Cut Engineering Costs by 30–50% With AI
Your engineering costs are growing faster than your revenue. AI systems can replace manual engineering work, automate operational overhead, and let you do more with fewer people—without sacrificing quality or velocity.
Leaders focused on engineering efficiency
CTOs
Under pressure to cut engineering spend while maintaining delivery
Founders
Whose burn rate is dominated by engineering salaries
VPs of Engineering
Looking to increase output without increasing headcount
COOs
Who see engineering as a cost center that needs optimization
Private Equity
Portfolio companies focused on operational efficiency
Why engineering costs spiral
Engineering teams grow because work expands, not because output increases:
- Maintenance burden compounds—every feature shipped adds maintenance load
- Operational work multiplies—deployments, monitoring, incident response eat engineering time
- Manual processes persist—data pipelines, testing, documentation stay manual "for now"
- Specialization fragments—you hire DevOps, then SRE, then platform engineering, then ML ops
- Coordination overhead scales—more people means more meetings, more alignment, more delays
A 20-person team often delivers less than a 10-person team with better automation.
AI systems that replace manual engineering work
Automated Operations
- Infrastructure monitoring and self-healing
- Incident detection and initial response automation
- Log analysis and anomaly detection
- Deployment automation and rollback systems
Development Acceleration
- Code generation for boilerplate and patterns
- Automated testing and test generation
- Documentation generation and maintenance
- Code review assistance and standards enforcement
Data Pipeline Automation
- ETL workflow automation
- Data quality monitoring and correction
- Schema change management
- Report generation and distribution
Process Automation
- Manual QA process automation
- Release management automation
- Security scanning and compliance checks
- Technical debt identification and prioritization
From audit to cost reduction
Discover
Week 1–2Audit engineering workflows, identify high-labor-cost activities, map automation opportunities with realistic cost savings.
Build
Week 3–10Build automation systems in priority order, starting with highest-impact, lowest-risk opportunities. Weekly progress reviews.
Ship
Week 10–12Production deployment with documentation and training. Ensure your team can maintain and extend the automations.
Scale
Week OngoingOptional retainer for expanding automation coverage and continuous optimization.
Example savings by role
| Role/Function | Typical Cost | AI Replacement Cost | Annual Savings |
|---|---|---|---|
| Junior QA (manual testing) | $80K | $15K (AI testing) | $65K |
| DevOps (routine operations) | $140K | $30K (automation) | $110K |
| Data engineer (pipeline maintenance) | $150K | $35K (automated pipelines) | $115K |
| Documentation/technical writing | $90K | $20K (AI generation) | $70K |
Example: 15-person engineering team
- Current annual cost: ~$2.2M
- AI automation of 3 FTE-equivalent work: $100K
- Net savings: $350K–500K annually (15–23%)
- Remaining team focuses on high-value work
What cost reduction looks like
reduction in engineering operational overhead
equivalent work automated per engagement
faster incident response with AI monitoring
ROI timeline for most automation initiatives
Do More With Less
Your engineering budget doesn't have to grow linearly with your ambitions. Let's identify which work should be automated and what that saves you annually.
Get a Cost Reduction AssessmentWe'll analyze your engineering workflows and deliver a prioritized automation roadmap with realistic savings estimates.