The AI team you own.
Deployed in 30 days.
Specialists per Pod
21–30
Days to first sprint
8
AI domains covered
100%
Your IP from day one
What Are AI Pods
A delivery team built
for one domain.
Owned by you.
Most companies trying to build AI capability make the same mistake. They hire generalists, engage a vendor, or bolt AI features onto an existing engineering team. None of these produce owned, compounding capability.
An AI Pod is different. It is a focused team of AI specialists working in a single domain — all employed directly by you, all building IP that belongs to your organisation. Three engineers or thirty. LLM specialists or computer vision researchers. The Pod is sized for the problem, not a vendor’s billing model.
The Pod is also the foundation of everything that scales. A Nano GCC is a structured Pod. A Micro GCC is multiple Pods under one architecture. Every stage of the Capability Evolution Model™ starts here.
“An AI Pod is not a team you rent for a project. It is the first unit of a capability institution you are building permanently.”
Miracle Global — Capability Architecture
Domain-Specific
Every Pod is built around one AI domain. No generalists. No shared pools. A team that goes deep on the problem you actually have.
Fully Owned
The team is employed by your entity. The IP is yours by contract from day one. There is no vendor dependency built into the model.
Designed to Scale
A Pod is sized to start, not to stay. As your mandate grows, it scales — into a Nano GCC, a Micro GCC, or an Enterprise Centre.
Operational in 30 Days
GCC Digital Twin on day one. First hire within 10 days. First sprint within 30. Every milestone defined before you commit.
LEVEL 01
🤖
AI Pods YOU ARE HERE
3–30 specialists · Single domain · 21–30 days
LEVEL 02
🏭
Nano GCCNEW
5–30 specialists. Owned innovation centre. Live in 30–45 days.
LEVEL 03
🏗
AI Micro GCCFLAGSHIP
20–80 specialists. Multi-domain AI centre. Live in 60–90 days.
— Most popular · Full capability depth
LEVEL 04
🏢
Enterprise GCC
100+ specialists. CoE-grade innovation architecture.
Pod Types
Eight domains.
One for your problem.
Every AI Pod is built around a specific domain. The domain determines the team composition, the seniority mix, the hiring cities, and the delivery cadence. You don’t choose a generic AI team — you choose the domain that matches what you’re building.
🧠
LLM & Generative AI
Fine-tuning, RAG pipelines, prompt engineering systems, model deployment, and evaluation frameworks. For companies building AI-native products or deploying internal LLMs at scale.
→ LLM Engineers → Prompt Engineers → RAG Specialists
⚙️
MLOps & Infrastructure
Model training pipelines, monitoring, drift detection, CI/CD for ML, and inference cost optimisation. For teams with models in production that need reliability and scale.
→ MLOps Engineers → Platform Engineers → Data Engineers
📊
Data Science
Predictive modelling, customer analytics, demand forecasting, and recommendation systems. For organisations with large data assets that need intelligence, not just dashboards.
→ Data Scientists → ML Engineers → Analytics Engineers
👁
Computer Vision
Object detection, quality inspection, document processing, medical imaging, and satellite analysis. For manufacturing, healthcare, logistics, and property tech.
→ CV Engineers → Image ML Specialists → Video AI Engineers
🤖
AI Automation
Agentic workflow systems, RPA+AI, document processing, and customer service automation. For any organisation with high-volume intelligent processes that should not require human intervention.
→ Agentic AI Developers
🚀
AI Product Development
End-to-end AI feature development, integration engineering, and user-facing AI systems shipped on your roadmap. For SaaS companies that need AI in production, not in a backlog.
→ AI Product Engineers
🔬
AI Research
Novel model architecture, paper-grade research, and proprietary dataset development. For AI-first companies and corporate R&D functions building at the frontier — not implementing it.
→ Neural Network Engineers
🏥
Vertical AI (Health / Fin / Legal)
Domain-specific AI models trained on regulated data, compliance-aware systems, and vertical datasets unavailable on the open market. For regulated industries.
→ Domain AI Specialists
→ Vertical Data Scientists
Delivery Model
How a Pod goes from
decision to sprint.
The delivery model is the same across every Pod type. Every milestone is defined before you commit. No ambiguity. No surprise delays.
01
GCC Digital Twin™ Session
We run your Digital Twin before anything else. Domain selection, team composition, city recommendation, cost model, and 30-day activation plan — all mapped and presented to your leadership team.
Day 1 — 60 min intake · 48hr turnaround · 90 min presentation
02
Legal Entity & IP Structure
Your Indian entity is registered. IP assignment agreements are signed before a single hire is made. Ownership is established in the legal structure from day one.
Days 3–10 — runs parallel to sourcing
03
Talent Sourcing & Screening
We source from Miracle Global’s Distributed Capability Network™ across 20+ India cities. Every candidate passes a domain-specific technical screen. You interview and approve every hire.
Days 7–18 — first shortlist within 7–10 days
04
Onboarding & Infrastructure
Workspace, equipment, toolchain, access credentials, and governance framework are all in place before the team’s first day.
Day 18 — 26
05
First Sprint Ships
The first sprint runs on your priorities, your roadmap, your sprint cadence. You get delivery velocity data from week one.
Day 21–30 — first sprint complete
What You Own
Everything built by your Pod
belongs to you. Always.
The Team
Your employees
Every specialist is employed by your entity. They work for you — not a vendor.
The IP
Your code & models
All IP assigned to you contractually before work begins. Every model, every dataset, every line of code.
The Knowledge
Your institution
Documentation and knowledge management protocols mean nothing walks out when someone leaves.
The Entity
Your structure
When you’re ready, the BOT model hands full entity control to you. Miracle Global steps back on your timeline.
Outcomes & Use Cases
What AI Pods actually
deliver.
The output depends on the domain. Here is what a Pod produces across the eight domains Miracle Global specialises in — and the types of organisations that deploy them.
🧠
LLM & Generative AI
Custom LLMs in Production
Fine-tuned models, RAG pipelines, evaluation frameworks, and inference-optimised deployments. Proprietary models your competitors cannot access.
→ AI product companies · SaaS platforms · Enterprise LLM deployments
⚙️
MLOps & Infrastructure
AI That Runs Reliably
Training pipelines, monitoring, drift detection, CI/CD for ML. The difference between a model that works and a model that works every day at scale.
📊
Data Science
Intelligence From Your Data
Predictive models, recommendation systems, demand forecasting, and customer analytics that turn raw data assets into product decisions and revenue outcomes.
→ E-commerce · Fintech · Logistics · SaaS with large data assets
👁
Computer Vision
Systems That See
Object detection, quality inspection, document processing, and medical imaging. Automating visual tasks that require human judgment at scale.
→ Manufacturing · Healthcare · Logistics · Property tech
🤖
AI Automation
Processes That Run Themselves
Agentic workflows, RPA+AI hybrids, and intelligent document processing. Replacing high-volume repetitive work that should never require human attention.
→ Finance ops · Legal · Insurance · Customer operations
🚀
AI Product Development
AI Features Shipped
End-to-end AI feature development on your roadmap. Not a prototype. Not a proof of concept. Production-grade AI shipped on your timeline.
→ SaaS companies · AI-native startups · Product-led growth companies
🔬
AI Research
Capabilities Nobody Else Has
Novel architectures, proprietary datasets, and frontier research that builds structural AI advantages unavailable through any model API or open-source library.
→ AI-first companies · Corporate R&D functions · Deep tech
🏥
Vertical AI
Domain Intelligence At Depth
Sector-specific models trained on regulated data. HealthAI, FinAI, LegalAI, ClimateAI — built by specialists who understand the domain, not just the data.
→ Healthcare · Financial services · Legal · Climate tech
What Comes Next
Scale your Pod
into something permanent.
Scale Up — Level 02
What is a Nano GCC
The next step from an AI Pod. A full, owned capability centre of 5 to 30 people, operational in 30 to 45 days.
Flagship — Level 03
AI Micro GCC
20 to 80 specialists. Multi-domain. The model most organisations grow their Pod into at 6 to 12 months.
Start Here
GCC Digital Twin™
See your Pod or GCC before you build it. Team, cost, and scaling path — all mapped before you commit to anything.
Begin Your Journey