Previously, the assistant was only available through One Medical, the primary care provider Amazon acquired for $3.9 billion in 2023.

With the expansion, customers can now access Health AI through the Amazon platform without needing to be Prime subscribers or One Medical members.

Health AI is designed to answer general health questions, explain medical records, help manage prescription renewals and schedule appointments. The tool can also connect users to healthcare professionals when medical attention is required.

According to Amazon, the AI assistant can respond to general health queries even without access to personal medical information.

However, with a user’s permission, the system can retrieve health data through the Health Information Exchange, a nationwide network that securely shares patient medical records.

This allows Health AI to interpret lab results, diagnoses and other medical records to provide more personalised responses about symptoms or medications.

Users can interact with the assistant by typing questions on Amazon’s website or in the app. For example, they may ask the system to explain cholesterol test results or seek advice on symptoms such as congestion or sore throat.

The company said all interactions with Health AI take place in a HIPAA-compliant environment, with conversations protected by encryption and strict access controls. Amazon added that its AI models are trained using abstracted patterns rather than identifiable patient data.

For instance, if many users ask about medication interactions, the company may analyse those patterns to improve responses while keeping personal information private.

Still, researchers have pointed to the risk of sharing sensitive health information with AI systems, warning that some companies use user conversations to train their models.

Health AI can also connect users with providers at One Medical if professional care is needed. In the United States, Prime members using the service are eligible for up to five free direct-message consultations with a One Medical provider for more than 30 common conditions such as cold and flu, allergies, acid reflux and urinary tract infections. Non-Prime users can still consult providers through Amazon’s pay-per-visit option.

The expansion comes as several artificial intelligence companies move further into healthcare. OpenAI recently introduced a health-focused version of ChatGPT designed to answer medical questions, while Anthropic launched a healthcare-oriented version of its Claude chatbot.

Amazon Pharmacy adds caregiver support and expands PillPack access

Alongside the Health AI rollout, Amazon also announced two updates to Amazon Pharmacy aimed at simplifying how customers manage medications.

The first update introduces a caregiver feature that allows trusted individuals to manage prescriptions for family members or loved ones through their own Amazon Pharmacy accounts. Once verified, caregivers can place orders, manage medications and track deliveries on behalf of the patient.

Amazon said the feature addresses a growing need for support among caregivers. Data from AARP shows that about one in five adults in the United States, around 53 million people, care for an ageing family member, usually spending lots of time coordinating healthcare and medications.

Through the new feature, customers can invite caregivers by sending a secure SMS link from their Amazon Pharmacy account. After confirming details such as the patient’s date of birth, caregivers can begin managing prescriptions online.

The company also expanded access to PillPack from Amazon Pharmacy, a service that delivers medications in pre-sorted packets organised by date and time. The system is designed for patients who take multiple prescriptions daily, helping them avoid managing several pill bottles.

With the update, more than 50 million beneficiaries of Medicare Part D can now use their insurance to access the PillPack service. Customers enrolled in the program receive monthly deliveries of personalised medication packets and can track shipments through the Amazon app.

Amazon Pharmacy accepts most insurance plans, including Medicare Part D nationwide and Medicaid in selected states. The company also offers additional discounts and delivery benefits for Prime members, including free same-day medication delivery in some U.S. cities.

The acquisition brings Torch’s four-person team into OpenAI and folds its core technology straight into the health product unveiled in January 2026. 

Torch gives OpenAI a ready-made system for pulling together scattered medical data at a moment when the company wants to enhance its focus on personal health tools.

Torch had been building what it described as “a medical memory for AI, unifying scattered records into a context engine.” The idea is to take health information spread across clinics, labs, wearables and wellness apps, and make it usable in one place. 

That work now sits at the heart of ChatGPT Health, which allows users to securely link medical records and daily health data inside the chatbot.

While OpenAI did not disclose the price, reports vary. Some put the value near $100 million in equity, others closer to $60 million. Either way, the structure points to an acqui-hire. The team joins; the product becomes infrastructure.

This development lands just over a year after a very different ending for the same founders. Torch’s team met while working at Forward Health, a high-profile clinic startup built around automated care. 

Forward raised close to $400 million before shutting down abruptly in late 2024, laying off staff and closing its doors. Torch’s sale shows how fast fortunes can turn in health technology, where ideas outlive companies.

ChatGPT Health itself is standing carefully. OpenAI says it is a secure, separate space within ChatGPT, designed to help people organise information, prepare questions and understand records, not to replace doctors. More than 260 physicians were involved in building safeguards around how responses are delivered.

With Torch in-house, OpenAI wants to solve one of the hardest problems in digital healthcare; making sense of messy, fragmented data without losing context or trust. 

In Nigeria, where the healthcare system faces immense strain from underfunding, limited infrastructure, and brain drain, AI presents an opportunity to reimagine patient care.

But there is a looming question: Can we embrace this technology without sidelining the human essence of care?

The AI Promise in African Healthcare

In Nigeria, where medical personnel often operate under enormous pressure, AI has the potential to bridge long-standing gaps in care delivery.

From overburdened teaching hospitals in Lagos to under-equipped clinics in rural Sokoto, AI could provide critical support through:

Remote diagnostics powered by machine learning, giving frontline workers decision support where doctors are scarce.

• Predictive analytics to model disease outbreaks or optimise maternal health interventions.
• AI-augmented telemedicine platforms that help triage and escalate patient cases efficiently.

In one of my co authored research AI Automation Framework for Single Cell Analysis, presented at the BIOTECHNO Conference 2023 I demonstrated how AI-driven automation can process complex biomedical data at scale, improving diagnostic insight and operational efficiency.

The same principles of scalable, explainable, and ethically governed AI can be adapted to strengthen Nigeria’s diagnostic systems, especially where skilled personnel are limited.

I’ve observed growing interest in tools that localise AI models for public health particularly in areas like maternal health and cervical cancer detection.

But innovation is not without its challenges.

The Pitfalls: When Tech Ignores the Human Story

Healthcare is inherently human. It is built on trust, empathy, and relationships. AI systems that are trained on foreign datasets or deployed without understanding cultural context risk becoming not only ineffective, but also dangerous.

Picture a diabetic patient in Ogun State receiving advice from an AI chatbot trained on Western diets and drug protocols.

There’s a very real risk of misdiagnosis or poor outcomes if cultural and socioeconomic factors aren’t embedded in the algorithm.

This is why I strongly advocate for “Trust-by-Design” principles in AI development. AI must not be a black box; it must be interpretable, explainable, and ethical—especially in high-stakes environments like healthcare. 

The Data Governance Dilemma

Another critical challenge is data privacy and digital ethics.

Nigeria, like many emerging economies, lacks enforceable data protection standards equivalent to the EU’s GDPR or the US’s HIPAA. Without proper governance, patient data used to train AI models could be mishandled, leading to misuse, breach of consent, or digital exclusion.

Continuously raising awareness around responsible data stewardship, especially in regions where digital inclusion is still a work in progress.

If we want AI to truly support the healthcare system, we must prioritise:

• Ethical AI SLAs and transparency metrics
• Public education on data rights
• In-country capacity to design and audit AI tools, not just import them

Human-in-the-Loop: Why AI Should Empower, Not Replace

AI must be framed as a co-pilot, not a replacement for clinicians.

I advocate for models where AI provides clinical decision support while the final judgment remains with human professionals. This hybrid model is essential in environments where:

• Digital literacy varies
• Emotional intelligence and bedside manner are vital
• Patients value face-to-face reassurance, especially in critical care

What’s Working Already?

There are signs of progress:

• mDoc Nigeria is enabling chronic disease self-management through AI-assisted virtual care.
• Helium Health is digitising patient records in hospitals across West Africa, creating a foundation for smart systems.
• 54Gene is using genomics and AI to understand diseases common to Africans—creating a more inclusive AI health future.

These examples showcase that Nigeria doesn’t have to wait for Silicon Valley solutions. We can innovate from within ethically and inclusively.

Final Reflections: Leading Responsibly in the AI Era

As an advocate of responsible use of I believe Africa can be a testbed for ethical, inclusive AI if we get the fundamentals right.

We need:

• Collaborations between health professionals, AI researchers, and policy leaders
• Mentorship pipelines to upskill local data scientists
• Train local health workers to work alongside AI, not fear it.
• Establish national guidelines for AI ethics, data use, and clinical accountability.
• Build AI tools for Nigeria, in Nigeria, reflecting our people, our conditions, and our context.
• Foster public dialogue so Nigerians understand both the risks and rewards of healthcare AI.
• Public discourse around algorithmic bias, patient rights, and trust

The future of healthcare is not about choosing between man and machine. It’s about designing systems where both works together safely, ethically, and inclusively.

AI can transform African healthcare. But only if we keep people at the centre. 

About the Author

Oyetola Florence Idowu is a forward-thinking technology professional specialising in digital transformation and the practical application of Artificial Intelligence in organisational settings. Known for her strategic mindset and passion for innovation, she works at the intersection of data, automation, and user-centred design, helping teams adopt emerging technologies safely and effectively. Oyetola is a published author, speaker, and mentor recognised for championing ethical AI use, promoting digital literacy, and driving technology initiatives that improve efficiency, accessibility, and community impact.  As a writer, author and award-winning co-author. Her work in AI and digital tech innovation has been featured in both local and international publications.

The round was led by Blume Ventures, with support from Schema Ventures, Vicus Ventures, Together Fund, DeVC, Medmountain Ventures, and strategic investors including Innovaccer, Memora Health, and existing customers.

The company is tackling one of healthcare’s biggest pain points which is patient phone calls. Despite digital options, 81% of patients in 2025 still use the phone to contact doctors, often facing long waits, confusing menus, or delayed responses. On the other side, understaffed front desks struggle to manage the volume, leading to frustration and burnout.

Confido’s platform removes the traditional phone tree. Its voice agents answer calls immediately, verify the caller, check insurance eligibility, and handle tasks such as referrals, refills, payments, updates, or appointment bookings. More complex issues are transferred to staff, with all interactions recorded directly into electronic health record (EHR) or practice management systems (PMS).

The need for such automation is increasing. The American Hospital Association has warned that hospitals are under severe financial strain while demand for round-the-clock access keeps growing. Many startups have entered this space in 2025, but Confido differentiates itself by offering a broader range of workflows beyond scheduling, giving providers higher efficiency and return on investment.

In less than a year, the company has scaled rapidly, serving more than one million patients today compared to just 150,000 in December 2024. Automation rates exceed 80%, with clients reporting reduced wait times, faster resolutions, and significant time savings for staff.

At Dallas Renal Group, results were immediate as 66% of patients confirmed appointments instantly on outbound calls, fewer than 6% required staff involvement, and inbound call wait times dropped to 15 seconds, saving nearly 50 staff hours in a single week. “Confido has helped make access faster, smoother, and far less stressful for everyone,” said Srinivas Danda, COO of Dallas Renal Group.

Confido’s Co-founder and CEO, Chetan Reddy, stressed the urgency of the moment. “Healthcare is at an inflection point. Labour shortages and rising patient demand mean practices can’t keep scaling front desks the way they used to. At the same time, building AI for healthcare isn’t like other industries – it requires deep empathy for both staff and patients. Our agents are designed to support people, not replace them, so patients get faster access and workers feel less stressed. That combination is what makes this moment so powerful.”

The company already operates across multiple specialities, including paediatrics, orthopaedics, nephrology, dermatology, gastroenterology, and pain medicine. Its roadmap goes beyond scheduling to include recalls, reactivation, payments, and care coordination, with speciality playbooks, audit trails, analytics, and first-call resolution metrics.

Investors are confident in Confido’s position. Sanjay Nath, partner at Blume Ventures, said: “Chetan, Vichar and the Confido team have gone incredibly deep into the trenches of the healthcare industry, having faced the pains of poor patient experience themselves – and have emerged with an offering that is transforming the way patient communication with providers is run. 

“It is clear to us that healthcare especially in the US is ripe for AI-led transformation, given the widespread administrative staff shortages, and Confido Health is well positioned to 10X the patient experience. We are very excited to lead this investment round and see a clear path to Confido becoming the market leader in this space, driven by a patient-first product ethos and close partnership with the provider ecosystem.”

Shubham Gupta, founding general partner at Together Fund, added: “Chetan, Vichar, and the Confido team have gone deeper than anyone we’ve seen in tackling the patient access problem. Their fully generative, multi-agent platform is not just a tech innovation — it’s already proving its impact in real-world provider settings by handling the communication bottlenecks that EHRs and legacy vendors have consistently failed at. 

“They are also building the most differentiated tech in this space focused on data & integrations not just voice. We’re excited to partner with them in building the market leader in AI-powered patient engagement.”

Confido Health believes that phones will remain healthcare’s most common entry point. In turning calls into efficient, human-like conversations, the company aims to become the standard infrastructure for patient communication across clinics and health systems of every size.

The company has built this model as a tool for both conversation and solving real enterprise problems in sectors like software engineering, financial services, and healthcare.

From day one, GPT-5 will replace its predecessors across ChatGPT platforms, and it’s being offered with expanded access tiers, including free usage, a $20/month Plus subscription, and a $200/month Pro tier. 

Developers also now have access through the OpenAI API, with three variants: GPT-5, GPT-5-mini, and GPT-5-nano. These versions differ in how long they spend “thinking” about problems, with pricing ranging from $1.25 to $10 per million tokens.

While consumer interest in AI remains high, enterprise adoption has been slower. OpenAI is hoping GPT-5 can tip the scale. The model delivers what CEO Sam Altman called “software on demand,” capable of building fully functional apps from natural language prompts. 

“GPT-5 is really the first time that I think one of our mainline models has felt like you can ask a legitimate expert, a PhD-level expert, anything,” Altman said during a press conference.

Behind the launch, OpenAI has struggled with the sheer technical demands of scaling up its models. The company has faced hardware failures during training, hit limits in the availability of new high-quality training data, and spent months waiting for results from high-cost training runs. 

At the same time, it has had to justify its skyrocketing costs, including investor expectations built on a potential $500 billion valuation and signing bonuses of up to $100 million for top AI talent.

Back then, when GPT-4 was launched, it passed a simulated bar exam in the top 10%, compared to GPT-3.5’s performance in the bottom 10%. With GPT-5, the upgrades are more subtle but targeted.

Take code generation. On SWE-bench Verified, a real-world benchmark for software engineering tasks, GPT-5 scored 74.9% on first attempts, outperforming Claude Opus 4.1 from Anthropic (74.5%) and Gemini 2.5 Pro from Google DeepMind (59.6%). In healthcare, its error rate on HealthBench Hard Hallucinations is 1.6%, far below GPT-4o’s 12.9%.

In science, GPT-5 Pro achieved 89.4% accuracy on PhD-level science queries, slightly ahead of rivals from xAI and Anthropic. But it lags in other areas, including real-time web navigation tasks. On the Tau-bench airline site navigation test, GPT-5 scored 63.5%, slightly behind OpenAI’s earlier o3 model (64.8%).

Despite these nuanced results, OpenAI insists the model is “safer, smarter, and more useful.” Alex Beutel, the company’s lead on safety research, said GPT-5’s reduced deception rates are essential for building trust. 

“It’s more transparent and honest in ways users can trust,” he said, adding that GPT-5 also more reliably filters out harmful queries while reducing false positives, unnecessary content rejections.

From a usability standpoint, GPT-5 also comes with new personalisation features. Users can now select from four built-in personalities, Cynic, Robot, Listener, and Nerd, which adjust the tone and structure of responses. Unlike earlier models, users no longer have to manually tweak settings to get different types of output.

Internally, OpenAI believes GPT-5 represents a shift in how people will use AI, not just to answer questions, but to act more like agents or assistants. 

That includes handling schedules, creating research briefs, analysing financial documents, and building apps from scratch. “This idea of software on demand is going to be one of the defining features of the GPT-5 era,” Altman said.

But while the ambition is high, there’s still caution among experts. Some reviewers told Reuters they weren’t convinced GPT-5 is a major leap over GPT-4. Others, like Noah Smith, raised concerns about the financial sustainability of current AI development. 

“Business spending on AI has been pretty weak, while consumer spending has been fairly robust because people love to chat with ChatGPT,” he said. “But the consumer spending on AI just isn’t going to be nearly enough to justify all the money that is being spent on AI data centres.”

Altman himself admitted GPT-5 still has limitations, especially around independent learning. It cannot, on its own, acquire new knowledge or skills without user input. And while test-time compute (a method of giving the model more thinking power when needed) helps in solving complex problems, it’s not a substitute for self-directed learning.

Still, the company believes in its innovation. With over 700 million weekly ChatGPT users and increasing partnerships with enterprise customers, GPT-5 may help OpenAI bridge the gap between consumer curiosity and business utility.

This four-month initiative, now in its second year, targets startups working in sectors such as cybersecurity, transportation, education, and healthcare. 

Google is not offering equity-based investments here, instead, it’s providing hands-on mentorship, sales training, and access to its cloud tools, including some of its most advanced AI models. Most of the sessions will be virtual, but participants will later gather in person for a summit.

These companies had to meet tight selection criteria: evidence of market traction, at least six months of financial runway, and a product or service capable of making significant impact. Applications closed in mid-May following a highly competitive process.

Startups selected for this new cohort include:

1. Block Harbor, working on cybersecurity for automotive systems.
2. Attuned Intelligence, building AI-powered voice agents for call centres.
3. CloudRig, helping construction contractors manage production workflows with AI.
4. Mpathic, automating clinical trials and medical documentation.
5. StudyFetch, offering personalised learning tools to students and educators.
6. Omnia Fishing, which gives users personalised fishing advice based on data.
7. Making Space, matching disabled jobseekers with potential employers.
8. Tansy AI, helping users organise their medical records and appointments.
9. Waterplan, which lets companies track and respond to water-related risk.
10. Nimblemind.ai, making health data more usable and searchable.
11. Satlyt, a platform to process satellite data efficiently.
12. Tradeverifyd, which helps companies assess supply chain risk in global markets.
13. CircNova, using AI to understand RNA patterns for new therapeutics.
14. Otrafy, automating supply chain compliance and documentation.
15. Partsimony, helping companies build and manage their manufacturing supply chains.
16. Vetr Health, providing at-home veterinary care.
17. MedHaul, which connects hospitals with non-emergency transport options.

Among the programme’s earlier alumni is Cloverleaf AI, which secured a $2.8 million seed round after joining last year’s cohort. Another, Zordi, raised $20 million from Khlosa Ventures for its autonomous agtech solutions. These reveal Google’s reputation as an early identifier of high-impact startups in the AI space.

The Academy is just one of several efforts by the tech giant to shape the AI startup ecosystem. In May 2025, Google launched the AI Futures Fund, a rolling investment initiative supporting startups that are already building with DeepMind’s latest models—Gemini, Imagen, and Veo.

Startups funded under the Futures Fund receive equity investment, early access to the models, Google Cloud credits, and hands-on help from Google and DeepMind engineers. Some notable participants so far include:

• Toonsutra, a comic app that uses Gemini to translate webtoons across multiple languages.
• Viggle, an AI meme-generation platform powered by Gemini and Veo.
• Rooms, which allows users to create 3D spaces with interactive avatars.

This expansion into targeted funding aligns with Google’s broader AI education and inclusion strategy. At the UN Summit of the Future, CEO Sundar Pichai announced a $120 million Global AI Opportunity Fund aimed at reducing what he called the “AI divide”, the growing disparity in access to AI knowledge and tools across countries. 

He said: “We believe AI should benefit everyone, everywhere, not just those in high-income economies.”

The $120 million fund works with NGOs and local partners to bring AI education to underserved communities, especially in countries with poor digital infrastructure or lacking policy frameworks to support tech growth.

Meanwhile, Google.org, Google’s charitable arm, has launched a $20 million Generative AI Accelerator that supports nonprofits using AI for public good. This includes funding for projects in climate resilience, healthcare access, and digital education.

These developments come as AI remains both a disruptive force and a promising tool. While the headlines usually focus on the risks, from deepfakes to disinformation, Google appears to be betting that the next breakthroughs will come from startups willing to solve practical, overlooked problems.

With its blend of funding, mentorship, and infrastructure support, Google is building a growing network of startups, and we see the company wants a hand in shaping how AI evolves far beyond Silicon Valley.

Today, the company announced $15 million in new funding to scale its voice AI platform, which automates high-friction insurance calls that cost provider organizations and billing companies billions of dollars every year.

The debt and equity series A round was led by SignalFire, with participation from Slow Ventures, BoxGroup, and Scrub Capital. It includes $3 million in venture debt for SuperDial to invest in R&D and go-to-market initiatives. 

In total, the company has now raised over $20 million in funding. This also marks one of the first investments from SignalFire’s new $1 billion fund focused on applied AI.

SuperDial builds AI agents that handle outbound phone calls from providers and billing companies to insurers – navigating phone trees, waiting on hold, and conducting live conversations with payer reps. 

These AI agents support tasks like benefits verification, prior authorisation, claims follow-up, and credentialing. When a call can’t be completed by an AI agent, SuperDial’s human call centre team steps in, ensuring reliable outcomes while continually improving the AI.

The platform integrates with EHRs and other systems of record to automate documentation, including writing back data gathered from calls, such as claims status updates. Customers rely on SuperDial not just to cut costs, but to unlock capacity across their revenue operations teams. Customers have reported up to 3x cost savings per call and 4x productivity gains for their existing billing teams. 

SuperDial was founded by Sam Schwager and Harrison Caruthers, who met at Stanford while studying computer science. After building a healthcare billing company that spent thousands of hours on repetitive calls to payers, they saw the opportunity to automate the problem. What started as an internal tool quickly grew into a standalone solution.

“The timing is perfect for us to tackle this problem at scale, with AI capabilities quickly maturing and the healthcare sector looking for new ways to drive efficiency by leveraging next-gen technology. Our success to date, and the incredible level of interest and excitement we’re seeing from the market, are clear signs that we’re solving a real, urgent problem,” said Sam Schwager, co-founder and CEO of SuperDial. 

Since launching at the end of 2023, the company has quickly scaled to seven figures in revenue and tens of thousands of calls per week. 

Earlier this year, SuperDial acquired MajorBoost, a voice AI company specialised in navigating complex phone trees and insurer workflows. The acquisition deepened SuperDial’s technical team and further cemented its leadership in healthcare-specific call automation.

SuperDial’s growth comes as healthcare organisations seek to cut admin costs without expanding headcount. The $150 billion U.S. RCM market still relies on manual phone calls for basic tasks – calls that can take over an hour and pull staff away from higher-impact work.

SuperDial’s customers include RCM companies and large provider organisations – including DSOs and MSOs – that manage billing in-house. Their customers rely on SuperDial to improve financial performance, reduce burnout, and unlock their teams’ capacity to focus on higher-value work. 

At West Coast Dental, SuperDial now handles over 10,000 calls per month to check claim statuses, a process that previously left nearly 70,000 claims in backlog and would have required five new hires to process. With SuperDial, the team has significantly reduced AR days and gained trustworthy, up-to-date visibility into claims.

“SuperDial isn’t just automating phone calls – they’re building the connective tissue for how the healthcare ecosystem will communicate in the future,” said Yuanling Yuan, Partner at SignalFire. 

“We believe agentic AI infrastructure is inevitable, and SuperDial is leading that shift with rapidly growing traction and a team that deeply understands the problem. This is exactly the kind of applied AI we’re excited to back.”

Looking ahead, SuperDial will deepen its EHR integrations, expand to new administrative workflows, and continue training its agents using real-world call data. 

Although healthcare never built the APIs to enable clean, system-to-system communication, SuperDial is building the next best thing: a network of AI agents that can navigate fragmented infrastructure on behalf of the organisations that rely on it. 

SuperDial believes the future of healthcare coordination will be agent-powered – where payers, providers, pharmacies, labs, and other healthcare organisations can seamlessly communicate with one another, AI-to-AI. And SuperDial will power that future.

To solve this, RISA Labs has raised a $3.5M funding round to help healthcare organizations eliminate one of the most persistent barriers to timely cancer care: prior authorization delays. 

RISA Labs has already proven that faster care is possible by dramatically reducing manual workflows and administrative burden.

The seed was led by Binny Bansal (Flipkart co-founder) with participation from Oncology Ventures, General Catalyst, z21 Ventures, ODD BIRD VC, and Ashish Gupta. The capital will accelerate deployments in the next 100 cancer centers across the country within the next two years. 

“Prior authorizations remain one of the least automated parts of our healthcare system,” said Ben Freeberg, managing partner at Oncology Ventures.

“In oncology, the stakes are higher. 70% of cancer patients experience delays in care because of prior authorization requirements. In 33% of those cases, the delay is one month—a time window that can increase the risk of death by 13% in certain cancer types. The current system isn’t just inefficient – it’s dangerous.”

RISA’s platform—Business Operating System as a Service (BOSS) – is not another automation bot or AI assistant. 

It’s a full-stack orchestration engine built for the vertical complexity of healthcare, Instead of relying on humans to push paperwork or brittle bots that break when systems change, BOSS decomposes complex workflows into micro-tasks, then delegates them to a network of intelligent agents—LLMs, digital twins, and reinforcement learners, extending across an institution’s entire software stack. 

This allows BOSS to create a parallel digital workforce, operating on behalf of teams and alongside them. A 1,000-person institution can function like a 2,000-person one overnight, with digital agents making up half the workforce.

“We’ve had Windows, we’ve had Linux, we’ve had Mac, each OS helped humans extract more from machines. But now, we’re drowning in software. There’s too much of it, and a shortage of skilled labor to operate it. Software that was supposed to get work done has become work itself,” Kshitij Jaggi, co-founder and CEO of RISA Labs adds.

“BOSS is an AI OS designed for the post-ChatGPT era : where work is no longer about learning tools, but simply expressing intent.”

At a leading US cancer center, BOSS reduced prior authorization times from 30 minutes to under five. In just a few months, it processed over $1 million in medications, freed up 80 percent of staff time, and cut administrative costs by 66 percent.

“Cancer care is time sensitive. Every delay in treatment can affect outcomes. Prior authorizations continue to slow us down. What RISA is building is not just smart technology. It removes barriers so our teams can move faster and stay focused on what matters most: caring for patients,” said Dr. Jeffrey Vacirca, CEO of New York Cancer and Blood Specialists.

Based in Silicon Valley, RISA is founded by IIT Kanpur alumni and repeat founders, Kshitij Jaggi (CEO) and Kumar Shivang (CTO) who’ve been friends for more than a decade now, who’ve previously built and scaled Urban Health. 

Their frustration with fragmented, slow, and error-prone healthcare workflows during that journey inspired the duo to take a systems-first approach, leading them to develop a foundational AI operating system that can simulate, understand, and orchestrate entire institutional workflows from end to end.

“BOSS is low-entropy system design to bring flow state in system-2 thinking for LLMs; it aims to maximise AI agents’ usefulness for critical problems like oncology operations,” said Kumar Shivang, co-founder & CTO of RISA. 

“Its orchestration layer then turns that intelligence into precise, real-time execution with integrations with systems of record like Flatiron Health’s EMR.”

RISA’s founding team first explored these concepts through research, co-authoring ‘Digital Twin Ecosystem in Oncology Clinical Operations’—an early effort to envision smarter, AI-driven cancer care workflows.

This foundational work laid the conceptual groundwork that later translated into tangible improvements in real-world oncology operations.

RISA’s platform signals a broader shift in enterprise AI. “As AI agents unbundle the $4.6 trillion services industry, RISA’s BOSS leads the way—proven in oncology and built to scale,” said Binny Bansal, co-founder of Flipkart and lead investor.

Looking ahead, RISA plans to extend across multiple nodes within the oncology ecosystem, positioning itself as the AI transformation partner for both operational and clinical workflows. 

This includes enabling coordination and intelligence across providers, life sciences organizations, and other stakeholders throughout the journey of a drug – extending the company’s long term vision to building a unified layer for AI-driven orchestration in oncology.