Business Case for Obvyr

The Core Question

How much does unreliable testing cost your team?

Most engineering teams can't answer this question because they've never measured it. But the costs are real:

• Developers debugging flaky tests that aren't actually broken
• Production incidents from environmental differences tests didn't catch
• CI/CD compute costs for tests that never find bugs
• Manual code review bottlenecks when AI accelerates development

Obvyr helps you measure these costs, then systematically address them.

The Four Testing Cost Categories

1. Flaky Test Debugging Costs

The Problem:

• Developers waste time investigating test failures that aren't real bugs
• Teams lose confidence in test results and start ignoring failures
• Real bugs slip through because failures are assumed to be "just flaky"

Questions to Ask Your Team:

• How many hours per week do developers spend debugging flaky tests?
• How often do you re-run CI because tests failed inconsistently?
• Have you shipped bugs because the team ignored test failures?
• What's your team's loaded hourly cost for senior developers?

How Obvyr Addresses This:

• Pattern detection identifies which tests are truly flaky vs. genuinely broken
• Root cause analysis reveals why tests fail (environment, timing, infrastructure)
• Evidence-based confidence lets teams trust test results again

Value Framework:

Hours/week debugging flaky tests × Developers × Hourly cost × 52 weeks = Annual cost

Calculate your actual number based on your team's reality.

2. Environment Divergence Incident Costs

The Problem:

• Tests pass in CI but production breaks due to environmental differences
• Configuration, dependencies, or infrastructure differs between environments
• Each incident requires emergency debugging, potential rollbacks, and lost trust

Questions to Ask Your Team:

• How many production incidents per month are caused by environmental differences?
• What's the average cost of a production incident (downtime + debugging + customer impact)?
• How often do deployments get rolled back due to environment-related issues?
• How much time do you spend trying to keep local, CI, and production environments aligned?

How Obvyr Addresses This:

• Environment comparison reveals systematic differences between local, CI, and production
• Proactive detection identifies drift before deployment
• Evidence-based deployment confidence reduces rollback rates

Value Framework:

Incidents per month × Average incident cost × 12 months = Annual cost

Estimate based on your actual incident history and typical impact.

3. CI/CD Waste Costs

The Problem:

• Large test suites accumulate over time without evaluation
• Many tests never fail or catch bugs but still slow down CI
• Teams pay for compute and wait time without knowing which tests provide value

Questions to Ask Your Team:

• How long is your CI pipeline? How much of that is test execution?
• What's your monthly CI/CD compute cost?
• Do you know which tests actually catch bugs vs. which have never failed?
• How many deployments are delayed by slow CI pipelines?

How Obvyr Addresses This:

• Test value assessment identifies which tests catch bugs vs. which ones don't
• Evidence-based optimisation lets you safely remove low-value tests
• Pattern analysis reveals tests that slow down CI without providing value

Value Framework:

CI compute cost per month × Percentage of low-value tests = Potential savings
Developer time waiting for CI × Hourly cost = Productivity cost

Measure your actual CI costs and developer wait time.

4. AI Development Quality Gap Costs

The Problem:

• AI tools accelerate code generation 10x
• Manual code review and test validation remain 1x
• Quality assurance becomes the bottleneck to AI productivity

Questions to Ask Your Team:

• Are you using AI coding tools (GitHub Copilot, Claude Code, ChatGPT)?
• How many hours per week do senior developers spend reviewing AI-generated code?
• Have you had production incidents from AI-generated code that passed tests?
• Are you restricting AI tool usage due to quality concerns?

How Obvyr Addresses This:

• AI test pattern analysis identifies quality gaps in AI-generated tests
• Automated validation at AI speeds, not manual review speeds
• Evidence-based AI confidence enables full AI adoption with quality assurance

Value Framework:

Hours/week reviewing AI code × Hourly cost × 52 weeks = Manual review cost
Lost AI productivity from restricted usage = Opportunity cost
AI-related incidents × Incident cost = Quality gap cost

Calculate based on your actual AI adoption and review processes.

5. Compliance and Audit Cost Reduction

The Problem:

• Regulatory frameworks require documented evidence of testing practices
• Customer security audits demand proof of systematic quality assurance
• Manual documentation of testing activities is time-consuming and error-prone
• Audit preparation diverts engineering resources from development

Questions to Ask Your Team:

• How many hours per year do you spend documenting testing practices for audits?
• Do you face regulatory requirements that require proof of testing (financial services, healthcare, government)?
• How often do enterprise customers request evidence of your testing practices?
• What's the cost of failed audits or delayed deals due to insufficient testing documentation?

How Obvyr Addresses This:

• Automated audit trail of all test executions across your organisation
• Comprehensive evidence collection without manual documentation burden
• Immutable records of who ran what tests, when, where, and with what results
• Historical proof of testing practices and quality improvements

Value Framework:

Hours/year on audit documentation × Hourly cost = Manual documentation cost
Failed audit costs or delayed deals = Compliance risk cost
Annual Value = Documentation time saved + Risk mitigation

Measure based on your actual compliance obligations and audit frequency.

Calculating Your Team's Testing Costs

Step 1: Measure Current State

Work with your team to estimate:

1. Flaky test debugging: Hours per week per developer
2. Environment incidents: Count per month, average cost per incident
3. CI/CD costs: Monthly compute spend, pipeline duration
4. AI review burden: Hours per week if using AI tools
5. Compliance documentation: Hours per year on audit preparation and testing documentation

Step 2: Estimate Annual Impact

Use the value frameworks above with your actual numbers:

• Multiply weekly costs by 52
• Multiply monthly costs by 12
• Use your team's actual loaded costs per developer hour
• Include both direct costs (time, compute) and indirect costs (incidents, delays)

Step 3: Identify Highest-Impact Areas

Not every team has every problem. Focus on what matters most:

• Primarily flaky test issues? Obvyr's pattern detection provides immediate value
• Environment drift incidents? Environment comparison is your priority
• Slow, expensive CI? Test value assessment is key
• AI adoption challenges? AI test quality validation matters most
• Regulatory or audit requirements? Automated compliance evidence collection is critical

What Obvyr Costs

Obvyr pricing is based on team size. Contact us for specific pricing for your organisation.

Implementation costs:

• Setup time: ~10 minutes for first project/agent
• Learning curve: Minimal (wraps existing test commands)
• Infrastructure changes: None required
• Process changes: None required (tests run the same way)

ROI Evaluation Framework

Questions to Determine Fit

Do you have flaky tests?

• If yes: How much time do you spend investigating them?
• Obvyr value: Time savings + restored test confidence

Do you have environment-related production incidents?

• If yes: How many per month? What's the typical cost?
• Obvyr value: Incident prevention + deployment confidence

Is your CI pipeline slow or expensive?

• If yes: How long? How much does it cost? Which tests provide value?
• Obvyr value: Compute savings + developer productivity

Are you using AI coding tools?

• If yes: How do you ensure AI-generated test quality?
• Obvyr value: Automated validation + full AI adoption

Do you have regulatory or customer audit requirements?

• If yes: How much time do you spend documenting testing practices?
• Obvyr value: Automated compliance evidence + audit readiness

ROI Calculation Approach

Annual Value = Sum of:
  - Flaky test debugging time saved
  - Environment incidents prevented
  - CI/CD costs reduced
  - AI review time automated
  - Developer productivity gained
  - Compliance documentation time saved

Annual Cost = Obvyr platform cost + setup time

Net Value = Annual Value - Annual Cost
ROI = (Net Value / Annual Cost) × 100%

Use your actual numbers. Every team's situation is different.

Value Realisation Timeline

Week 1: Initial Insights

After 50-100 test executions:

• Identify obviously flaky tests
• Initial environment comparison data
• First pattern insights

Early wins: Fix 2-3 clearly problematic tests, prevent first environment issue

Month 1: Comprehensive Analysis

After 500-1,000 test executions:

• Comprehensive flaky test analysis with root causes
• Clear environment drift patterns
• Initial test value assessment
• AI test quality baseline (if using AI)

Measurable impact: Quantify time saved, incidents prevented, optimisation opportunities identified

Quarter 1: Full Value

Sustained data collection enables:

• Systematic flaky test resolution
• Proactive environment drift detection
• Evidence-based CI optimisation
• Automated AI quality validation

Sustained value: Ongoing cost reduction and quality improvement

Comparison to Alternatives

Alternative 1: Hire More QA Engineers

Approach: Add manual QA capacity to investigate test issues

Costs:

• 2-3 senior QA engineers: $300k-$450k/year
• Ongoing management and coordination overhead

Limitations:

• Manual processes don't scale
• Reactive (investigate after problems occur)
• Can't keep up with AI development speeds

Obvyr advantage: Automated pattern detection at scale, proactive identification, scales with AI velocity

Alternative 2: Accept Current Costs

Approach: Continue with current testing practices

Costs:

• All testing problems continue at current levels
• Costs compound as team and codebase grow
• AI adoption limited by quality concerns

Hidden costs:

• Technical debt accumulation
• Team morale from unreliable testing
• Competitive disadvantage from slower deployment velocity

Obvyr advantage: Systematically address root causes, scale quality with growth

Alternative 3: Build Internal Solution

Approach: Develop custom test observability tooling

Costs:

• 2 engineers × 6 months development: $150k-$200k
• Ongoing maintenance: 0.5 engineer/year: $80k-$100k/year
• Infrastructure costs: $20k-$30k/year

Risks:

• 6-month delay before any value
• Opportunity cost during development period
• Uncertain effectiveness
• Ongoing maintenance burden
• Feature gaps vs. dedicated solution

Obvyr advantage: Immediate value, proven effectiveness, continuous improvements, no maintenance burden

Key Value Drivers

For Different Team Sizes

Small teams (10-20 developers):

• Primary value: Flaky test resolution, environment drift prevention
• Quick wins from comprehensive data collection
• Higher impact per developer hour saved

Mid-size teams (30-50 developers):

• All five value categories apply
• CI optimisation becomes significant
• AI quality validation increasingly important

Large teams (100+ developers):

• Massive scale benefits from pattern detection
• CI cost savings multiply with team size
• AI adoption enablement critical for competitive advantage

For Different Industries

High-reliability industries (finance, healthcare):

• Environment drift prevention most critical
• Incident costs particularly high
• Deployment confidence paramount
• Compliance documentation requirements significant

Fast-moving consumer (SaaS, e-commerce):

• Deployment velocity from CI optimisation key
• Flaky test time savings impact feature delivery
• AI adoption enablement competitive advantage
• Customer audit readiness for enterprise deals

AI-first companies:

• AI quality validation primary value driver
• Enabling full AI productivity critical
• Automated testing at AI speeds essential

Regulated industries (government, financial services):

• Automated audit trail primary value
• Compliance evidence collection critical
• Risk mitigation through systematic documentation
• Change control and quality assurance records

Making the Decision

Start with Measurement

Before committing to Obvyr (or any solution), measure your actual costs:

1. Track flaky test debugging time for one week
2. Count environment-related incidents for one month
3. Calculate CI/CD costs and identify low-value tests
4. Measure AI code review time if applicable

This data is valuable regardless of whether you adopt Obvyr.

Pilot Approach

Test Obvyr's value with minimal commitment:

1. Week 1: One project, one agent, one test type
2. Week 2: Measure insights gained and quick wins
3. Week 3: Calculate actual time/cost savings
4. Month 1: Decide based on measured value, not assumptions

Decision Criteria

Obvyr makes sense if:

• ✅ You have measurable flaky test problems
• ✅ Environment-related incidents occur regularly
• ✅ CI/CD costs or time are significant
• ✅ You're adopting AI tools and need quality validation
• ✅ You value evidence-based decision making

Obvyr may not be a fit if:

• ❌ Your test suite is small and entirely reliable
• ❌ You have no environment divergence issues
• ❌ CI/CD is fast and inexpensive
• ❌ You're not using AI coding tools
• ❌ Testing problems aren't costing you meaningful time or money

Next Steps

1. Measure Your Current Costs

Use the frameworks above to estimate your actual testing costs. This is valuable whether or not you adopt Obvyr.

2. Understand the Solution

• Why Obvyr? - Complete value proposition
• Problems Solved - Detailed problem scenarios
• AI-Era Testing - AI development relevance

3. Try It

• Get Started - 10-minute setup, start collecting evidence
• Pilot program: Start small, measure value, expand based on results

4. Calculate Your ROI

Once you have data:

• Measured time savings from flaky test resolution
• Incidents prevented through environment drift detection
• CI costs reduced through test value assessment
• AI review time automated

Use actual results, not assumptions.

Contact Us

Have questions about Obvyr's fit for your team?

• Schedule a consultation: Discuss your specific testing challenges
• Request a demo: See Obvyr in action with your use case
• Pilot program: Try Obvyr with one project before full commitment

We help you measure your actual costs before discussing pricing. The goal is evidence-based decision making—for testing and for adopting Obvyr.

Start with Measurement

Whether or not you adopt Obvyr, measuring your testing costs provides valuable insights. Get started and see what the data reveals about your test quality.