TAD Delivery Intelligence
A structured diagnostic product that tells engineering leaders what is breaking in their delivery system, whether the data can be trusted, and what to do next. Not a dashboard. Not a coach. Not another framework. A diagnosis.
What TAD Analyzes
Every TAD diagnostic evaluates your delivery system across a defined set of dimensions. The depth and breadth depend on your tier.
Is work moving through your system without unnecessary stalls, handoffs, or wait states?
Are teams taking on the right amount of work, or is everything in progress and nothing finishing?
Can you reliably forecast when work will be done, or are estimates consistently wrong?
Is work entering your system well-defined, properly sized, and ready to build?
Are teams structured to deliver independently, or are dependencies creating hidden bottlenecks? (Plus and Pro)
Do the metrics your org tracks actually help you make decisions? (Plus and Pro)
Before you trust your metrics, you need to trust your data. TAD evaluates data confidence across four factors: completeness, consistency, workflow integrity, and metric reliability. If your data is unreliable, TAD tells you directly and qualifies its findings accordingly.
Your SLE is either real, or it’s a guess. TAD evaluates SLE integrity across 5 factors: existence, statistical basis, currency, alignment with actual data, and operational usage. A made-up SLE is worse than no SLE — it creates false confidence in a prediction that has no basis.
TAD’s scoring engine uses a weighted diagnostic model refined through years of hands-on delivery transformation work. Each dimension weight reflects its observed downstream impact on system-level delivery performance. The engine cross-validates self-reported data against declared pain points and optional CSV exports from Jira or Linear, then pattern-matches against 9 structural bottleneck types to generate root-cause findings with specific recommended actions.
The model was not designed from a textbook. It was refined by diagnosing real engineering delivery systems — and observing which failure modes have the highest compounding impact across teams.
Tier Comparison
Three tiers. Clear differentiation.
| Capability | Lite | Plus | Pro |
|---|---|---|---|
| Price | Free | $497 | $1,997 |
| Teams | 1 | Up to 3 | 4+ / dept |
| Dimensions Scored | 4 | 6 | 6 + 2 layers |
| Data Confidence | Signal | Assessment | Full layer |
| SLE Integrity | Signal | Validation | Full analysis |
| Cross-Team Comparison | — | If 2–3 teams | Full matrix |
| Root Cause Analysis | — | — | Systemic |
| Department Intelligence | — | — | Full rollup |
| Diagnostic Findings | 1–2 flags | 3–5 prioritized | Comprehensive |
| Report Format | On-screen | Executive PDF | |
| Action Plan | — | Signals | Sequenced plan |
| Debrief | — | — | 1-hour call |
| Rescore | — | — | 90-day included |
Maturity Bands
Four bands. Each has specific structural characteristics and recommended interventions.
Strong discipline across all dimensions. Optimization and measurement refinement are the priorities.
Delivery works but has exploitable weaknesses. Targeted fixes will yield measurable improvement in 30–60 days.
Structural problems actively degrading throughput. System design issues, not people issues.
Reliable delivery is structurally impossible. Requires a reset — not a tune-up.
Operator-built delivery intelligence · Not a dashboard, not a coach, not a survey