As doctors spend nearly half their workday on paperwork, Augnito’s voice-AI promises to reclaim their time and improve patient care. Rustom Lawyer, Co-Founder and CEO, Augnito, shares how gaps in medical documentation and his two decades with the NHS shaped a platform that captures, codes, and integrates clinical conversations in real time.
Let’s start with the big picture. What specific gaps in the medical documentation ecosystem led to the creation of Augnito? How does your background with the NHS influence your current vision?
The creation of Augnito emerged from a profound understanding of healthcare documentation inefficiencies that we witnessed firsthand during two decades of experience in the sector. When we co-founded Scribetech in the UK at the tender age of 19, we became pioneers in clinical transcription services for the NHS, working with prestigious institutions like Chelsea and Westminster Hospital and University Hospital Birmingham.
The NHS experience profoundly shapes Augnito’s vision today. Working with the world’s largest single healthcare organisation taught us the critical importance of data security, interoperability, and scalable solutions that could function across diverse healthcare environments. The NHS experience instilled in us a deep appreciation for systems that must serve millions while maintaining the highest standards of accuracy and compliance. This deeply influences Augnito’s mission till date: to liberate healthcare professionals from documentation burdens through intuitive, accurate, and interoperable technology, while maintaining the highest standards of performance, security and compliance. This extensive experience revealed a fundamental truth: doctors worldwide spend approximately 50 per cent of their working hours on record-keeping, while nurses spend around 30 per cent on similar tasks.
According to recent developments, Augnito’s flagship product Omni was launched in May 2025. Can
you walk us through what sets Omni apart in the crowded voice-AI landscape, especially in clinical settings?
Omni is a leap beyond traditional speech-to-text tools. It leverages Ambient Clinical Intelligence and Generative AI to capture natural clinical conversations in real time, across several languages, without requiring clinicians to change their communication style.
Doctors using Omni already report saving over two hours daily, with a 60 per cent reduction in documentation time—a crucial advantage in combating clinician burnout. Its flexibility also allows for both cloud and on-premises deployment, seamless integration with any EMR, and robust compliance with global data privacy standards.
Omni supports multiple languages and dialects. How critical is linguistic inclusivity in healthcare AI adoption—especially across multilingual geographies like India and the Middle East?
Linguistic inclusivity is absolutely critical. In regions like India and the Middle East, where clinicians and patients converse in a multitude of languages and dialects, the ability to document accurately in the
language of care is essential for both clinical precision and patient trust. Omni’s support for more than 20 languages—including regional Indian languages and Arabic dialects—ensures documentation accuracy
and accessibility, bridging gaps in care delivery and making advanced healthcare technology truly universal. This inclusivity also empowers rural and underserved populations, reducing disparities in access
and quality of care.
From your recent partnership with Almoosa Health in Saudi Arabia, what lessons can Indian healthcare systems learn when it comes to scaling AI across networks?
Our partnership with Almoosa Health demonstrates the importance of cultural and linguistic alignment in AI deployment. The key lesson for Indian healthcare systems is that successful AI integration requires three critical elements: technological sophistication, cultural sensitivity, and proven clinical efficacy.
The phased rollout approach we have implemented there—starting with pilot testing, then departmental expansion, and finally a proposed hospital-wide deployment—will also ensure minimal disruption, while maximising adoption. Indian healthcare systems can adopt this methodology, particularly the emphasis on continuous clinician feedback and real-time support during integration phases.
Your partnership with Apollo Hospitals has shown clear ROI and time savings. What were some of the key challenges in moving from pilot to full scale integration in such a large hospital network?
Transitioning from pilot to full-scale integration at Apollo Hospitals involved addressing several challenges: ensuring interoperability with diverse legacy systems, managing change across a
large and varied clinician base, and maintaining data security across multiple sites. We overcame these by offering flexible deployment options (cloud and on-premises), providing comprehensive training and support, and designing user friendly interfaces that required minimal workflow changes. Our human-centric approach—personalised onboarding, real-time support, and ongoing optimisation—was key to building trust and ensuring sustained adoption.
Augnito is currently used in over 375 hospitals across 25+ countries. What are the unique challenges in
standardizing voice AI across different healthcare systems, regulations, and EMRs?
The primary challenges are data interoperability, regulatory compliance, and linguistic diversity. Each country and even individual institutions have unique EMR/EHR/EPR and other hospital information
systems, documentation standards, and data protection laws (HIPAA, GDPR, ADHICS, etc.). Augnito
addresses this through flexible deployment (SaaS or on-premises), robust localisation, and adherence to regional compliance standards. Our APIs, HL7 support, and SDKs (low-code/no-code) enable seamless
integration with any existing system, while our language models are tailored for diverse clinical vocabularies and accents.
Our approach is always human-centric—customising solutions for each client and
providing hands-on support throughout the integration journey.
While automation improves productivity, how do you ensure clinical responsibility and trust remain intact? Is there a framework you follow for ethical AI deployment?
Clinical responsibility and trust are foundational to our philosophy. We design Augnito’s AI to augment—not replace— human expertise, ensuring clinicians remain in control of all decisions. Our ethical
framework is aligned with global best practices, focusing on transparency, robust audit trails, data security, and continuous validation. We also adhere to WHO’s pillars for AI in healthcare: transparency, risk management, data quality, and continuous post-market surveillance. Our systems ensure the final approval and control is always assigned to human clinicians, ensuring AI remains a supportive tool, not
an autonomous decision-maker.
Beyond voice recognition, there’s growing talk about multimodal AI. How is Augnito preparing for this
next wave of AI that blends voice, vision, and text in clinical workflows?
We see multimodal AI as the next frontier in healthcare technology. At Augnito, we’re actively involved in R&D across verticals— to ensure we are enabling richer, more contextual clinical workflows. This includes
integrating voice with medical imaging (like radiology), leveraging voice biomarkers for early disease detection, and developing agentic AI systems capable of orchestrating complex tasks across modalities. Our vision is to create an “invisible yet omnipresent” AI assistant that operates seamlessly in the
background, enhancing every aspect of care delivery.
What does the future roadmap look like for Augnito? Are there plans for patient-facing tools or home care integrations?
Our roadmap includes expanding our product suite across the care continuum—nursing, clinical decision support, finance, RCM, and possibly remote/home care. We are currently thoroughly invested in
patient-facing tools that leverage voice and ambient intelligence (as you can see with Omni) to empower both clinicians and patients in managing healthcare services proactively. Integration with wearable tech and remote monitoring are popular trends for the industry as a whole, essentially aiming to unify fragmented healthcare data and enable proactive, personalized care outside traditional hospital settings.
Finally, as someone deeply embedded in both Indian and global digital health ecosystems, how do you see the next five years shaping up for AI in healthcare—and what role do you see Augnito playing?
The next five years will see AI moving from isolated tools to comprehensive, agentic ecosystems—handling everything from documentation and diagnostics to care coordination and predictive analytics.
In India, the shift toward insurance-funded models, digital health adoption, and government-led initiatives like Ayushman Bharat will accelerate AI’s impact. Globally, AI will become an “AI staff” member— integral to healthcare teams. Augnito’s role will be central: delivering human-centric, interoperable, and ethical AI solutions that empower clinicians, enhance patient outcomes, and drive efficiencies across the entire healthcare continuum. As I’ve said in the recent past - The ultimate evolution won’t be measured in metrics or influence, but by transformed human connections: clinicians freed to heal, patients empowered to thrive, and technology that silently elevates both.
