The #1 question every engineering leader can't answer

You Spent $200K on Copilot. Can You Prove It Worked?

Stop reporting activity. Start proving value. CausalOps shows which engineering investments actually moved metrics—with statistical confidence intervals and honest uncertainty bounds.

david@causalops.ai • Statistical proof, not guesswork

Specific Investment Questions That Keep You Up at Night

"Is our $200K Copilot investment actually making us faster?"

You see usage stats and acceptance rates. But cycle time 'improvements' could be from new hires, process changes, or seasonal patterns. You need causal proof, not correlation.

"Did our $500K platform team increase overall productivity?"

Developer satisfaction scores look good. Deploy frequency is up. But did we gain net capacity, or just shift work around? Your board wants ROI numbers you can defend.

"Should we renew DataDog at the new price point?"

Incidents are down, MTTR improved. But was it the monitoring investment, the new SRE hire, or just fewer releases? You need attribution that withstands CFO scrutiny.

Why We're Different: The Honesty Advantage

Other tools tell you productivity increased 23%. We tell you it increased 15-25% with moderate confidence—and here are 3 confounders that could explain it.

Other Engineering Intelligence Tools

"Copilot increased developer productivity by 23%"
"Deploy frequency improved 40% after platform investment"
"AI tools saved 2.3 hours per developer per week"
"Your team velocity is 15% above industry average"

Activity metrics ≠ causal proof. Dashboards show what happened, not why.

CausalOps: Statistical Rigor with Honest Uncertainty

"Estimated impact: 15-25% productivity gain, moderate confidence"
"Deploy frequency correlation detected, causation uncertain"
"ROI range: $1.20-$2.80 per dollar invested (68% confidence interval)"
"3 major confounders flagged, 2 controlled statistically"

We're not another engineering dashboard. We're the proof layer.

What CausalOps Reports Actually Look Like

Honest attribution with confidence intervals—the proof your board actually wants to see.

causalops-report.pdf
📊 Copilot ROI Analysis
GitHub Copilot Implementation Assessment
📈 Impact Assessment
Estimated Impact: PR cycle time reduced 15-25%
🎯 Confidence Level
Confidence: ████████░░ MODERATE (68%)
⚠️ Confounders Detected
3 factors identified: new hires, process change, seasonal
💰 ROI Estimate
Recommendation: Renew — $1.2M value vs $480K cost

Built by an Engineering Leader Who Lived This Problem

"I spent a decade as an agile coach helping teams get faster. But I could never PROVE the improvements were from our changes vs. external factors. So I went back to school for causal inference and built the attribution engine I always wished I had."

David Nielsen • Former Engineering Coach • 10 years helping teams optimize • Tired of correlation masquerading as proof

ROI Analysis for Your Biggest Engineering Bets

Start with AI tools, expand to platform and reliability investments.

🤖

AI Tool ROI Analysis

Prove your $200K Copilot investment is working

Statistical analysis of coding velocity, quality impact, and developer satisfaction. Get board-ready ROI estimates with confidence intervals.

Start Analysis

Key Metrics

  • Cycle time reduction
  • Code quality impact
  • Developer satisfaction
  • Usage patterns vs. outcomes
🏗️

Platform Engineering ROI

Justify your developer platform team's headcount

Measure productivity gains from internal tools, infrastructure, and developer experience improvements.

Analysis Includes

  • Developer velocity
  • Tool adoption
  • Support burden
  • Cross-team efficiency
🛡️

Reliability Investment ROI

Show your $500K observability spend reduced incidents

Prove that monitoring, alerting, and SRE investments actually prevent costly outages.

Analysis Includes

  • Incident frequency
  • MTTR reduction
  • Prevention costs
  • Customer impact

How We Generate Statistical Proof

Research-grade causal inference applied to engineering metrics.

1

Establish Baselines

Capture engineering metrics before your investment. Create statistical baselines that account for trends and seasonality.

2

Track Interventions

Log exactly when tools were deployed, teams were formed, or processes changed. Precise timestamps enable before/after analysis.

3

Control for Confounders

Identify and adjust for team changes, product releases, and external factors that could explain metric improvements.

4

Quantify Uncertainty

Generate confidence intervals and flag assumptions. Report honest uncertainty instead of false precision.

Stop Reporting Activity. Start Proving ROI.

Join engineering leaders who are building evidence-based investment decisions. Get statistical proof your engineering investments actually work.