Most teams suffer from
Junior-heavy execution
Rotating ownership
Unsafe AI adoption
Leadership bottlenecksThese are design failures, not people failures
These are not India problems, these are vendor-model problems.
And they require a different operating model, not more seats.
Model Difference — India Context
Dimension | Traditional Offshore | India Strategic Unit |
|---|---|---|
| Structure | Seat-based | Unit-based |
| Leadership | Offshore escalation | Embedded seniority |
| Accountability | None | One unit, one owner |
| Quality | Regression-prone | 20–40% improvement |
| AI | Unsafe shortcuts | Guardrailed, disciplined |
Upgrade your engine: India Edition
India then operates as
A product-owning engineering unitA scale-ready engineering functionA model governed by AI-safe practicesAn output-led capability center
Limited output /
slow delivery
Ticket Factory: task-based, high ownership, high churn
Empowered HQ
Empowered HQ
Product
Ownership Team
Core Innovation, Strategic
Influence, Engaged Team
Platform
features
Expansion
Scaled delivery: Faster releases, high value global impact.
The Engineering Pods
Choose what works for your team
Product Ownership Unit (25)
Purpose
- Give an entire product slice a real owner not a vendor rotation.
When You Use This
- A feature set keeps slipping
- Integrations or modules need seasoned leadership
- You want safe, real AI implementation
- You want one team accountable end-to-end
Team Mix
- 5 Senior Backend
- 3 Senior Frontend
- 4 Mid Engineers
- 3 QA
- 2 Automation QA
- 2 DevOps
- Product Analyst
- UX/UI Designer
- Delivery Manager
- 2 Flex Seniors
- Optional: 1-2 Data/ML Engineers
Strategic Scale Unit (50+)
Purpose
- Support major migrations
- Lead multi-quarter initiatives
- Enable AI as a core capability
When You Use This
- You're planning major migrations, rebuilds, or multi-quarter initiatives
- The board is pushing for aggressive delivery
- You want AI capabilities as a real engineering function, not an experiment
- You need scale without compromising quality
Team Mix
- Engineering Manager
- Architect
- 12 Senior Engineers
- 16 Mid Engineers
- 6 QA
- 4 Automation QA
- 4 DevOps
- 1 SRE
- 1-3 Data/ML Engineers
- Data Analyst
- UX/UI
- Delivery Manager
How we work
Our 4-step launch model
Assessment
We assess your current engineering setup. Teams, ratios, seniority, sprint history, regression trends.
Redesign senior ratios
We map the exact unit you need. Right roles, right levels, right senior ratios.
Launch in 3–4 weeks
We launch the unit in 3-4 weeks Fast, predictable onboarding—no chaos.
Stabilize, then scale
We measure actual change using sprint velocity, regression rate, release frequency, and a leadership confidence index.
How Distributed Engineering Pods Drove 3-5% Infra Savings and 4× AI Adoption for a Global Travel Platform
"We were trapped in a hiring loop -months of recruitment, months of onboarding, still missing the mark. Uplers flipped that entirely.
They arrived with a data-driven methodology and deployed exactly what we needed: senior architects who could set direction, mid-level engineers who could execute at pace.
Within weeks, not months, we had teams moving on infrastructure modernisation and analytics platforms. Suddenly, we weren't asking 'when can we hire?' anymore - we were asking 'what can we build next?' That shift in velocity transformed how we operate."
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