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Investing in a business software company?
Talk to a partner who has diligenced companies like it before you commit capital.
Can the product win the enterprise? We find out before you invest.
Diligence of cross-industry business software: HR, finance, sales, marketing, and collaboration.
Horizontal SaaS companies compete in crowded markets, and growth plans usually depend on moving upmarket. That puts pressure on multi-tenancy, security, and the ability to ship a broader product fast. We test whether the architecture and team can deliver the enterprise readiness the plan assumes, at a gross margin that holds.
What we find most often
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Inconsistent tenant isolation.
A mix of separate cloud accounts for some customers and shared services for others, with no standard rule, which complicates compliance reviews, onboarding, and support. The scalable pattern is a Z-axis split on the AKF Scale Cube: tenants grouped into identical pods, with the largest customers isolated by design rather than by exception.
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Enterprise expectations the platform can’t yet meet.
Single-region hosting, untested failover, and gaps in audit logging or access controls that surface in enterprise security reviews.
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Engineering time spent on undifferentiated builds.
Custom components where a commercial product would do, absorbing capacity that should go to the product customers pay for.
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Features shipped without outcome metrics.
Usage is tracked but not tied to adoption, retention, or revenue, so the roadmap can’t show which investments worked.
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Cloud and data costs that grow faster than revenue.
Data that is never archived, infrastructure cost not tied to usage, and an application scaled as one unit instead of splitting high-load functions into services that scale independently (a Y-axis split).
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Informal planning and concentrated knowledge.
High velocity today that depends on a few people and undocumented flows, with no delivery forecasting. Our technical debt balance sheet shows how much capacity the debt is really costing.
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AI features under competitive pressure.
Thin AI add-ons on common use cases that are easy for competitors to match and costly to run.