Ambient clinical scribe
Capture the patient-clinician conversation, transcribe it securely and draft a structured note in the EHR that the clinician reviews, edits and signs before it enters the record.
Voice AI & SpeechAI for Healthcare
AI in healthcare that clinicians trust.
We build AI in healthcare that lightens documentation, speeds up triage and gives patients clearer access to care. Every system plugs into your EHR through FHIR and HL7, keeps clinicians in the loop and treats protected health information with the care it demands.
- FHIR and HL7 ready
- Clinician-in-the-loop design
- PHI-aware architecture
01 Overview
Practical healthcare AI that respects clinical workflows
Clinicians spend a large share of each shift typing notes, chasing prior authorizations and searching charts for context. Our healthcare AI development work targets that friction first: ambient scribing that drafts SOAP notes, retrieval assistants that surface relevant history from the chart, and coding suggestions that a human coder confirms before anything is billed.
We treat ai in healthcare as clinical decision support, never a replacement for judgment. Models are validated against your own patient population, outputs show their sources, and every recommendation lands inside the EHR screen a nurse or physician already uses. Audit logs, role-based access and de-identification pipelines are part of the first sprint, not an afterthought.
02 Challenges
What AI solves in healthcare.
The recurring problems we hear from healthcare teams — and where AI makes the biggest difference.
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CH-01
Documentation burden drives burnout
Physicians finish notes late at night because templated EHR fields are slow to fill. Speech-driven drafting and smart summarization return that time to patient care and rest.
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CH-02
Unstructured data hides clinical insight
Discharge summaries, pathology reports and faxed referrals bury key facts in free text. Clinical NLP extracts diagnoses, medications and follow-ups into structured fields teams can actually query.
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CH-03
Radiology and pathology backlogs
Imaging volumes outpace reading capacity. Computer vision can prioritize studies with suspected critical findings so radiologists open the most urgent scans first, while still reading every study themselves.
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CH-04
Revenue cycle leakage
Missed codes, incomplete documentation and slow denials management erode margins. AI reviews encounters for coding gaps and drafts appeal letters that billing staff verify before submission.
03 Use cases
AI use cases for healthcare.
Proven applications we design, build and run in production — each delivered through one of our core services.
Chart summarization for handoffs
Retrieval-augmented assistants pull labs, imaging impressions and medication changes from the chart and produce a concise shift-handoff summary with links back to each source document.
RAG DevelopmentImaging triage worklists
Vision models score incoming CT, X-ray or MRI studies for suspected urgent findings and reorder the PACS worklist, so time-sensitive cases reach a radiologist sooner.
Computer VisionPatient access and scheduling chatbot
A multilingual assistant answers coverage questions, books and reschedules appointments against real provider availability and hands off to staff whenever symptoms or billing disputes come up.
AI Chatbot DevelopmentReadmission and sepsis risk scoring
Predictive models trained on historical encounters flag patients at elevated risk so care managers can schedule follow-up calls or escalate monitoring, with explanations attached to each score.
Predictive AnalyticsPrior authorization automation
Agents gather required clinical evidence from the chart, assemble payer-specific forms and track submission status, routing every packet to a staff member for final approval.
Intelligent Automation04 Services
AI services for healthcare.
RAG Development
Retrieval-augmented generation grounded in your private data.
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Natural Language Processing
NLP for classification, extraction, summarization and search.
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Computer Vision
Image and video AI for detection, inspection and recognition.
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Voice AI & Speech
Speech recognition, voice agents and text-to-speech.
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Predictive Analytics
Forecasting, churn, demand and risk models.
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AI Integration
Embed AI into your existing CRM, ERP, apps and data stack.
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05 How we deliver
From use case to production.
Every healthcare AI project follows the same five stages, so you validate value early and scale with confidence.
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01
Discover Typically 1–2 weeks
AI readiness assessment, data audit and use-case discovery. We rank opportunities by business value and feasibility and agree on measurable success criteria.
Output: Roadmap · data audit · success metrics
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02
Prototype Typically 2–4 weeks
A working proof of concept on your real data, scored against an evaluation set — so you see accuracy, latency and cost before committing to a full build.
Output: PoC · evaluation report · build estimate
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03
Build Sprint-based
Production engineering: data pipelines, model training or fine-tuning, RAG retrieval, agent tooling, APIs, guardrails and the user interface your team will actually use.
Output: Production code · APIs · guardrails
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04
Deploy Cloud, on-prem or edge
Secure deployment on AWS, Azure, Google Cloud, your private infrastructure or edge devices, with CI/CD, LLMOps and MLOps pipelines from the first release.
Output: CI/CD · infrastructure as code · rollback
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05
Operate Ongoing
Monitoring, drift detection, retraining and cost optimization keep models accurate and affordable as your data, users and the AI landscape change.
Output: Monitoring · retraining · cost reports
06 Built for your sector
Designed for healthcare realities.
Data, regulation and the systems you already run shape every architecture decision we make.
PHI and HIPAA requirements
Systems are designed around HIPAA requirements: encryption in transit and at rest, business associate agreements with model vendors, minimum-necessary data access, de-identification for training sets and full audit trails of who saw what.
EHR and FHIR integration
We integrate with Epic, Oracle Health, athenahealth and other EHRs through FHIR APIs, HL7 v2 feeds and SMART on FHIR launch contexts so AI output appears inside existing clinical screens.
Clinical validation and oversight
Models are tested on local data for accuracy and bias across patient groups, monitored for drift after go-live and governed by a clinical review committee that can pause any feature.
How is AI used in healthcare today?
The most mature uses are ambient documentation, imaging triage, patient scheduling assistants, coding support and risk prediction for readmissions or deterioration. In each case the AI drafts, ranks or flags, and a licensed clinician or trained staff member makes the final call. That keeps accountability where it belongs while removing repetitive work.
Can your healthcare AI integrate with Epic or Oracle Health?
Yes. We build against FHIR R4 APIs, HL7 v2 interfaces and SMART on FHIR app launch, which are the standard integration paths for major EHRs. Where an EHR requires marketplace review or vendor approval, we plan that process into the project and work alongside your interface team.
How do you protect patient data when using large language models?
We use deployment options that keep PHI inside your cloud tenancy or under a signed business associate agreement, strip identifiers where they are not needed, block prompts and outputs from being used for vendor training and log every access. Architecture is designed around HIPAA requirements and reviewed with your privacy officer.
Does clinical AI need FDA clearance?
It depends on intended use. Administrative tools such as scheduling or documentation drafting usually fall outside device regulation, while software that diagnoses or drives treatment may be regulated as a medical device. We help you scope features so regulatory questions are identified early and reviewed with qualified regulatory counsel.
How do you prevent AI from giving unsafe medical advice?
Patient-facing assistants are constrained to approved content, refuse to diagnose and escalate symptom questions to clinical staff. Clinician-facing tools cite the chart sources behind every answer. We red-team prompts before launch, monitor conversations afterward and give clinical leadership controls to adjust or disable behavior.