Custom AI Solutions Built and Managed for You
We design, develop, and deploy bespoke AI solutions tailored to your unique requirements. Full ownership of code and infrastructure. Best for enterprises with complex needs requiring custom development. Pilot strongly recommended before committing to full build.
Duration
3-9 months
Investment
$150,000 - $500,000+
Path
b
Telehealth providers face unique AI requirements that off-the-shelf solutions cannot address: real-time clinical decision support integrated with proprietary encounter workflows, multimodal patient data analysis across video/audio/text streams, HIPAA-compliant conversational AI that understands specialty-specific terminology, and predictive models trained on your patient population demographics. Generic healthcare AI tools lack the depth needed for your specific clinical protocols, state licensing requirements, and integration with specialized platforms like Doxy.me, Teladoc infrastructure, or custom EHR implementations. Building proprietary AI capabilities enables differentiation in crowded markets—whether through superior diagnostic accuracy, automated clinical documentation, or proactive patient engagement that reduces no-shows and improves outcomes. Custom Build delivers enterprise-grade AI systems architected specifically for telehealth operational requirements. Our engagements ensure HIPAA and HITECH compliance through encryption, audit logging, and BAA-compliant infrastructure on AWS/Azure/GCP healthcare-certified environments. We design for the scale demands of telehealth—handling concurrent video sessions, real-time transcription, and sub-200ms response times for clinical AI assistants. Full integration with existing systems includes HL7/FHIR interfaces to EHRs, bidirectional sync with practice management platforms, SSO authentication, and webhook-based workflows. Production deployment includes model monitoring, A/B testing frameworks, automated retraining pipelines, and 99.9% uptime SLAs with failover capabilities critical for patient care continuity.
Real-Time Clinical Documentation AI: NLP system that listens to patient-provider video consultations, extracting symptoms, diagnoses, and treatment plans to auto-generate SOAP notes in your EHR format. Architecture includes speech-to-text with medical vocabulary customization, transformer-based clinical entity extraction, and FHIR-compliant API integration. Reduces documentation time by 40% while improving billing code accuracy.
Intelligent Patient Triage and Routing Engine: Multi-model system combining symptom analysis, urgency classification, and provider matching based on specialty, availability, and patient history. Uses gradient boosting for severity scoring, recommendation algorithms for optimal provider assignment, and integration with scheduling systems. Decreased wait times by 35% and improved first-visit resolution rates by 28%.
Proactive Patient Engagement Platform: Predictive system identifying patients at risk of appointment no-shows or care gaps using historical behavior, social determinants, and clinical factors. Deploys personalized outreach via SMS/email with LLM-generated, culturally-appropriate messaging. Integrated with Twilio and your CRM. Reduced no-show rates from 23% to 11% and increased chronic care follow-up adherence by 45%.
Multimodal Diagnostic Support System: Computer vision and audio analysis AI assisting providers during dermatology, ENT, or behavioral health video visits. Includes image classification for skin conditions, acoustic analysis for respiratory symptoms, and affect recognition for mental health assessments. Deployed as HIPAA-compliant browser extension with real-time inference. Improved diagnostic confidence scores by 32% and reduced specialist referral delays.
We architect systems with HIPAA compliance from day one—implementing end-to-end encryption (in-transit and at-rest), comprehensive audit logging, role-based access controls, and automatic PHI detection with masking capabilities. All infrastructure resides in BAA-compliant cloud environments with dedicated security reviews at each development milestone. We execute BAAs before any PHI touches development environments and provide full compliance documentation for your security audits.
Custom Build excels at integrating with specialized and legacy systems that off-the-shelf AI cannot support. We reverse-engineer APIs, build custom connectors for proprietary databases, and work with HL7 v2/v3, FHIR, CCD, and even screen-scraping when necessary. Our architects have integrated with 40+ telehealth platforms and EHR systems including athenahealth, DrChrono, SimplePractice, and fully custom builds.
Timeline depends on scope: simpler systems like intelligent chatbots or documentation assistants deploy in 3-4 months, while complex diagnostic or predictive models require 6-9 months including clinical validation and staged rollout. We prioritize MVP deployment with core functionality in the first 90 days, then iterate with additional capabilities. Every engagement includes a detailed roadmap with milestone-based delivery and early production pilots to demonstrate value quickly.
We architect for horizontal scalability using containerized microservices, auto-scaling infrastructure, and model serving frameworks that handle traffic spikes without degradation. Systems include specialty-agnostic frameworks where adding new clinical domains requires retraining modules rather than rebuilding architecture. All deployments include load testing to 10x current volume, and we provide runbooks for scaling infrastructure as your telehealth practice grows.
You retain complete ownership of all custom code, trained models, and intellectual property—including commercial rights to any AI innovations. We deliver comprehensive technical documentation, architecture diagrams, model cards, and training for your engineering team to maintain and enhance systems independently. Deployments use open-source frameworks (PyTorch, TensorFlow, Hugging Face) and standard cloud services to ensure portability, with optional ongoing support contracts rather than mandatory dependencies.
A regional telehealth network serving 125,000 patients across behavioral health and primary care faced 22% no-show rates and provider burnout from documentation overhead. We built a dual AI system: (1) predictive engagement engine analyzing 18 months of appointment data, EHR records, and social determinants to identify at-risk patients and trigger personalized outreach, and (2) ambient clinical documentation AI transcribing sessions and auto-generating therapy notes with DSM-5 alignment. The system integrated via FHIR APIs with their Nextgen EHR and Mend telehealth platform, deployed on Azure Healthcare APIs with 99.95% uptime. Within 6 months post-deployment, no-show rates dropped to 9%, provider documentation time decreased 38 minutes per day, and patient satisfaction scores increased 23 points—enabling the network to serve 3,200 additional patients annually with existing clinical staff.
Custom AI solution (production-ready)
Full source code ownership
Infrastructure on your cloud (or managed)
Technical documentation and architecture diagrams
API documentation and integration guides
Training for your technical team
Custom AI solution that precisely fits your needs
Full ownership of code and infrastructure
Competitive differentiation through custom capability
Scalable, secure, production-grade solution
Internal team trained to maintain and evolve
If the delivered solution does not meet agreed acceptance criteria, we will remediate at no cost until criteria are met.
Let's discuss how this engagement can accelerate your AI transformation in Telehealth Providers.
Start a ConversationTelehealth providers deliver remote medical consultations, digital diagnostics, and virtual healthcare services across specialties using video conferencing and health monitoring technology. The sector has experienced rapid growth driven by changing patient expectations, regulatory reforms, and the need for accessible care in underserved areas. Providers range from dedicated telehealth platforms to traditional healthcare systems expanding their digital service delivery. AI enhances diagnostic accuracy through symptom analysis algorithms, personalizes treatment recommendations based on patient history and outcomes data, automates triage to route patients to appropriate care levels, and optimizes appointment scheduling to maximize provider utilization. Computer vision assists in dermatology assessments and wound monitoring, while natural language processing enables automated documentation and extracts insights from patient narratives. Predictive analytics identify patients at risk of deterioration requiring escalated care. Key technologies include diagnostic decision support systems, conversational AI for patient intake, ambient clinical intelligence for automated note-taking, and remote patient monitoring integration with real-time alert systems. Machine learning models continuously improve accuracy as they process more clinical encounters. Telehealth providers face challenges including provider burnout from documentation burden, scalability constraints during demand spikes, inconsistent diagnostic quality across providers, and patient engagement gaps between appointments. Many struggle with integrating fragmented data sources and demonstrating clinical outcomes to payers. Digital transformation opportunities center on automating administrative workflows, implementing AI-powered triage to optimize resource allocation, deploying clinical decision support to standardize care quality, and utilizing predictive analytics for proactive patient outreach. Telehealth platforms using AI improve diagnostic precision by 60%, reduce wait times by 70%, and increase patient satisfaction by 65%.
Timeline details will be provided for your specific engagement.
We'll work with you to determine specific requirements for your engagement.
Every engagement is tailored to your specific needs and investment varies based on scope and complexity.
Get a Custom QuoteIndonesian Healthcare Network implemented AI diagnostic imaging across their telehealth platform, achieving 45% faster diagnosis turnaround and 89% diagnostic accuracy rate across 50,000+ remote consultations.
Oscar Health deployed AI-driven insurance operations that reduced claims processing costs by 60% and decreased member service response times by 75%.
Ping An's AI Healthcare Platform serves over 400 million users with 92% patient satisfaction, demonstrating that AI-enabled telemedicine can maintain high care quality at massive scale.
AI handles pre-visit intake, symptom assessment, and post-visit education, allowing providers to spend their limited video time on diagnosis, treatment planning, and empathetic connection. Patients get faster access to care while providers focus on clinical judgment, not data collection.
Yes. AI ambient documentation generates visit notes that include all required elements for E/M coding (history, exam, medical decision-making) plus quality metric documentation. The AI shows its work with timestamps and quotes, creating audit-ready records that often exceed human-documented notes in completeness.
Ambient documentation shows immediate ROI (30-60 days) through provider productivity gains—same providers see 20-30% more patients weekly. AI patient engagement pays back within 6-9 months through reduced no-shows, better medication adherence, and fewer preventable ED visits. Most telehealth platforms achieve full payback within 6-12 months.
AI improves accessibility for less tech-savvy patients by simplifying workflows—voice-based symptom checkers, automated appointment reminders via text/email, and post-visit instructions in plain language. For patients unable to use video, AI-powered phone triage provides many benefits while your human providers handle the actual consultation.
Yes. AI documentation ensures every visit meets medical necessity criteria for reimbursement, captures required quality metrics automatically, and generates data for value-based contract negotiations. As payers shift from fee-for-service to value-based care, AI-enabled outcome tracking becomes your competitive advantage.
Let's discuss how we can help you achieve your AI transformation goals.
""How do we ensure AI-assisted diagnoses meet standard of care requirements and don't increase malpractice liability for our providers?""
We address this concern through proven implementation strategies.
""Telehealth reimbursement varies by 50 states and hundreds of payers - can AI really navigate this complexity without creating more denials?""
We address this concern through proven implementation strategies.
""Our platform differentiates on provider quality and bedside manner - won't AI automation make us feel like a healthcare vending machine?""
We address this concern through proven implementation strategies.
""What happens when AI escalation rules fail and a serious condition gets treated via telehealth instead of being sent to ER?""
We address this concern through proven implementation strategies.
No benchmark data available yet.
