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Capability Models — AI Labs
Your R&D centre
in India. Permanently
owned.
Delivery teams execute what you already know how to build. A research lab builds what nobody has built yet. Miracle Global builds AI Labs in India — purpose-built R&D centres where your team develops proprietary AI capability that compounds permanently.
5–40
AI researchers at launch
45–60
Days to first sprint
4
Lab types
100%
IP & model ownership
India
5–40
Researchers at launch
4
Lab types available
45–60
Days to first experiment sprint
100%
IP and model ownership
IIT
Academic partnership option
What Is an AI Lab
Not faster execution.
Deeper capability.
Delivery teams execute what you already know how to build. They ship features. They implement roadmaps. They are essential — but they do not generate structural competitive advantage on their own.
The companies that will define the next decade in AI are not the ones who implemented the best available models. They are the ones who built capability those models cannot yet replicate.
Delivery Team
Executes what you know
Ships features, implements roadmaps, builds on existing models. Essential for product velocity — but knowledge is bounded by what already exists.
- →Implements LLM APIs
- →Builds product features
- →Ships on your roadmap
- →Knowledge resets when vendor ends
AI Lab
Builds what doesn’t exist yet
Develops novel architectures, trains proprietary models, creates datasets nobody else has. Knowledge compounds into institutional IP permanently.
- →Trains proprietary models
- →Develops frontier capability
- →Builds your IP permanently
- →Knowledge compounds over time
The 4 Lab Types
Four models.
One right for your mandate.
Every AI Lab is purpose-built for its research mandate. The lab type determines the team profile, the research protocols, the infrastructure, and the IP strategy. You don’t choose a generic research team — you choose the lab model that matches what you’re trying to build.
01
🧪
Foundational AI Research Lab
For companies at the AI frontier
AI researchers and ML scientists working on novel model architectures, foundational model training, and proprietary datasets. For companies that need to build what doesn’t yet exist — not implement what already does. Staffed with PhD-level researchers sourced from India’s IITs.
For: AI-first companies, deep tech, corporate R&D functions building at the frontier
02
⚗️
Applied AI Innovation Lab
For companies building structural product advantage
Fine-tuning, agentic pipeline development, RAG architecture research, and proprietary toolchain engineering. Builds AI capability structurally embedded in your product — advantages competitors cannot replicate by calling the same API. The most common lab type for product companies at scale.
For: SaaS companies at scale, AI-native product companies, enterprise technology platforms
03
🔬
Vertical AI Lab
For regulated and domain-intensive industries
Domain-specific AI research in sectors where generic models fail. HealthAI, FinAI, LegalAI, ClimateAI, IndustrialAI. Develops proprietary datasets that do not exist on the open market and models trained on them — giving your organisation AI capability permanently unavailable to competitors.
For: Healthcare, financial services, legal, climate tech, manufacturing, logistics
04
🎓
Academic-Industry Hybrid Lab
For organisations that want frontier research access
Partnership labs co-located with IITs or TIFR — giving your organisation access to frontier research infrastructure, academic talent pipelines, and publication credibility, while building permanent owned capability under your brand. The most prestigious lab model.
For: Companies seeking academic credibility, frontier AI companies, organisations building long-term India AI presence
Why India for AI Labs
The deepest AI research
talent pool outside the US.
India produces more AI and ML researchers annually than any country except the United States. The IIT and NIT pipeline feeds directly into a research ecosystem that spans academia, startups, and global technology companies. For organisations building AI Labs, India is not the cost-efficient option — it is the talent-dense option.
01
IIT Research Pipeline
23 IITs producing world-class AI and ML researchers. IIT Bombay, Delhi, Madras, and Kharagpur run AI labs ranked in the global top 50. The researcher who would cost $180K in San Francisco completed their PhD at IIT and wants to work on hard problems permanently in India.
Top 50 globally — IIT AI labs
02
Research Ecosystem Density
TIFR, IISc, C-DAC, and a growing cluster of AI research organisations in Bangalore, Hyderabad, and Mumbai create a researcher ecosystem with depth across every AI subdomain. India’s AI patent filings grew 300% in four years.
300% growth in AI patents — 4 years
03
Cost Structure for Research
A senior ML researcher with five years of post-PhD experience and two first-author publications costs $55–80K per year in India. The equivalent in the US is $220–280K. Compute infrastructure costs are identical. Research output is comparable.
$55–80K — senior ML researcher salary
How We Build It
From decision to
first experiment in 60 days.
Every AI Lab starts with a GCC Digital Twin session. We map your research mandate, your team profile, your infrastructure requirements, and your IP strategy before a single researcher is hired.
01
Lab Digital Twin Session
We map your research mandate, lab type, researcher profile, infrastructure requirements, and IP strategy. You see your AI Lab before you build it.
Week 1 — 60 min intake · 48hr model · 90 min presentation
02
Research Architecture Design
Lab structure, research domains, team composition, and IP framework designed before sourcing begins. Academic partnership scoped if applicable.
Week 2 — runs parallel to legal setup
03
Legal Entity & IP Structure
Entity registered. IP assignment framework established. Publication agreements and academic partnership legal structure completed before first hire.
Weeks 2–4
04
Researcher Sourcing & Hiring
Domain-specific sourcing from IIT alumni, TIFR networks, and the Indian AI research community. Every candidate passes research portfolio review. You interview the shortlist.
Weeks 3–7 — first researcher shortlist week 3
05
Lab Infrastructure & First Sprint
Compute infrastructure, research toolchain, data access, and experiment tracking in place before the team arrives. First experiment sprint runs within 45 to 60 days.
Day 45–60 — first experiment sprint
What You Own
Every model, dataset,
and research output.
Permanently yours.
Research IP
Your models
Every model trained, every architecture developed, every dataset created belongs to you by contract from the first experiment.
Publication Rights
Your research
Publication decisions are yours. You control what is published, when, and under whose name. No researcher publishes without your approval.
The Institution
Your lab
The entity, the team, the infrastructure, and the research culture. The BOT path transfers full independent operational control when you’re ready.
Lab vs GCC — Can You Have Both?
Yes. Many organisations run an AI Lab alongside their GCC — the Lab develops frontier capability, the GCC deploys it into products. The two structures are designed to work together under the Capability System™.
Explore Further
Build the full
capability architecture.
Complement Your Lab
Enterprise GCC
The Enterprise GCC deploys what your AI Lab builds. Research and delivery operating together under one capability architecture.
Start Smaller
AI Research Pod
Not ready for a full AI Lab? An AI Research Pod is a 3 to 15 person research team — same ownership model, faster to deploy.
Begin Here
Lab Digital Twin Session
We map your lab type, researcher profile, and IP strategy before you hire anyone. 60-minute session, 48-hour turnaround.
AI Labs in India
Build the capability
your competitors can't access.
Design your AI Lab before you hire anyone. We’ll map your lab type, your researcher profile, and your IP strategy in one session.