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Enter your current monthly software cost breakdown. See what the same workload would cost under AI-MSL, with at least 3x delivery speed. No calendar booking, no email gate until you want the detailed report.
Four sliders, two pickers · Under 60 seconds
Verified outcomes from active customer engagements. The calculator estimates. These engagements delivered.
From two to four week sprint cycles down to two to three day delivery, on a HIPAA-compliant AWS production system. Engineering team scaled from twelve to three on the same workload, audit passed without findings post-transition.
Production AWS BI pipeline transitioned from traditional managed development to governed AI-MSL execution. Six feature groups, sixteen tasks, SOC-1 reporting requirements held throughout.
AI-MSL ran end-to-end across three interconnected advertising-technology codebases simultaneously, with cross-product dependencies surfaced during evaluation rather than discovered in production.
AI tools shave hours off coding, but unmanaged acceleration creates new failure modes faster than it removes the old ones. Three patterns we see almost everywhere.
Every new feature costs a new hire, a new manager, and another round of onboarding. The roadmap moves at the speed of your hiring pipeline, not your product.
Generated code lands faster than it can be reviewed, documented, or aligned to the architecture. Quality erosion is silent until production catches it.
Sprint cycles depend on capacity, not process. Every quarter is a negotiation between roadmap intent and what the team can ship.
Three governed phases. AI assists at every stage. Execution is bounded by expert review, traceability gates, and lifecycle accountability.
Requirements, architecture, and acceptance criteria are documented before code generation. Nothing ambiguous propagates into the implementation.
AI executes inside structured gates with expert review at each transition. Speed scales, but discipline scales with it.
Every change is auditable, every decision is documented, every regression is surfaced before it reaches users.
Four outcomes a governed AI-Managed Software Lifecycle engagement is designed to produce. The calculator above gives you a directional range for your team. The full report tells you what an engagement at your size and stage typically looks like.
Software evolution stops being throttled by hiring cycles. The lifecycle, not headcount, sets the cadence.
Outcome-priced engagements with timelines you can defend in front of a board. Roadmap meets reality on schedule.
Typical directional impact: 30 to 70 percent reduction in the lifecycle cost of maintaining and extending revenue-critical systems.
Speed without architectural drift, security blind spots, or fragmented system knowledge. Discipline scales with output.
The calculator gives you a defensible range in 30 seconds. The full report walks through the math on your inputs and what a v1 engagement at your size and stage typically looks like.