The stakes are high. India wants to turn promising AI experiments into products that solve local problems—such as crop prediction, healthcare diagnostics, and fraud detection—at a national scale. The Alliance is designed to unlock resources, reduce risk for founders, and drive broader AI innovation in India.

What the Deep Tech Funding Alliance brings to Phase 2

First, the Deep Tech Funding Alliance pools funding and expertise. Second, it matches corporate partners with research labs and startups. Third, it helps projects scale infrastructure and talent quickly. In short, the Alliance is a growth engine for the IndiaAI program.

Moreover, by bringing BharatGen, Tech Mahindra and Fractal into the fold, the Alliance widens its technical and sectoral reach. BharatGen brings genomics and biotech capabilities. Tech Mahindra offers enterprise deployment and systems integration. Fractal contributes advanced analytics and AI model engineering. Together, they accelerate practical, locally relevant solutions.

BharatGen: Genomics meets national AI goals

BharatGen’s entry signals an important trend: health and biotech will be central to IndiaAI Phase 2. BharatGen will work with the Alliance to develop genomic AI tools that support disease surveillance and personalized medicine. For example, AI models trained on local genomic datasets can spot outbreak patterns faster and suggest targeted responses.

Importantly, BharatGen’s participation underscores a broader aim of the Deep Tech Funding Alliance: build AI that respects Indian data sovereignty while improving public health outcomes.

Tech Mahindra: industrial scale and enterprise reach

Tech Mahindra brings a commercial muscle that many startups lack. The company will help design enterprise-grade deployments for government agencies, hospitals, and large manufacturers. As a result, pilots can move to production faster and with fewer integration headaches.

Furthermore, Tech Mahindra’s role strengthens the Alliance’s promise to make AI innovation in India operational and enterprise-ready.

Fractal: advanced analytics and real-world models

Fractal will provide teams that specialize in turning messy data into reliable AI models. They will also mentor startups on model governance, fairness checks, and explainability. This is crucial because trustworthy models increase adoption in sectors like finance, insurance, and public services.

Thus, Fractal’s involvement boosts the Alliance’s capacity to deliver robust AI that regulators and citizens can trust.

How Phase 2 differs from the first phase

Unlike Phase 1, which focused on research and pilot programs, IndiaAI Phase 2 emphasizes scale and deployment. The government wants proven AI solutions to operate in real conditions, not just in lab settings. Consequently, the Alliance will fund infrastructure, provide cloud credits, and back training programs so projects can be rolled out nationwide.

Moreover, Phase 2 includes clear guardrails on ethics and governance. It requires teams to publish model bias audits, document data sources, and follow privacy best practices. This combination of scale and compliance is a key element of the Alliance’s mission.

Funding, infrastructure, and developer support

The Deep Tech Funding Alliance will invest in compute, data pipelines, and open datasets. It will also fund incubators and accelerator programs that target founders building for India’s unique markets. In addition, the Alliance will sponsor regional AI hubs to decentralize innovation across smaller cities and towns.

To make this practical, the Alliance plans to offer three types of support:

• Seed and growth funding for promising startups.
• Cloud and compute credits for research labs and companies.
• Mentorship and enterprise partnerships to accelerate product-market fit.

These measures should reduce the common pitfalls that stall AI projects—lack of money, lack of infrastructure, and lack of connections to paying customers.

Building talent and governance across India

Scaling AI requires people. Accordingly, the Alliance is funding education and certification programs. For technical teams, the AI+ Engineer™ certification provides practical skills in building models and deploying them at scale. For data leaders, the AI+ Data™ credential teaches pipeline design and data governance. And for business leaders, the AI+ Business Intelligence™ training helps align AI projects with measurable outcomes.

These programs will help keep the talent pipeline local, reducing brain drain and increasing the pool of deployable engineers and product managers.

Early use cases and expected impact

The Alliance is prioritizing verticals where AI can produce quick, measurable benefits:

1. Agriculture: AI models will predict crop stress and optimize irrigation. In turn, farmers can increase yield and reduce input costs.
2. Health: Genomic tools and predictive diagnostics can speed up disease detection and resource allocation.
3. Public services: AI can optimize traffic, power distribution, and welfare disbursement—improving efficiency and fairness.

Consequently, these real-world pilots will test whether the Deep Tech Funding Alliance can translate innovation into economic and social gains.

Addressing common challenges

Adoption will not happen without tackling known obstacles. For instance:

• Data gaps: Rural and small-business datasets are patchy. The Alliance will invest in structured data collection to improve model accuracy.
• Regulation: Clear rules on privacy and consent are essential. IndiaAI Phase 2 will work with regulators to set pragmatic standards.
• Cost: Training models is expensive. The Alliance’s compute subsidies will lower that barrier for qualified projects.

By confronting these issues early, the Alliance aims to increase the odds that pilots will scale successfully.

Why this matters for startups and investors

For startups, the Alliance provides two big advantages: access to funding and pathways to scale. Rather than spending months chasing pilots, founders can tap into a network that connects them to enterprises and public-sector buyers.

For investors, the Deep Tech Funding Alliance de-risks deep tech bets. When capital partners, industry leaders, and the government align, investors see clearer exit paths—via enterprise procurement, public contracts, or industry acquisitions.

Therefore, the Alliance could tilt investor attention toward India-based deep tech companies that build sustainably for local markets.

Regional inclusion and decentralization

Crucially, this effort avoids concentrating AI infrastructure only in major metros. The Alliance plans regional nodes and training centers in smaller states. As a result, talent from second-tier cities will gain exposure, and local problems can be solved with local teams. This decentralized approach makes AI innovation in India more inclusive and resilient.

Measuring success: KPIs and accountability

The Alliance has set measurable goals for Phase 2. These include:

• Number of deployed pilots that hit production.
• Jobs created in AI and adjacent industries.
• Percentage improvement in target outcomes (e.g., yield gains, diagnostic speed).
• Number of certified professionals trained under partner programs.

Additionally, the Alliance will publish progress reports to maintain transparency and build public trust.

Global positioning and export potential

If successful, Phase 2 can position India as an AI exporter. Solutions built for Indian conditions—low bandwidth, diverse languages, and fragmented markets—could work well in other emerging economies. Thus, the Deep Tech Funding Alliance could seed startups that go global with products tailored to constrained environments.

Risks and cautions

However, the plan has risks. For example, scaling too fast could create poorly governed systems. To avoid that, Phase 2 emphasizes model audits and third-party reviews. Moreover, if government procurement favors large incumbents over startups, smaller innovators may be squeezed out. The Alliance must balance speed with fairness.

Conclusion

The inclusion of BharatGen, Tech Mahindra, and Fractal strengthens the Deep Tech Funding Alliance and gives IndiaAI Phase 2 a practical pathway to scale. By combining funding, enterprise partnerships, education, and governance, the Alliance aims to move India from ambitious pilots to deployed AI that helps people and businesses every day.

If Phase 2 succeeds, it could reshape how the world thinks about AI innovation in India—from consumption to creation, and from lab experiments to large-scale social impact.

👉 For more on the Alliance’s global moves, read our previous report: Deep Tech Funding Alliance Expands Google’s AI Infrastructure in Africa. It explains how similar partnerships are accelerating regional AI hubs.