Generative AI Development Company
Arkenea is a generative AI development company that has worked exclusively in healthcare since 2011. We design, build, and support HIPAA compliant LLM applications, RAG systems, and AI assistants for health systems, digital health startups, and medtech companies across the United States.
Request a QuoteSome of Our Key Healthcare and Medical Clients
Healthcare Is All We Do
A Generative AI Development Company That Only Builds for Healthcare
Most generative AI agencies serve a dozen industries at once. Arkenea has spent 15 years working only in healthcare, and that focus changes what gets built. Our team knows what a prior authorization request looks like inside a payer portal, why a clinician abandons software that adds clicks to a patient encounter, and what an auditor asks for when reviewing how an application handles protected health information.
The opportunity is substantial. McKinsey's analysis of generative AI in healthcare points to meaningful gains across clinical documentation, member services, and administrative workflows. Capturing those gains in production takes more than a working demo. It takes software that holds up under clinical scrutiny, connects to the systems your teams already use, and satisfies HIPAA from the first architectural decision. That is the standard we have applied to custom healthcare software development since 2011, and it is the standard we apply to every generative AI engagement.
What We Build
Our Generative AI Development Services
From the first strategy conversation through years of production support, Arkenea covers the full lifecycle of generative AI development. Every service below is delivered by a US based team that works only on healthcare projects.
Generative AI Consulting and Strategy
We help you decide where generative AI belongs in your product or operations, and where it does not. Our AI consulting engagements produce a prioritized use case roadmap, a data readiness assessment, and a build plan with defined costs before you commit to development.
Custom LLM Application Development
We build applications on large language models, from clinician facing copilots to patient engagement tools, designed around your existing workflows rather than bolted onto them. Each application ships with evaluation pipelines that measure accuracy on your data, not on generic benchmarks.
Retrieval Augmented Generation Systems
RAG systems ground model output in your own clinical content, policies, formularies, and knowledge bases, so answers draw on your approved sources instead of the model's general training. This is the architecture we recommend most often for clinical and member facing tools.
Model Fine Tuning and Domain Adaptation
When retrieval alone is not enough, we adapt foundation models to your specialty's terminology, document formats, and tone using supervised fine tuning on curated datasets, with careful controls on what data is used and where the resulting model runs.
AI Chatbots and Virtual Assistants
Patient intake assistants, appointment and medication reminders, benefits navigation, and internal staff support. Our guide to healthcare chatbots covers what these systems can and cannot safely do, and we build them with escalation paths to human staff from day one.
Ambient Clinical Documentation
Tools that listen to the patient encounter and draft the note, so clinicians review and sign instead of typing. We design these systems around specialty specific note structures and the review step clinicians need to trust the output.
EHR and Systems Integration
Generative AI features are only useful inside the systems where work happens. We integrate with EHRs through FHIR and HL7, including experience with Epic, Athenahealth, and Oracle Health, building on 13+ years of EHR development and integration work.
Adding AI to Existing Software
You do not need to rebuild your product to add generative AI. We retrofit AI features into live healthcare applications, an approach we outline in how to add AI to your existing healthcare software, without disrupting the users who depend on it today.
Maintenance, Monitoring, and Optimization
Models drift, vendor APIs change, and usage patterns evolve. We monitor accuracy, latency, and cost per interaction in production, and we tune prompts, retrieval, and model choice as newer models arrive so quality does not quietly degrade.
Compliance as Architecture
HIPAA Compliant Generative AI Development
HIPAA compliance at Arkenea is an architectural decision made at the start of a project, not a checkbox reviewed at the end. Before any code is written, we map every point where protected health information enters, moves, or rests in the proposed system, and we design the AI architecture around those flows.
How PHI Is Protected in LLM Applications
We use HIPAA eligible model deployments such as Azure OpenAI, AWS Bedrock, and Google Cloud Vertex AI, with Business Associate Agreements in place with every vendor in the chain. PHI is never used to train vendor models, is excluded from vendor logging where the platform allows it, and is encrypted in transit and at rest.
Aligned With the HHS Security Rule
Access controls, audit logging, and integrity safeguards follow the HHS HIPAA Security Rule. Every AI feature we ship logs who asked what, what the model returned, and what data it touched, which is exactly what your compliance team will need during an audit.
Data Minimization and Deidentification
The model receives only the data the task requires. Where the workflow allows it, we deidentify inputs before they reach the model and rehydrate identifiers afterward, which shrinks both your risk surface and your compliance burden.
Arkenea signs Business Associate Agreements with clients, and you own the code we write for you. If a generative AI vendor cannot explain where your PHI goes, that is a signal to keep looking.
Where It Pays Off
What Can Generative AI Do for a Healthcare Organization?
In production healthcare settings, generative AI is delivering value today in clinical documentation, administrative automation, patient communication, and knowledge retrieval. These are the use cases we see producing returns most consistently, drawn from the broader set we cover in our guide to generative AI in healthcare.
Ambient Clinical Documentation
Drafting encounter notes from the clinical conversation so physicians review instead of type. Documentation burden is a leading driver of clinician burnout, and this is currently the most widely adopted generative AI use case in care delivery.
Prior Authorization and Payer Workflows
Assembling clinical documentation, drafting authorization requests, and checking them against payer policy criteria before submission. The hours clinical staff spend on this work are among the easiest to give back.
Medical Coding Support
Suggesting diagnosis and procedure codes from clinical documentation for coder review, with the supporting text highlighted. Coders stay in control while spending less time searching and more time validating.
Patient Intake and Triage Assistants
Collecting history, answering common questions, and routing patients to the right care setting before the visit, with clear escalation to staff whenever the conversation moves beyond the assistant's scope.
Clinical Knowledge Automation
Assembling drug monographs, formulary reviews, and literature summaries from sources such as PubMed, NIH, and FDA data. We have built this class of system in production for clinical pharmacy teams.
Revenue Cycle Workflows
Analyzing denial patterns, drafting appeal letters grounded in the payer's own policy language, and flagging claims with missing documentation before submission rather than after rejection.
Model Agnostic by Design
AI Models and Technology Stack We Work With
We are not tied to any single model vendor. In a regulated setting, model selection comes down to five questions: accuracy on your specific task, how the deployment handles PHI, where the model can run, cost per interaction at your volume, and how auditable the output is. We benchmark candidate models against your data during discovery and recommend accordingly.
Model Families
OpenAI GPT models, Anthropic Claude, Google Gemini, and Meta Llama, along with open weight models where private hosting is the right call. For PHI workloads we deploy through HIPAA eligible platforms with Business Associate Agreements in place.
Orchestration and Retrieval
LangChain and LlamaIndex for orchestration, with vector databases such as Pinecone and pgvector powering retrieval over your clinical content, policies, and documents.
Cloud and Deployment
Azure, AWS, and Google Cloud under Business Associate Agreements, plus private and on premise hosting of open weight models when your data governance requires that nothing leaves your environment.
Evaluation and Safety Tooling
Curated test sets built from your workflows, automated accuracy and hallucination checks on every release, and human review queues for outputs that touch clinical decisions.
Proof, Not Promises
Generative AI and Intelligent Automation Case Studies
A sample of the AI and automation work we have shipped for healthcare clients. You can browse the full portfolio on our case studies page.
Kethan
AI First Implant Identification for Surgeons
Surgeons needed a faster, more reliable way to identify orthopedic implants from radiographic images, where misidentification causes confusion and procedural delays. Arkenea built an AI first iOS application with computer vision trained to recognize implant types regardless of image orientation, paired with an expandable implant database. The tool cuts the time and error involved in implant identification and was designed so clinicians with no AI background can use it confidently. Read the Kethan case study.
Formulary Insights
Clinical Knowledge Automation for Pharmacy Teams
Clinical pharmacists were assembling drug class reviews by hand from scattered online sources, a slow and inconsistent process. Arkenea built a platform that automatically populates drug monographs with current content from PubMed, NIH, and FDA sources, with customizable monograph formats and a workflow for ongoing reviews. The product now serves insurers and clinical pharmacists as a subscription service. Read the Formulary Insights case study.
TruMedical
Intelligent Insurance Compliance Automation
A medical practice was manually processing insurance compliance data from multiple insurers, an error prone routine that demanded constant vigilance. Arkenea built a web application that ingests insurer data automatically, extracts what matters, and proactively flags changes in patient compliance status. Manual data entry was eliminated and the practice team now intervenes on compliance changes as they happen. Read the TruMedical case study.
Beyond AI specific work, healthcare teams from Fortune 500 pharma to physician founded startups have trusted Arkenea with their products, including Novo Nordisk, NPHub, ORLink, and HomeHosp.
Discovery First, Always
Our Generative AI Development Process
Every Arkenea engagement begins with a paid discovery phase that produces a functional specification before any fixed price is quoted. You know exactly what will be built and what it will cost before committing to development, which is how we avoid the scope surprises that derail AI projects and budgets elsewhere.
Discovery and Functional Specification
We define the use case, map your data and PHI flows, assess feasibility against your compliance requirements, and produce a functional specification. Fixed pricing is quoted from this document, not from a guess.
UI and UX Design
Prototypes designed around clinical and administrative workflows as they actually run, tested with the people who will use the software before development begins.
Data Architecture and Model Selection
We benchmark candidate models on your data, design the retrieval and grounding architecture, and lock the deployment approach that satisfies your PHI and governance requirements.
Application Development
Agile development in short cycles with working software you can react to, covering the application, the AI pipeline, and the integrations in parallel.
Evaluation and Accuracy Testing
Output is tested against curated evaluation sets built from your real workflows, with hallucination checks and clinician review loops before anything reaches production users.
Compliance Implementation
Business Associate Agreements, access controls, audit logging, encryption, and security testing are implemented and verified against the specification from Step 1.
Deployment and Integration
Rollout into your environment and the systems your teams already use, including EHR integration through FHIR and HL7 where the workflow calls for it.
Monitoring, Support, and Optimization
Production monitoring of accuracy, latency, and cost, with ongoing tuning of prompts, retrieval, and model choice as usage grows and better models become available.
Start With Discovery, Not a Leap of Faith
A paid discovery phase gives you a functional specification, a fixed price, and a clear view of feasibility before you commit to a build. It is the least risky way to start a generative AI project.
Request a QuoteThe Difference Focus Makes
Why Choose Arkenea as Your Generative AI Development Company
Healthcare Exclusivity Since 2011
We have never taken a project outside healthcare. That means the terminology, the workflows, the payer dynamics, and the regulatory constraints are already understood on day one of your project, not learned at your expense.
Recognized Three Years Running
Arkenea won the GHP Global Excellence Award for Best Custom Healthcare Software Development Company (East Coast USA) in 2024, 2025, and 2026, alongside a 4.9 client rating on Clutch.
Discovery First Engagement Model
A functional specification before fixed pricing means you are never billed against a moving target. Projects that skip this step routinely blow through budgets when the scope turns out to be bigger than the pitch.
Accuracy and Safety Safeguards
Evaluation pipelines, hallucination testing, and human review for clinically sensitive outputs are part of our standard process, not premium extras. In healthcare, a fluent wrong answer is worse than no answer.
You Own the Code
Full ownership of the code we write transfers to you, and we sign Business Associate Agreements as standard practice. Your product and your data remain yours.
A Partner Beyond the Build
Generative AI systems need attention after launch. Clients stay with us for monitoring, optimization, and new feature development, which is why relationships like our work with Novo Nordisk span years rather than a single release.
Answers Up Front
Frequently Asked Questions About Generative AI Development
What does a generative AI development company do?
A generative AI development company designs, builds, and maintains software powered by AI models that generate text, images, or structured output, including LLM applications, RAG systems, chatbots, and copilots. The work spans strategy and use case selection, data preparation, model selection and fine tuning, application development, integration with existing systems, and production monitoring. Arkenea provides all of these services with an exclusive focus on healthcare.
How much does it cost to develop a generative AI solution?
Most generative AI development projects at Arkenea fall between $50,000 and $250,000, with focused pilots at the lower end and production systems spanning multiple workflows above it. Because every engagement starts with a paid discovery phase that produces a functional specification, the fixed price you receive is grounded in defined scope rather than an estimate that grows midway through the build.
How long does generative AI development take?
A focused pilot typically ships in 8 to 12 weeks, while production systems with EHR integration usually take 4 to 8 months. Discovery itself runs a few weeks and gives you a reliable timeline for your specific project before development starts.
How do you keep PHI secure when using large language models?
PHI is processed only through HIPAA eligible deployments such as Azure OpenAI, AWS Bedrock, and Google Cloud Vertex AI, with Business Associate Agreements covering every vendor in the chain. Client data is never used to train vendor models, inputs are minimized or deidentified where the workflow allows, and access controls and audit logging follow the HHS Security Rule.
Can generative AI integrate with our existing EHR and software systems?
Yes. We integrate generative AI features with EHRs through FHIR and HL7, with experience across Epic, Athenahealth, and Oracle Health, and we regularly add AI capabilities to clients' existing applications without rebuilding them. Integration scope is defined during discovery so there are no surprises about what connects to what.
Which AI models do you build with, and how do you choose?
We work with OpenAI GPT models, Anthropic Claude, Google Gemini, Meta Llama, and open weight models for private hosting, and we are not tied to any vendor. During discovery we benchmark candidate models on your actual task and data, then weigh accuracy, PHI handling, deployment options, cost per interaction, and auditability to recommend the best fit.
What data do we need to get started?
You do not need perfectly organized data to start a generative AI project. Discovery includes a data readiness assessment that identifies what you have, what condition it is in, and what the chosen use case actually requires, which is often less than teams expect, particularly for retrieval based architectures that work with your existing documents.
How do you control hallucinations and ensure accuracy?
We ground model output in your approved content through retrieval, test every release against curated evaluation sets built from your workflows, and route clinically sensitive outputs through human review. Accuracy is measured continuously in production, not assumed after launch, and thresholds are agreed with your clinical and compliance stakeholders during discovery.
Do you work with organizations outside healthcare?
No. Arkenea works exclusively with healthcare organizations, including health systems, medical practices, digital health startups, medtech companies, and pharma. That exclusivity since 2011 is precisely why our generative AI work holds up in clinical and regulated settings.
How do we get started with Arkenea?
Request a quote through our contact form and we will set up an introductory call to understand your goals. From there, we scope a discovery phase, and you will have a functional specification and a fixed price proposal before making any commitment to development.
Looking for a Generative AI Development Company That Understands Healthcare?
Tell us what you are trying to build. We will bring 15 years of healthcare software experience, a discovery first process, and a team that has already shipped AI in clinical settings.
Request a Quote