Orbiit AI-Driven Addiction Platform Launches, Expanding Clinical Insight Beyond the Therapy Hour
AI Driven Recovery Support
Daniel Francis, CEO Orbiit Services Inc
Orbie, Your Virtual Peer Recovery Coach
The Substance Use Disorder Foundation
Intelligent tech wearables are an important part of recovery today
Orbiit launches an AI-driven addiction platform giving clinicians objective insight beyond sessions, helping detect disengagement and relapse risk earlier.
AI should never replace the clinician,” Francis said. “It should remove blind spots, reduce guesswork, and allow clinicians to focus on what they do best—building trust and guiding change.”
ATLANTA, GA, UNITED STATES, December 13, 2025 /EINPresswire.com/ -- Orbiit Services Inc. announced today the release of the Orbiit AI-Driven Addiction Treatment Platform, a clinical support ecosystem designed to give addiction treatment professionals objective insight into patient engagement, behavioral patterns, and early indicators of relapse risk—extending visibility well beyond the traditional clinical session.— Daniel Francis, CEO Orbiit Services Inc.
In addiction treatment, clinicians routinely rely on self-report, observation, and affect to assess patient stability. While essential, these methods provide only a point-in-time snapshot and often fail to capture the gradual disengagement and cognitive drift that precede relapse—a process well documented in addiction research.¹²
Orbiit was built to address that gap.
“Clinicians are highly skilled at reading body language, tone, and emotional cues,” said Daniel Francis, CEO of Orbiit Services Inc. “What they haven’t had is access to what happened yesterday, the day before, or during the quiet moments when disengagement begins. Orbiit provides that missing layer of clinical visibility.”
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Objective Insight for Proactive Intervention
Decades of research show that relapse is a process, not an event, often preceded by emotional, cognitive, and behavioral changes that occur outside the clinical hour.¹³ Traditional assessment methods—particularly self-report—are limited by recall bias, minimization, and lack of insight, especially in substance use disorders.⁴
The Orbiit platform uses AI-driven pattern recognition, informed by behavioral science and neuroscience, to identify changes in engagement and cognitive patterns commonly associated with increased relapse risk. The data is derived from the way participants interact with mobile phones.
Rather than relying solely on subjective reporting, Orbiit provides clinicians with objective behavioral signals, allowing earlier, calmer, and more targeted intervention.
Key clinical benefits include:
* Behavioral engagement tracking that highlights meaningful changes over time⁵
* AI-assisted pattern recognition to surface early warning indicators⁶
* Neuroscience-informed insights that contextualize stress, motivation, and cognitive load⁷
* Actionable clinical prompts that support timely intervention
This approach supports a shift from reactive, crisis-driven care to **preventive, data-informed clinical decision-making**.
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Designed to accompany any therapy model
Orbiit was intentionally developed as a clinical augmentation tool, not a replacement for therapeutic judgment or the therapeutic alliance—an approach consistent with best practices in clinical AI deployment.⁸
The platform integrates into existing treatment models and workflows, supporting clinicians across outpatient, community-based, and hybrid care environments.
“AI should never replace the clinician,” Francis said. “It should remove blind spots, reduce guesswork, and allow clinicians to focus on what they do best—building trust and guiding change.”
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Closing the Visibility Gap in Addiction Treatment
Research consistently demonstrates that disengagement and reduced participation often precede relapse by days or weeks.¹⁹ Digital phenotyping and continuous behavioral monitoring have emerged as promising tools for identifying these early shifts before they escalate.¹⁰¹¹
Orbiit translates these scientific advances into **clinically usable insight**, enabling providers to recognize risk earlier and intervene more effectively—while maintaining the clinician as the central decision-maker.
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Availability
The Orbiit AI-Driven Addiction Platform is now available to clinicians and treatment organizations nationwide. There is no cost for clinicians to engage, and onboarding options are available for both individual providers and organizational providers.
About Orbiit Services Inc.
Orbiit Services Inc. develops intelligent recovery technologies that support clinicians treating addiction and related mental health conditions. By integrating AI, neuroscience, and behavioral science, Orbiit delivers clinically relevant insight that extends care beyond the session and into the real-world moments where recovery is most vulnerable.
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Selected References
1. Marlatt, G. A., & Gordon, J. R. (1985). *Relapse Prevention: Maintenance Strategies in the Treatment of Addictive Behaviors*. Guilford Press.
2. Witkiewitz, K., & Marlatt, G. A. (2004). Relapse prevention for alcohol and drug problems. *American Psychologist*, 59(4), 224–235.
3. McKay, J. R. (2009). Treating substance use disorders with adaptive continuing care. *Addiction*, 104(1), 51–59.
4. Del Boca, F. K., & Darkes, J. (2003). The validity of self-reports of alcohol consumption. *Alcohol Research & Health*, 27(1), 27–33.
5. Mohr, D. C. et al. (2017). The behavioral intervention technology model. *Journal of Medical Internet Research*, 19(6).
6. Shatte, A. B. R., Hutchinson, D. M., & Teague, S. J. (2019). Machine learning in mental health. *Psychological Medicine*, 49(9), 1425–1434.
7. Volkow, N. D., Koob, G. F., & McLellan, A. T. (2016). Neurobiologic advances from the brain disease model of addiction. *New England Journal of Medicine*, 374, 363–371.
8. Topol, E. (2019). High-performance medicine: the convergence of human and artificial intelligence. *Nature Medicine*, 25, 44–56.
9. Carroll, K. M., & Weiss, R. D. (2017). The role of behavioral interventions in buprenorphine maintenance treatment. *American Journal of Psychiatry*, 174(8), 738–747.
10. Onnela, J.-P., & Rauch, S. L. (2016). Harnessing smartphone-based digital phenotyping. *Neuropsychopharmacology*, 41, 1691–1696.
11. Insel, T. R. (2017). Digital phenotyping: technology for a new science of behavior. *JAMA*, 318(13), 1215–1216.
Daniel Francis
Substance Use Disorder Foundation
+1 706-531-6286
bert@myorbiit.com
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