A Virtual Coach that Can Improve Performance in the Flow of Work

Cost and timeline vary based on the custom learning objects developed, number of channels, integrations, and guardrails. After a short discovery period/session, we can give a practical estimate and propose a pilot that fits your constraints.

AI usage within the Mobile Coach platform is intentionally flexible and depends on how each customer chooses to design their Virtual Coach. Some organizations use the Mobile Coach platform to build fully deterministic coaches with no AI at all, while others incorporate AI selectively to support natural language dialogue, reflection, or coaching-style interactions. The Mobile Coach platform is designed to support this full spectrum of approaches.

Rather than forcing a single AI-first model, the platform enables customers to decide where AI adds value and where deterministic logic is more appropriate. This allows organizations to balance creativity and flexibility with consistency, risk management, and control, depending on the use case.

No. The Mobile Coach platform is not a large language model. We describe it as Managed-AI.

The Mobile Coach platform is independent of any LLM. The platform orchestrates logic, content, user experience, and integrations without using an LLM. An LLM is integrated only if requested, to offer the learner natural language dialogue preferences when communicating with the platform. This separation allows organizations to benefit from advances in AI models while maintaining control over the content and how those models are used within a governed, purpose-built experience.

Guardrails are implemented through a combination of human-designed logic, system instructions, and deterministic rules that define what the Virtual Coach can and cannot do. These guardrails help to govern the tone, scope, content boundaries, escalation behavior, and when AI should or should not be used.

Because these controls are explicitly designed and maintained, the Virtual Coaches’ behavior is not left to AI interpretation alone. This ensures that the experience remains aligned with organizational expectations, program intent, and acceptable use policies.

Only the information required to generate a specific response is shared with an AI model at runtime. The Mobile Coach platform has full control over how the context window is constructed, including which instructions, content, and user inputs are packaged and sent to an LLM via API.

The Mobile Coach platform uses AI providers whose API terms and conditions specify that data sent via API is not stored or used for model training. This ensures that customer data is processed transiently for response generation and not retained by the AI provider.

User data is stored within the Mobile Coach Platform using secure, enterprise-grade infrastructure and access controls. Data is protected through role-based permissions, authentication mechanisms, and operational safeguards designed to prevent unauthorized access or misuse.

When AI is involved, only the minimum information required to generate a response is sent to an AI model via API, processed transiently, and not retained by the AI provider. The Mobile Coach platform maintains control over data handling and applies security practices aligned with its ISO/IEC 27001–certified Information Security Management System.

Privacy is handled through configurable access controls, role-based permissions, and data-handling practices aligned with enterprise expectations. Organizations retain ownership of their data and control how it is collected, processed, and accessed within the Virtual Coach experience.

The Mobile Coach platform is designed to minimize data exposure by sharing only what is necessary for functionality and by allowing customers to define authentication, visibility, and administrative access policies appropriate to their use case.

Yes, but only when explicitly designed to do so.

Proactive engagement—such as reminders, nudges, reflections, or check-ins—is configured intentionally within the platform. These interactions are triggered by defined conditions or schedules, not autonomously initiated by the AI, ensuring that proactive messaging aligns with program goals and organizational intent.

The Mobile Coach Platform includes built-in reporting dashboards that allow administrators to monitor usage, engagement trends, and overall system performance over time. These dashboards help organizations understand how their Virtual Coach is being used, where users may be encountering friction, and how effectively the experience is supporting program goals.

In addition, the platform maintains User Logs, which serve as an audit record of Virtual Coach interactions. These logs make it possible to trace how responses were generated, including the underlying logic paths, deterministic rules, and AI-generated components involved in each interaction. This supports governance, quality assurance, and ongoing refinement by providing transparency into both content origination and system behavior.