Software and SaaS development
Digital products that solve, scale and evolve.
Insights
Guides, analysis and lessons on product, artificial intelligence, growth, communications and cloud.
Editorial themes
Digital products that solve, scale and evolve.
AI systems for real-world processes.
Demand, catalogue, partners and conversion.
Every conversation in the right channel.
Available, observable and ready services.
Set rules for detecting duplicate customers, deciding which records to merge, and protecting useful data through confidence levels, human review, and traceability.
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Learn how to assess whether you can replace a SaaS solution without losing data or disrupting workflows by reviewing exports, dependencies, exit tests, and responsibilities.
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Compare shared databases, separate schemas, and dedicated databases to choose the data isolation model that fits your SaaS risks, customers, and team.
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Treat customer offboarding as a verifiable workflow. Revoke access, handle data and integrations, and confirm that no active dependencies remain.
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Shadow mode compares an automated decision with real operations before activation, helping teams identify risks and make evidence-based choices.
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A spreadsheet can support useful processes for years. This framework helps identify when its flexibility becomes an operational risk.
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Define users, cases, exceptions, and dependencies to run a representative, safe digital pilot that can guide the next decision.
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A practical framework for distinguishing a product gap from a process, data, permissions, user experience, or adoption issue before prioritizing development.
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A practical framework for assigning autonomy to automations based on impact, reversibility, uncertainty, context, and the ability to correct errors.
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Learn how to prioritize errors in digital processes using operational criteria to block, alert, or correct them without unnecessarily slowing the business.
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A practical framework for defining data retention, archiving, anonymization, and deletion without disrupting operations or increasing risk.
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A practical framework for deciding which internal processes require an SLA and which need reviewable operational targets, without inflating costs.
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