A difficult test case
Sales incentive planning combines source-system ambiguity, changing rules, sensitive compensation data, approvals, and calculations that must be reproducible.
New technical whitepaper
AI can challenge SaaS. That does not make enterprise software simple.
We used Buzzy Builder MCP through Codex to build a working sales incentive and planning prototype in days. The result is a practical test of what AI changes—and what it does not.
Is the SaaSpocalypse Real? examines a category where effective-dated plans, commissions, approvals, reconciliation, disputes, and payroll-adjacent controls make superficial demos easy to spot.
~2,900
lines of customer-specific code in the working Buzzy prototype
Approximately 932 lines powered the deterministic mock data service; roughly 2,000 lines covered narrow chart and calculation-waterfall widgets. Conventional custom build 50,000–129,000 lines
Buzzy prototype Approximately 2,900 lines
Directional prototype-equivalent comparison, not a total-cost benchmark. The Buzzy figure counts the customer-specific code created for this scope, not Buzzy’s platform implementation. Real production hardening and operational costs remain.
What the test showed
The hard work became inspectable earlier
Five role-based experiences, 29 managed and remote data tables, live charts, seller statements, approvals, exceptions, reconciliation, and calculation evidence could be reviewed as a connected system—not just admired as static screens.
Most requirements, flows, data, screens, permissions, and workflow state remained visible as managed application artifacts. Custom code was concentrated in the specialised edges.
Inside the paper
A serious category, tested without the theatre
One governed model
Seller, manager, operations, finance, and executive experiences share the same app definition while exposing the right workflow and data for each role.
Bounded custom code
Code remains where it earns its keep: deterministic integrations, specialised visualisations, and calculation execution.
Explicit limits
The prototype does not prove payroll accuracy, production-scale performance, full security hardening, or lower multi-year total cost of ownership.