Technical Founders
Connect engineering decisions to business outcomes. Track ROI, revenue impact, and strategic metrics.
Customer impact radius
For any given service or function, know which customers and revenue streams depend on it
Feature delivery date estimation
Trace from issue creation through historical PR-to-deploy timelines to give realistic ship dates
Feature impact attribution
Trace from business metrics (conversion, engagement) back to specific feature deployments
Architecture migration sequencing
When planning a migration, suggest optimal ordering based on dependency graph and risk
Build vs. buy decision support
When considering building a feature, surface historical data on how similar internal builds performed vs. third-party alternatives
Due diligence automation
For fundraising or M&A, auto-generate technical health reports from the knowledge graph
Acquisition integration planning
When acquiring a company, model how their stack would integrate based on technology overlap and dependency compatibility
Communication-code gap detection
Identify when Slack/meeting discussions about architecture diverge from what's actually being built
Effort-outcome mismatch detection
Flag areas where engineering effort is high but measurable output (deploys, issue closure, metric impact) is low
Revenue impact estimation
Given a service degradation, estimate revenue loss per minute based on traffic patterns and conversion funnels
Customer churn causal analysis
Trace from a churn event backward through support tickets, incidents, feature gaps, and competitor signals
Revenue per engineering hour
Trace from engineering time investment through features shipped to revenue impact
Vendor replacement analysis
When a third-party service underperforms, suggest alternatives based on integration complexity and feature parity
Competitive response speed benchmarking
Measure time from competitor feature launch (detected via monitoring) to internal implementation
Regulatory compliance mapping
Trace data flows and access patterns against regulatory requirements (SOC2, GDPR) and identify gaps
Startup health dashboard for investors
Real-time composite score combining velocity, reliability, cost efficiency, team health, and growth metrics
Implicit architecture emergence
Detect when a new architectural pattern is emerging organically across teams without explicit design
Churn risk from incident exposure
Correlate customer-facing incidents with churn data to predict which customers are at risk after an outage
Cost spike attribution
Trace unexpected cost increases to specific deployments, traffic patterns, or infrastructure changes
Optimal deploy window recommendation
Based on traffic patterns, team availability, and historical incident timing, suggest when to ship
Engineering ROI by initiative
Trace from strategic initiatives through epics, issues, PRs, deploys to business metric impact
Platform reliability as sales asset
Generate reliability reports for enterprise sales based on actual uptime, incident response times, and resolution patterns
Product-market fit signal detection
Correlate feature usage telemetry, support tickets, and churn data to identify PMF signals per feature
Seasonal reliability patterns
Identify recurring reliability issues tied to time patterns (end of month billing runs, marketing campaigns)
Cross-customer issue correlation
Identify when seemingly unrelated customer complaints share a technical root cause