Frequently Asked Questions

Everything you need to know about working with SoftDoes. Can’t find an answer?

How do we migrate from our existing LMS without disrupting training?

SoftDoes automates as much migration as possible via APIs or export/import scripts, ensuring compliance-critical records remain accessible for audits. This approach lets you streamline training operations without risking downtime or data loss.

Can we start with a limited LMS scope and expand later?

Absolutely. This reduces risk and ensures subsequent investment is guided by measurable business outcomes rather than assumptions. It also lets you align training initiatives with evolving business needs before committing to a full-scale rollout.

Who owns the source code and data in a custom LMS built by SoftDoes?

In typical custom development engagements, the client owns the LMS source code (excluding third-party or open-source components under their own licenses) and all learning data stored in the system. This ownership model gives you complete control to continue evolving the platform with SoftDoes or another partner, and avoids being locked into proprietary, per-user SaaS licensing models.

How secure is a custom LMS compared to major SaaS platforms?

Security depends on architecture, implementation, and operations, not on whether a solution is SaaS or custom. SoftDoes follows industry best practices including secure SDLC, regular security testing, and infrastructure hardening. A well-built custom LMS can match or exceed the security posture of major SaaS LMS platforms when these practices are consistently applied.

Is AI-driven adaptive learning worth adding in the first release?

Adaptive learning powered by AI/ML delivers the most value when enough high-quality learning data exists. Many organizations phase it in after launching the core LMS and collecting baseline behavior and performance metrics. A basic LMS with solid data foundations beats a feature-rich platform with no data to fuel it. SoftDoes designs the data model and event tracking from day one so that adaptive algorithms, recommendation engines, difficulty adjustment models, ai driven learning paths, can be layered on later without re-architecture. This way, you get the benefits of personalized learning without delaying your initial LMS implementation.

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