How to Make an AI Revenue Software Like Akasa

Key Takeaways

• Healthcare providers are adopting AI revenue cycle management software to automate billing, reduce claim denials, and improve financial performance.
• Platforms like AKASA combine healthcare-specific LLMs, AI coding, clinical documentation improvement, and automated claims management for efficient revenue operations.
• A successful platform requires EHR integrations, AI-driven coding, pre-bill optimization, secure healthcare infrastructure, and continuous model learning.
• AI revenue software helps hospitals improve coding accuracy, reimbursement rates, operational efficiency, and revenue recovery while reducing administrative workloads.
• How Idea Usher can help businesses build AI revenue software with healthcare AI, EHR integrations, intelligent automation, and scalable revenue cycle solutions.

Healthcare providers are no longer satisfied with software that only automates billing tasks. They want AI that understands the revenue cycle and improves with every claim it processes, which is why AI revenue software is gaining attention. These platforms help hospitals reduce revenue leakage, speed up reimbursements, and make day-to-day operations much more efficient. If you’re planning to build a similar solution, the real goal is to create software that keeps learning and delivers measurable financial value over time.

We’ve built numerous AI revenue software solutions that combine healthcare-specific large language models with intelligent revenue cycle automation to help providers streamline billing operations and maximize reimbursements. As we have this experience, we’ve put together this blog to walk you through the key steps involved in building an AI revenue software solution like Akasa. Let’s get started!

Market Potential of AI Revenue Cycle Management Softwares

According to Grand View Research, the AI revenue cycle management market was valued at USD 20.6 billion in 2024 and is expected to reach USD 70.1 billion by 2030, growing at a 24.2% CAGR. That kind of growth shows how quickly healthcare organizations are adopting AI to improve financial performance. Instead of relying on manual billing and claims processes, providers are using AI to recover more revenue, get paid faster, and reduce the workload on their administrative teams. 

Source: Grand View Research

Investors looking at this space will find a market backed by strong secular tailwinds. The sheer volume of medical billing data, combined with increasingly complex insurance rules, makes manual processing obsolete. Take Waystar as a prime example of this momentum. The cloud-based healthcare payment platform, which relies heavily on AI-driven automation, reported a staggering $1.099 billion in total revenue for the fiscal year 2025, marking a 17% growth year-over-year.

• Compounding Market Value: Industry data shows the global AI billing market accelerating at a double-digit compound annual growth rate.
• Uncapped Addressable Market: Every tier-one hospital group, regional clinic, and digital health provider requires collection infrastructure, ensuring a vast base of potential clients.
• Regulatory Catalysts: Frequent compliance changes force healthcare providers to update their systems, creating a recurring need for modern software solutions.

This growth is not a temporary trend. It represents a fundamental restructuring of how healthcare providers secure their cash flow, making it a highly attractive sector for deployment of capital.

Reducing Hospital Revenue Loss

Healthcare providers lose billions of dollars every year because of claim denials, coding mistakes, and inefficient billing workflows. AI revenue cycle software helps catch these issues before claims are submitted. This improves reimbursement rates, speeds up payments, and reduces the amount of manual work for administrative teams.

The demand for these solutions is reflected in companies like FinThrive, which generates an estimated $400–500 million in annual revenue. Its growth shows that hospitals are actively investing in AI platforms that can recover lost revenue, improve financial performance, and deliver a clear return on investment.

Massive Enterprise SaaS Opportunity

Enterprise healthcare buyers are looking for integrated platforms rather than fragmented tools. They want comprehensive software that easily connects with their existing Electronic Health Record systems while automating everything from patient eligibility checks to final collections. This enterprise demand translates into an exceptional business model for software owners:

• High Average Contract Value: Large hospital networks sign multi-year agreements worth hundreds of thousands in recurring revenue.
• Strong Net Revenue Retention: Once a hospital integrates an RCM platform into its daily workflow, the switching costs become incredibly high, resulting in predictable long-term revenue.
• Monetization Pathways: Beyond standard software licensing, platforms can charge volume-based transaction fees or take a small percentage of successfully recovered revenue.

How AKASA Uses Generative AI to Transform Revenue Cycle Management?

The AKASA platform is a specialized enterprise generative AI platform built explicitly for healthcare revenue cycle management. Operating at a massive scale, the software is deployed across more than 650 hospitals and 6,500 outpatient clinics, processing operational workflows for health systems that represent over $120 billion in net patient revenue.

Backed by more than $200 million in venture capital from elite firms like Andreessen Horowitz and Bond Capital, the platform goes far beyond basic task automation. It utilizes advanced machine learning architectures to natively solve the deep financial complexities, billing inefficiencies, and administrative burdens that eat away at health system margins.

Specialized LLMs Power Workflows

Generic AI models are not designed for the complexities of healthcare revenue management. AKASA addresses this by using healthcare-specific large language models trained on clinical documentation, insurance claims, and payment data. This enables the platform to understand medical terminology, coding requirements, and payer policies with much greater accuracy than general-purpose AI.

Another advantage is adaptability. Traditional automation tools often fail when workflows or payer requirements change, leading to costly manual intervention. AKASA’s AI understands the context behind healthcare data, allowing it to adjust to new documentation and billing requirements more effectively. This helps providers reduce claim errors, improve coding accuracy, and keep revenue flowing without constant rule updates.

AI Reviews Whole Clinical Records

Most hospitals rely on sample-based audits where compliance teams manually check a tiny fraction of claims due to time constraints. This approach leaves millions of dollars in unbilled care on the table while exposing the system to compliance penalties. AKASA changes this dynamic by analyzing complete patient records in seconds, reviewing everything from physician narratives and lab metrics to imaging summaries and medication logs.

• Total Coverage: The platform evaluates 100% of eligible patient encounters before the bill is ever sent out to the insurance provider.
• Gap Detection: The system flags hidden documentation gaps where a provider performed a service but failed to document it with the specific phrasing required for payment.
• Risk Prevention: By cross-referencing clinical notes with final codes, the software automatically removes overcoding risks that could trigger federal audits.

Reviewing the entire chart pre-billing allows health networks to identify missed revenue opportunities instantly. Entrepreneurs looking to build in this space should notice how moving audits from a reactive, retrospective sample to proactive, total coverage delivers immediate financial returns to enterprise clients.

Human and AI Collaboration

Healthcare revenue management requires a high level of accuracy, so relying entirely on AI is not always practical. AKASA uses a human-in-the-loop approach where AI supports billing teams instead of replacing them. Every recommendation is backed by evidence from the medical record, giving staff the confidence to review and approve decisions within their existing workflow.

When the platform encounters a complex claim or an unusual case, it automatically hands the task to a billing specialist. The AI then learns from how the expert resolves the issue, making it better prepared to handle similar cases in the future. This combination of automation and human expertise improves accuracy while helping healthcare organizations automate more of their revenue cycle over time.

AKASA’s AI Workflow: From Clinical Documentation to Claims

A medical claim goes through several steps before a healthcare provider receives payment. Even small mistakes during this process can lead to delays or claim denials. AKASA simplifies the entire revenue cycle by using AI to convert clinical documentation into accurate, compliant claims. This helps providers reduce manual effort, submit cleaner claims, and receive reimbursements more quickly. 

AI Converts Records into Insights

A patient record contains a massive mix of unstructured data. Physician progress notes, laboratory panels, diagnostic imaging reports, medications, and vital sign trends are often scattered across different systems. AKASA ingests all of this diverse data to reconstruct the entire clinical story of a patient encounter. Its clinical AI reads between the lines of unstructured physician notes to detect patterns that suggest a specific, billable diagnosis.

• Catching Omissions: If a doctor prescribes aggressive intravenous fluids and monitors kidney function but forgets to list acute kidney injury on the discharge sheet, the AI flags this discrepancy instantly.
• Preventing Revenue Leaks: Missing minor details can drop a hospital’s reimbursement rate significantly. This process ensures every piece of clinical care is accounted for before a bill is generated.

Analyzing clinical charts at this level of depth dramatically reduces errors, helping billing departments recover thousands of dollars per complex patient stay.

Intelligent CDI and Coding

Historically, Clinical Documentation Improvement teams and medical coders worked in separate silos. This disconnect led to communication delays, slow billing cycles, and missed opportunities. The AKASA Prebill Optimization Suite unites these two functions into a single, cohesive workflow.

The platform re-analyzes every single inpatient encounter, prioritizing the highest-impact cases. It provides clear, evidence-backed recommendations directly to the staff, allowing CDI specialists and coders to collaborate instantly before any bill is finalized.

• 95% and Higher Accuracy: By using AI to cross-reference documentation with coding guidelines, platforms can push clean-claim accuracy rates past the 95% threshold.
• Targeted Efficiency: Instead of manually reviewing hundreds of claims, teams are directed to the top 10% or 15% of complex cases that actually require human judgment.

This targeted approach streamlines hospital workflows, eliminating back-and-forth communication and cutting down pre-bill processing times from days to hours.

Smarter Claims Speed up Payments

When accurate clinical documentation and correct coding flow naturally into a bill, the result is a clean claim. This highly accurate output drastically reduces insurance denials, compliance risks, and revenue leaks. Beyond coding, the platform automates secondary administrative bottlenecks like prior authorizations and real-time claim status tracking.

• Lowering Cost-to-Collect: Automating these tedious manual steps helps hospitals drop their cost-to-collect by 30% or more.
• Fewer Denials: Clean claims yield an immediate drop in initial insurance denial rates, which frequently run as high as 10% to 15% at traditional facilities.
• Days Sales Outstanding (DSO) Reduction: Getting payments processed correctly on the first attempt slashes DSO by multiple days, releasing valuable working capital for health systems.

Key Features of an AI Revenue Software Like Akasa

Replicating the market success of AI revenue software requires understanding the specific tools healthcare teams rely on daily. When billing departments deploy Akasa, they look for features that eliminate manual data entry and actively prevent revenue loss. The core capabilities listed below define how users interact with the Akasa ecosystem to streamline their financial operations.

1. AI Coding and Suggestions

Medical coders use Akasa to accelerate their daily chart review by letting the AI handle the heavy lifting of code assignment. The platform scans the full clinical record and instantly suggests the most accurate ICD and CPT codes.

• Verifiable Proof: Instead of just guessing a code, Akasa shows the exact sentence or lab result from the medical record that justifies the recommendation.
• Workflow Integration: Coders review these suggestions inside their usual workspace, either approving them with a single click or making quick adjustments based on the clear text evidence.

This transparent approach builds immediate trust, allowing coding teams to process double the amount of charts per day while maintaining strict compliance.

2. CDI Assistant Capabilities

CDI specialists use the Akasa assistant to catch missing information while the patient is still admitted. The software works in the background, continuously reading physician progress notes to spot clinical contradictions. When Akasa notices a gap, it creates a structured clinical query for the doctor. The CDI team reviews this pre-drafted query and sends it over to clarify the patient story. This early correction ensures the hospital can bill for the exact severity of care provided, directly improving quality reporting metrics.

3. Prebill Optimization Suite

Hospital billing managers use the Akasa Prebill Optimization Suite to break down the walls between their coding and CDI teams. Users log into a centralized dashboard that highlights high-value or high-risk claims right before they are sent to insurance companies. Akasa automatically cross-references the final codes against the clinical chart one last time. It flags compliance risks, catches overcoding errors, and prevents revenue leakage by identifying services that were documented but left off the final invoice.

This proactive review eliminates the need for expensive claim rework, allowing teams to fix errors before a formal rejection ever happens.

4. Prior Authorization Automation

Front-office administrative staff use Akasa to eliminate the headache of securing insurance approvals before complex medical procedures. The authorization module handles the tedious communication channels that usually stall patient care.

• Rule Checking: Akasa instantly determines if a scheduled procedure requires prior approval based on the patient’s specific insurance plan.
• Automatic Submissions: The software extracts clinical data from the medical record, populates the insurer’s form, and submits the package automatically.
• Status Monitoring: It logs into insurance portals around the clock to track approvals, updating the hospital team the moment a status changes.

5. Automated Claim Tracking

Billing representatives use Akasa to stop wasting hours on hold with insurance companies just to check on unpaid claims. The software automatically logs into hundreds of payer portals to pull real-time updates on every submitted invoice. Akasa categorizes these insurance responses instantly. If a claim is approved and scheduled for payment, the system updates the record without human intervention. If a claim is stuck due to a documentation error, Akasa flags the issue and moves it directly to the right specialist’s queue for immediate resolution.

6. Revenue Cycle AI Advisor

Operational teams use the Akasa AI Advisor as an on-demand expert for complex billing questions. Users type natural language questions into the interface to navigate the thousands of pages of changing insurance policies and federal coding manuals.

• Instant Answers: A coder can ask, What are the current documentation requirements for billing a complex cardiac ultrasound?
• Contextual Sourcing: Akasa instantly pulls the exact section of the payer policy, summarizes the rules, and provides a clear checklist for the user.

This feature eliminates the need to manually flip through massive policy updates, drastically increasing the daily productivity of the entire revenue cycle team.

7. Custom Models and EHRs

IT directors and health system executives experience Akasa as a deeply embedded layer within their core technology stack. The platform integrates directly with major Electronic Health Record systems like Epic and Cerner.

• Hyper-Local Training: Rather than using a generic model, Akasa trains its system on the specific clinical vocabulary and historic billing data of the host hospital.
• Seamless Delivery: Users never have to log into a separate third-party website. The AI recommendations appear directly within the EHR screens they already use.

This deep technical alignment ensures the platform constantly learns from the hospital’s specific patterns, providing highly accurate recommendations that get smarter over time.

How to Make an AI Revenue Software Like Akasa?

Building AI revenue software for healthcare requires more than standard development tools. We design enterprise-grade platforms with healthcare-specific AI, secure architecture, and scalable infrastructure so businesses can launch reliable solutions that improve financial performance and meet industry requirements from day one.

1. Custom Healthcare AI Engines

Generic large language models fail when handling medical billing because they miss the relationship between medical documentation and financial reimbursement. Building a viable competitor to Akasa requires training domain-specific models on hundreds of millions of clean clinical and financial data points.

Our development strategy prioritizes training models that natively interpret complex medical terminology, payer contracts, and localized hospital billing structures. Moving beyond basic prompting ensures the AI engine adapts fluidly to sudden changes in insurance rules without breaking down or producing inaccurate outputs.

2. AI Coding and CDI Development

The core of an advanced revenue platform is its medical coding and Clinical Documentation Improvement (CDI) engine. This module reads unstructured patient text and identifies documentation gaps before the final claim goes out to the insurer.

• Deep Text Analysis: The AI parses complex physician progress logs, lab results, and diagnostic images to surface accurate ICD and CPT codes.
• Evidence Mapping: The system must highlight the exact sentence or clinical value that validates a specific code, giving human reviewers total transparency.
• Gap Extraction: If the software detects a treatment pattern that lacks the matching clinical phrasing, it generates a compliant clarification query for the care team.

Our engineering teams construct these modules to streamline complex clinical tasks, ensuring high accuracy and protecting hospitals from expensive auditing penalties.

3. Claims and Authorization Automation

Administrative bottlenecks in front-office and back-office operations regularly delay cash collections. Automating authorization checks and status tracking frees up human staff to focus on highly complex claim exceptions. We focus heavily on building automated workflows that log directly into diverse insurance portals around the clock. The software extracts approval requirements, submits clinical documentation packets, and categorizes payer updates instantly, cutting manual tasks by a significant percentage.

4. Unified Prebill Dashboards

Managing coding, clinical documentation, and compliance across different systems slows down billing and increases the risk of missed revenue. We build unified pre-bill workspaces that bring these workflows together in one place, making it easier for teams to review patient records, identify high-priority cases, and submit accurate claims with less manual effort.

5. EHR Integration and Security

An AI revenue application must operate natively inside existing healthcare technology frameworks. Hospital IT directors will quickly reject a standalone dashboard that requires manual data uploads.

• Interoperability: The platform must connect deeply with major Electronic Health Record systems like Epic and Cerner using modern HL7 and FHIR API standards.
• Compliance Framework: Healthcare data demands strict security architectures including end-to-end data masking, strict access controls, and infrastructure built to satisfy HIPAA, HITRUST, and SOC 2 requirements.

We bring deep integration experience to the table, engineering compliant systems that place AI recommendations directly onto the interface screens hospital staff already use every day.

6. Conversational Copilots for Staff

Operational staff need immediate answers when researching complex insurance rules or explaining tricky coding recommendations. A conversational AI assistant integrated into the platform acts as an on-demand billing consultant.

• Natural Queries: Staff can ask questions about active insurance guidelines or billing guidelines directly in plain text.
• Policy Sourcing: The AI assistant instantly isolates relevant policy documents and delivers clear, evidence-backed next steps to the user.

Our development approach focuses on building helpful digital assistants that maximize individual staff output, speeding up decision-making across the whole billing department.

7. Continuous Model Data Training

The best AI revenue software continues to improve as it is used. We build feedback loops that learn from coding corrections and reviewer decisions, allowing the platform to adapt to each healthcare organization’s workflows. Over time, this improves claim accuracy, reduces manual effort, and helps providers achieve better reimbursement outcomes.

Cost to Develop an AI Revenue Software Like Akasa

Building an AI revenue cycle software requires careful planning to balance development costs with long-term scalability. We help businesses estimate budgets based on the features, AI capabilities, compliance requirements, and infrastructure needed to build a secure, enterprise-ready platform that delivers lasting value.

MVP vs Enterprise Development Cost

Starting with an MVP is a practical way to validate your AI revenue cycle software before making a larger investment. We recommend launching with core features such as AI-assisted coding, claims management, and EHR integration, then expanding into advanced AI capabilities, analytics, and multi-hospital support as your product and customer base grow.

Our development squads focus on lean architectural planning, helping founders build highly functional MVPs that can scale smoothly into enterprise tiers without requiring a complete code rewrite.

Factors That Influence Development Cost

The final capital requirement for a healthcare revenue platform depends heavily on specific technical decisions and integration requirements. The more deeply embedded the software is within the hospital’s day-to-day operations, the more engineering hours are required to secure the environment.

• Custom AI Model Fine-Tuning: Relying on generic API models is affordable but lacks a defensive product moat. Fine-tuning models on specialized clinical documents and financial datasets requires substantial data preprocessing and optimization.
• System Interoperability: Building reliable API tunnels connecting to major health networks using HL7 and FHIR standards is a major cost driver. The system must securely sync data with platforms like Epic and Cerner without disrupting active clinical workflows.
• Strict Security and Compliance: Healthcare finance demands rigorous security controls. Building infrastructure that guarantees data masking, total access logging, and strict adherence to HIPAA, HITRUST, and SOC 2 guidelines adds a premium to initial development budgets.

We mitigate these cost spikes by deploying pre-audited security frameworks and using pre-built interoperability protocols, drastically reducing the billable engineering hours needed to achieve top-tier hospital certification.

Estimated Budget and Development Timeline

Most AI revenue cycle platforms are built in phases instead of being developed all at once. A typical development timeline ranges from 9 to 18 months, with many businesses launching an MVP in the first 6 months. This approach helps validate the product, gather customer feedback, and demonstrate early business value before expanding the platform.

We support businesses through every stage of development, from planning and MVP delivery to enterprise-scale expansion. As the platform grows, we add advanced capabilities such as AI-powered pre-bill optimization, predictive denial analysis, and intelligent workflow automation while ensuring the solution remains secure, scalable, and compliant.

Investment Trends in AI Revenue Cycle Startups

Investment in AI-powered healthcare revenue cycle software continues to grow as hospitals look for better ways to improve financial performance. Strong interest from venture capital firms and enterprise investors shows that AI-driven billing and revenue management have become a long-term priority, creating significant opportunities for businesses entering this market. 

1. Abridge Moves to Revenue Workflows

Originally recognized for its ambient clinical documentation tools, Abridge has leveraged massive funding rounds to push directly into revenue cycle intelligence. The company secured a major $250 million investment round followed by an additional $316 million to scale its advanced generative AI capabilities across enterprise healthcare networks.

• Platform Expansion: This massive $566 million capital injection accelerates the development of tools that translate live patient-doctor conversations directly into structured coding insights.
• Workflow Value: By moving deeper into the billing pipeline, the software links clinical notes directly to final code validation, ensuring accurate insurance submissions.

This scale of funding underscores the high value the market places on end-to-end platforms that naturally connect bedside documentation with backend hospital reimbursement workflows.

2. CodaMetrix Scales Autonomous Coding

CodaMetrix, which was originally incubated within the Mass General Brigham hospital ecosystem, continues to secure major growth capital to tackle coding inaccuracies. The platform raised a significant $40 million Series B growth round led by major healthcare technology funds to expand its autonomous medical coding footprint.

Targeted Technical Solution: CodaMetrix builds specialized machine learning models that translate multi-specialty clinical text into verified ICD and CPT billing codes, reducing manual review backlogs for complex procedures.

This targeted investment demonstrates that large healthcare systems are actively looking to purchase software that replaces fragile, rule-based legacy applications with deep, clinical semantic understanding.

3. SmarterDx Wins Revenue Integrity Capital

SmarterDx has emerged as another highly funded player in the prebill optimization space, capturing a $50 million Series B funding round led by Transformation Capital. This injection brings their total funding to over $71 million, establishing the platform as a core asset for hospital networks looking to audit their clinical data before bills are sent out.

• The Second-Layer Audit: SmarterDx uses its proprietary AI engine to process over 30,000 data points per chart, performing a deep second-level review of 100% of patient encounters.
• Measurable Returns: The platform routinely surfaces roughly $2 million in hidden, legitimate revenue per 10,000 patient discharges, providing an immediate return on investment for health networks.

Build an AI Revenue Software with Idea Usher

Deploying capital into healthcare technology requires a partner who understands the high stakes of enterprise software. Hospital groups do not purchase standalone experiments. They buy reliable systems that protect their cash flow while matching strict regulatory demands.

We bring the deep technical expertise and compliance-first engineering required to build production-ready software assets that capture serious enterprise market share.

Design Around Real Hospital Workflows

Successful AI revenue software should fit naturally into existing healthcare workflows instead of forcing teams to change how they work. We design platforms that integrate with coding, documentation, and billing processes, helping providers automate routine tasks, improve productivity, and achieve faster adoption with minimal disruption.

Enterprise AI With Healthcare Compliance

Healthcare data demands top-tier engineering standards. A weak infrastructure layout will immediately disqualify an application during hospital security reviews, destroying potential contract value.

• Modern Interoperability: We build utilizing modern communication protocols like FHIR and HL7, ensuring smooth data exchanges with systems like Epic and Cerner.
• Security Architecture: Every platform layer is engineered for absolute compliance, using data masking, access logging, and structures ready for HIPAA and SOC 2 audits.
• Elite Engineering Force: With over 500,000 hours of coding experience, our team of ex-MAANG/FAANG developers and 250+ technical experts constructs cloud-native AI platforms that handle high data volumes effortlessly.

This technical backbone ensures your product passes enterprise procurement checks, allowing you to pitch major hospital groups with total confidence.

Scale From MVP to Full Automation

Launching with a focused MVP helps businesses enter the healthcare market faster and validate their product with lower upfront investment. We recommend starting with high-impact features such as AI-assisted medical coding or claims management, then expanding the platform with advanced capabilities like clinical documentation improvement, authorization automation, and predictive denial analytics as customer needs evolve.

Conclusion

Building an AI revenue software like AKASA requires more than adding AI to existing billing workflows. Success comes from combining healthcare-specific intelligence, seamless system integrations, and a platform that delivers measurable financial results for providers. By starting with the right features and expanding over time, businesses can create a scalable solution that reduces administrative burden, improves reimbursements, and meets the growing demand for AI-powered revenue cycle management.

Things to Know About AI Revenue Softwares