Software delivery, end to end.
Run six months. Compare what ships, what stays useful, and how customers feel.
An illustrative model, not a forecast.All four run together. Choose one to highlight its results and inspect its workflow.
Customer satisfaction
Useful delivery can lift satisfaction; defects and delays can reverse the gain. Higher is better.
Day 0out of 100
Press Run comparison to watch six months unfold. Every result follows the same day.
Guided AIat day 0
- Quality
- Not yet released
Working and fit for purpose
- Satisfaction
- 50 / 100
Customer experience
- Unmet needs
- 24
Customer backlog
Inside Guided AI’s workflow
The working loop
Guided AIDay 0
Faster builds, human direction and selective approval, with independent checks.
01Backlog
0 queued
Demand arrives in bursts
02Design
0 queued
Human direction
03Build
0 queued
4× build pace
04PR review
0 queued
Selective approval
05Verify
0 queued
Independent checks
06In use
0 features
Working features reach users
- Released features
- 0
- Useful in use
- 0
- Escaped defects
- 0
- Still affecting users
- 0
- Off-target releases
- 0
- Repairs queued
- 0
- Delivery queue
- 0
- Repair cycles
- 0
- Average lead time
- Pending
- Defect-days in use
- 0
Delivery queue counts idle jobs. Unmet customer needs also include active work and features released broken or off target.
How the model works
Same customer needs and request-level random seed for every approach. All rates and scores are illustrative assumptions, not measured results.
Demand never stands still
Every team starts with 24 customer needs. New requests arrive in weekly bursts, with demand growing 25% over six months. Around 15% of features take 2.5× longer. Unmet needs count requested features that are unreleased, broken or off target; a good process still has a backlog.
Speed and finite capacity
Baseline capacity: two design slots, three build slots, three verification slots and one PR reviewer. Build time averages two days per ordinary feature, divided by AI speed. Design and verification take half a day with human direction and independent checks, or a quarter-day without. PR review takes half a day. YOLO has 3× parallel automated capacity and adds two speculative proposals per customer need; it prioritizes new features over repairs.
Defects and customer experience
The initial backlog is relatively well understood. Ambiguity rises toward 35% over 60 days; extra scope grows less certain as time passes. Human direction resolves 85% of unclear goals. Initial defect risk is 25%, multiplied by 1.25 for consequential features (30% of work). Review detects 45% of defects; independent checks detect 90%, basic checks 5%. Repairs carry 45% of the original risk and use the same pipeline.
Late failures emerge 20–60 days after release. Their chance is defect risk × parallel pressure, reduced by a 0.05 guard for independent checks or 0.55 for every PR reviewed, capped at 90%. Defects stay in use until repaired; feedback takes four days, with up to three repair cycles. Product quality weights consequential features 3×. Useful product can fall when regressions appear.
Satisfaction starts at 50 with an unmet backlog. Its target combines 70% product quality and 30% fulfilled customer needs, minus up to 20 points for overdue unreleased requests after seven days. It responds with an eight-day lag. Early useful delivery can lift satisfaction before delayed failures erode it. No preset has a fixed score or penalty.
This model omits dependencies, team learning, review depth and security incidents. Related reading: the limits of automation and DORA’s 2025 research (opens in a new tab). These sources inform the questions, not the numerical rates.