Team Solution Case Study Get Started
Become AI-Native Fast

Scale OPEX,
Not Headcount

Convert human hours into compute hours.
Convert headcount into operational expenses.

20-50% more productive AI-enhanced engineers

Get Started See Case Study

Meet the Founders

Industry veterans who've been building AI before it was mainstream

Ziyad Mir

Ziyad Mir

Partner & Principal Engineer

Serial innovator with deep expertise in AI systems at scale. Invented groundbreaking technologies including YouTube's AI-powered Peak Points advertising system, revolutionizing how billions experience video content. Expert in large-scale ML infrastructure and product development.

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Amit Gaur

Amit Gaur

Partner & General Manager

Seasoned engineering executive with extensive experience building and scaling AI teams at Fortune 500 companies. Deep expertise in enterprise AI adoption, MLOps, and engineering excellence. Track record of delivering transformative AI solutions that drive measurable business impact.

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Hani Mir

Hani Mir

Partner & Principal Engineer

ML engineer previously at Character.AI, with experience at Jane Street and as former CTO/Co-founder of ELI5. Brings deep expertise in high-frequency trading systems, AI infrastructure, and building products from 0 to 1. Alumni of Google, Uber, LinkedIn, and Microsoft.

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The Traditional Scaling Problem

  • Headcount Scaling is Expensive – $250K+ per engineer annually plus benefits, equipment, management overhead
  • Hiring is Slow & Risky – 6+ months to hire, onboard, and achieve productivity
  • Management Complexity – each new hire requires more coordination, process, and overhead

Result: 100 engineers → 130 effective engineers = 30% cost increase for 30% capacity increase

🏢

Traditional Scaling

Cost per Engineer $250K+/year
Time to Productivity 6+ months
Capacity Increase +30%
Total Cost Increase +30%
🚀

AI-Native Scaling

Cost per Engineer $12K/year
Time to Productivity 30 days
Capacity Increase +50%
Total Cost Increase +10%

The AI-Native Solution

Scale via OPEX: ~$1,000/engineer/month in AI tools (passthrough pricing from vendors, no markup) vs. $12,000+/month for new hires

  • 20-50% Productivity Gains – Each engineer becomes 1.2-1.5x more effective
  • 10x AI-in-the-Loop Engineers – Optimized workflows across coding, ops, design, and research
  • Immediate Impact – Productivity gains in 30 days vs. 6+ months for hiring

Result: 100 engineers → 150 effective engineers = 10% cost increase for 50% capacity increase

ROI Calculation for 100 Engineers

100
Engineers
×
30%
Productivity Gain
=
130
Total Effective Engineers

Cost Savings: $7.5M in avoided hiring costs
Investment: $1.2M in AI tools annually
Net Savings: $6.3M per year

Target Audience

VP Engineering, Senior Director of Engineering, or Platform/Developer Productivity leads who:

  • Manage 30-50+ engineers and are responsible for productivity programs
  • Need to scale capacity without scaling headcount
  • Want to justify AI investments with measurable ROI

30-Day Productivity Gains

💻 PR Velocity Increase +40%
⏱️ Time-to-Merge Reduction -35%
🚨 MTTR Improvement -30%
🎯 Developer Satisfaction +85%

Service Model Forward Deployed Engineers

  • Pure Services – No SaaS deployment, no data sharing with us
  • Forward Deployed Engineers – We design, architect, implement, and deploy solutions
  • Your AI Tool Stack – Cursor, Claude, Copilot, Langflow, BrainTrust, plus optimized workflows

Engagement Model Process, not Price

🔍
Discovery
Week 1
🚀
Pilot
Weeks 2-4
📈
Measure
Week 4
⚡
Scale
Month 2+

Observability & Measurement

You can't fix what you don't measure. We apply distributed systems observability principles to developer productivity:

  • Per-Engineer Metrics – Adoption, usage, and productivity gains at individual level
  • Centralized Dashboards – Real-time visibility into AI tool ROI and performance
  • Traceability & Spans – Track productivity improvements across the entire development lifecycle

🧪 A/B Testing for Quantifiable Gains

We prove ROI through rigorous A/B testing of pilot teams within your organization:

  • Control vs. Treatment Groups – Compare AI-enhanced teams against traditional workflows
  • Statistical Significance – Measure PR velocity, MTTR, and cycle time with confidence intervals
  • Incremental Rollout – Start with 5-10% of engineers, expand based on proven results
  • Executive Dashboards – Real-time metrics that justify continued investment and expansion

Operating Rhythm

  • Direct Line – Slack/phone with all founders
  • Hands-On Delivery – we write code, pair with your ICs, and coach exec stakeholders
  • Transparent Artifacts – weekly demo, KPI dashboard, and next-step backlog

Proven Playbooks We Bring

  • Security review templates for frontier models
  • OKR patterns to tie AI work to business value
  • Pre-built agent workflows (LangChain, Devin-style, etc.) ready to customize

Reference Win (Coupang)

  • 25-person "AI guild" enabled across 2,500 engineers
  • 40% lift in PR throughput, 35% reduction in mean time to merge PRs, and 30% faster incident recovery within 30 days
Read Full Case Study

Why Left Shift AI

👥
Founders Only
🧠
Deep Tech
📊
Metric-First
🚀
Fast Results
🛡️
Enterprise Ready
🎯
ROI Focused
  • Founders Only – no hand-offs to junior consultants
  • Deep Tech + Change Management – we solve both code and culture
  • Metric-First Mindset – value demonstrated in the first sprint

Next Steps

Identify two use cases for internal productivity improvement

Schedule a 60-min scoping call

With Amit, Ziyad, and Hani

Kick off the one-month pilot

Start measuring impact immediately

Ready to Get Started?

Double your engineering capacity without doubling your payroll.
Book a strategy session to calculate your OPEX transformation.

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