Summary of Benefits
Scenario: Growing Your Engineering Capacity by 30%
You have 100 engineers and need 30% more capacity. You have two options:
Option 1: Hire 30 Engineers
+30%
Total Cost Increase
30 × $500K = $15M/year
Option 2: Make existing engineers 30% more productive with AI-optimized development systems
+2.5%
Total Cost Increase
100 × $12.5K = $1.25M/year
Key Insight: AI tooling costs only 2.5% of an engineer's salary ($12.5K ÷ $500K) but delivers 30% productivity gains
The Traditional Scaling Problem
- Headcount Scaling is Expensive – $500K+ 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
Before: add 30 engineers (130 total) → payroll up 30%, capacity up 30%—a costly linear gain
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
- 30% Productivity Gains – Each engineer becomes ~1.3x 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 with AI tools → 130 effective engineers—only 2.5% higher spend for 30% more capacity
Example ROI Calculation for 100 Engineers
Cost Savings: $15M in avoided hiring costs
Investment: $1.25M in AI tools annually
Net Savings: $13.75M per year
Target Audience
VP Engineering, Senior Director of Engineering, or Platform/Developer Productivity leads who:
- Manage 30-500+ engineers and are responsible for productivity programs
- Need to scale capacity without scaling headcount
- Want to justify AI investments with measurable ROI
Our Model: Services and Solutions 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
30-Day Productivity Gains
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
Why Left Shift AI
- 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 Founders
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.