compliance testing and data governance activities.
Purpose of the Empanelment
The empanelled agencies will provide specialized AI/ML professionals to:
- Support government projects that require technical expertise in AI, data science, and related technologies.
- Assist NeGD in problem identification, solution prototyping, model development, and deployment by deploying the required manpower.
- Enable rate standardization to facilitate transparent procurement and efficient budgeting for AI manpower engagements.
- Provide on-demand staffing support to ministries and departments, either directly or through NeGD coordination.
What Did the Empanelment Process Actually Look For?
This is where the empanelment becomes genuinely significant, because what NeGD evaluated was not simple. Approximately 80 companies participated. Six were empanelled.
The evaluation was multi-dimensional. It wasn’t enough to demonstrate that a company could build AI. NeGD was looking for companies that had already built AI, at scale, for government, under conditions that demanded security, sensitivity, and zero tolerance for failure.
Specifically, the framework assessed capability across seven distinct AI domains: Cognitive Analytics and Decision Intelligence, Computer Vision and Document Processing, Conversational AI, Generative AI and LLM Engineering, Voice-based Interfaces, Data Engineering and MLOps, and Responsible AI and Governance.
Beyond technical depth, the process examined whether companies could build and deploy AI on government-approved infrastructure without routing sensitive data through external or foreign cloud environments. It examined whether companies had frameworks for explainability, bias detection, human-in-the-loop validation, and audit trails. It examined whether they could build not just for one ministry but for many modular, reusable, API-driven solutions that could be standardised and shared across the government ecosystem.
In short, it asked whether a company was ready not just to build AI, but to build AI that India’s governance systems could trust.
Where Innefu Labs Fits
Innefu Labs was founded in 2010 with a singular belief: that India needed indigenous AI capability, built here, deployed here, accountable to India’s laws, India’s infrastructure, and India’s citizens.
For fifteen years, that belief has been tested in the field. Innefu has been deployed inside some of the country’s most critical and sensitive institutions: intelligence agencies, paramilitary forces, law enforcement agencies, financial intelligence units, and tax authorities. Systems that process intelligence at national scale. Platforms that surface financial fraud worth thousands of crores. Identity and analytics infrastructure that operates where failure is simply not an option.
That experience, combined with a full-stack AI capability spanning LLM infrastructure, multilingual conversational AI, intelligent document processing, computer vision, agentic AI frameworks, and a mature MLOps and responsible AI practice, is what Innefu brought to the NeGD empanelment.
And it is what NeGD evaluated, tested, and ultimately recognised.
