AKASA
AKASA Innovation & Technology Culture
Frequently Asked Questions
As an AI-native company, innovation is built into AKASA’s culture, product and how we partner with customers to solve some of the toughest challenges in healthcare.
We’re a leader in applying generative AI to real operational problems in healthcare revenue cycle management. Our proprietary AI platform is specifically designed to understand complex clinical and financial data and automate workflows that have historically been manual, time-consuming, and error-prone.
One clear example is our collaboration with Cleveland Clinic, where AKASA’s generative AI tools are being used to enhance medical coding and clinical documentation processes across the health system. These tools help coders and documentation specialists work more efficiently and accurately by scanning hundreds of documents in minutes and interpreting clinical context rather than just keywords.
Internally, multiple engineers have been featured in Built In as top innovators redefining what AI makes possible across industries. Our team’s work reflects a pragmatic yet forward-looking approach to AI: empowering users, enhancing workflows, and moving beyond manual tasks to deliver measurable impact.
Our innovation also shows up in market momentum. AKASA is widely recognized as the leading generative AI provider in healthcare revenue cycle automation at a time when it’s deeply needed.
Both internally and with our customers, AKASA continues to push the frontier of how AI can transform healthcare operations through our innovative culture and practices.
Our AI-native technology culture is defined by rapid experimentation, close collaboration with users, and a deep commitment to building AI that enhances human expertise rather than replacing it. With AI being the the core component of our products, approach to building, and even how we recognize the work of our employees. AKASAns describe the culture as innovative and iterative, a place where ideas move quickly from concept to prototype to real-world feedback, rather than sitting in isolation for months waiting to be perfected. That ethos is visible both in how we work internally and in how our engineers approach their craft, using AI as a tool to work smarter and faster.
That feedback loop is central to how we build technology at AKASA. Our engineers and product teams work alongside internal subject matter experts and customers from early in the development process, meaning what we ship reflects how people actually think and work. The Clinical Documentation Dashboard is a strong example: rather than building in isolation, we studied real user workflows, created prototypes before writing production code, and incorporated feedback early and often. The result was a product users immediately recognized as designed around their needs. That same collaborative approach underpins our strategic partnership with Cleveland Clinic. This collaboration works to develop and launch AI tools for the revenue cycle that reflects what's possible when technology is built alongside the people who use it. That approach is part of why AI adoption in hospital revenue cycle management is surging, and why health systems are increasingly turning to partners like AKASA to lead the way.
Internally, we enable our employees with high ownership and autonomy, and consider everyone a builder. Engineers take significant ownership over their work and are visible directly to the CEO and other stakeholders, and even managers write code. We encourage every employee to propose ideas that will improve the company and build prototypes. Many of our best ideas have come from hackathons or employee-led prototypes.
We reinforce this culture by building experimentation responsibly into the development process, using tools like Figma to validate concepts before engineering investment, running pilot programs before broad rollouts, and maintaining a rapid release cadence grounded in rigorous testing. We also have regular updates on innovative use cases that employees have found to further incorporate AI into their day-to-day work. Our differentiated ML approach means we train our own models, and that our product models continuously improve based on real-world usage and customer input. All of this internal work has earned AKASA external recognition from Black Book Market Research, which named us one of 2025's fastest-rising AI RCM startups, and reflects how seriously our technology culture is regarded both internally and in the market.
AKASA Employee Perspectives
Describe a project you’re especially eager to tackle in the new year.
I’m especially excited to build more capable AI for complex healthcare workflows. Over the past year, we’ve made meaningful progress applying advanced ML and LLMs to extract structure from highly variable clinical and operational documents. In the new year, we’re pushing this even further and expanding the system’s ability to reason across multiple documents, understand nuanced context and surface richer, more accurate outputs. This next phase lets us break through some of healthcare’s most persistent administrative bottlenecks and deliver AI that feels more adaptive, more trustworthy and, ultimately, more helpful to the teams who rely on it every day.
What technologies and/or practices is your team leveraging to tackle this project?
We’re combining state-of-the-art language models with carefully engineered retrieval, validation and reasoning pipelines that reflect real-world healthcare workflows. Our team uses modern ML development tooling, including AI-assisted coding environments, to iterate quickly and with confidence. These tools help us test alternative architectures, generate prototypes, and validate assumptions in hours instead of days. We also lean heavily on collaborative engineering practices like tight feedback loops, well-instrumented experiments and continuous evaluation against real encounter data. Together, these technologies and practices allow us to build systems that are not only powerful but also aligned with the accuracy, reliability and transparency standards that healthcare requires.
How does this project tie into larger company goals?
This work sits at the center of AKASA’s broader mission: bringing AI to some of the hardest, most consequential problems in healthcare. By teaching our systems to understand complex clinical information with the nuance of a human expert, we’re opening the door to more accurate clinical documentation and fewer barriers across the care journey.
What types of products or services does your engineering team work on/create? What problem are you solving for customers?
AKASA builds ML systems designed to automate healthcare revenue cycle management, the complex set of processes that connect clinical documentation to billing and reimbursement. The revenue cycle is one of the most complex and error-prone areas in modern healthcare operations, and our goal is to remove friction from administrative processes so providers and staff can dedicate more time to what matters most: delivering excellent patient care.
One of our products enables medical coding. This involves creating systems that analyze clinical documentation from inpatient encounters and automatically assign ICD and PCS code, the standardized diagnostic and procedural codes used across the healthcare industry. These codes form the backbone of everything, from insurance billing and reimbursement to quality reporting and public-health tracking.
Traditionally, this has been a highly manual, detail-heavy task, requiring medical coders to review lengthy clinical notes and translate them into precise codes. It’s not only time-consuming but also susceptible to human error and inconsistencies.
Tell us about a recent project where your team used AI as a tool. What was it meant to accomplish? How did you use AI to assist?
Our research and engineering team uses a variety of AI-powered developer tools, including Cursor and Claude Code, to enhance our software development workflow. These tools serve as AI pair programmers, helping engineers brainstorm, design and debug code.
Recently, we have been using AI to accelerate experimental velocity, increasing the rate at which we can stand up prototype machine learning training and evaluation pipelines. Rather than spending hours poring over documentation, writing boilerplate code or manually testing small logic changes, working with coding agents allows us to divert more bandwidth to experimental design and analysis. The focus of hands-on-keyboard coding has shifted to regions of the codebase that are highly technical and/or bespoke, those where the internet would be of no help. For a new engineer ramping up on a codebase as complex as ours, having an AI assistant means being able to learn faster and contribute sooner. This approach embodies one of our team’s core principles: Use AI not just in the products we build but also in how we build them.
What would that project have looked like if you didn't have AI as a tool to use? How has AI changed the way you work, in general?
Without access to AI development tools like Cursor or Claude, each ML experiment would involve significantly more manual effort and incur a longer turnaround time. The absence of AI would also introduce more cognitive overhead and result in higher expenditure of mental energy on mechanical tasks.
For new team members like me, the onboarding curve would be much steeper, as they’d need to learn both the codebase and the domain context — without the real-time conversational code search and analysis AI now provides. Coding agents also empower those of us on the team who don’t come from traditional computer science backgrounds, broadening the surface area of code we can effectively collaborate on. In essence, AI has become a force multiplier for our teams, in engineering and beyond, helping us move faster, think bigger and deliver more intelligent solutions for our customers.
