TabibAI is a clinical workflow platform powered by a hybrid stack of frontier and open-weight AI models — designed for clinics and telemedicine teams to collect patient context, organize uploaded files, draft physician-ready notes, and keep clinicians in control. Estimated ~70% reduction in documentation time.
A focused suite instead of a generic chatbot. Each module supports a real clinic workflow and keeps final medical judgment with the clinician.
AI-powered guided intake flows for symptoms, history, medications, allergies, and visit goals — targeting intake completion in under 3 minutes (down from ~15).
Turns uploaded medical documents into source-aware summaries for faster human review — supporting 4+ document categories including lab reports, prescriptions, referral letters, and imaging notes.
Prepares structured SOAP-style notes for clinicians to edit, approve, or reject before they enter the record — saving an estimated 8–12 minutes per consultation.
Central queue for intake sessions, uploaded files, draft notes, and review status — designed for teams of 1 to 50+ clinicians with role-based access control.
Clear boundaries for medical AI: no diagnosis, no prescribing, and no autonomous care decisions — every output carries explicit draft labels and clinical review requirements.
Enterprise-grade architecture for hosted multi-model AI inference, document processing, secure storage, and audit logs — designed for HIPAA-aware deployment on Google Cloud and major cloud providers.
TabibAI routes each task across frontier models for complex reasoning and open-weight models for high-throughput extraction and summarization, supported by a specialized medical knowledge layer.
OpenAI's flagship model for reasoning-heavy clinical note drafting and complex intake synthesis.
Anthropic's frontier model for nuanced medical document comprehension and source-aware summarization.
Google's multimodal model for structured intake processing and multi-format data extraction.
Llama, DeepSeek, and Qwen models served on dedicated high-throughput inference for structured extraction and summarization.
Quantifiable impact across the TabibAI clinical workflow platform.
Estimated reduction in documentation time per visit¹
Estimated time saved per consultation through AI-assisted note preparation
Medical document categories supported for AI summarization
Estimated AI-powered intake completion time (down from ~15 min)
Clinicians supported per workspace with role-based access
Availability for intake collection and document processing
TabibAI integrates established medical standards and clinical reference databases to ground AI outputs in evidence-based medicine.
SNOMED-CT and ICD-10 coding references for structured clinical vocabulary and standardized diagnosis categorization.
RxNorm drug terminology and drug-drug interaction screening to flag potential medication conflicts for clinician review.
Evidence-based clinical practice guidelines integrated to align AI-assisted draft recommendations with established medical protocols.
A real cloud-backed workflow with responsible healthcare positioning and clinician-controlled outputs.
Patients submit history and documents before the visit through controlled forms.
Reports and free-text answers are summarized into clinician-reviewable context.
The system prepares draft notes and missing-information prompts.
A professional reviews, edits, and finalizes; TabibAI does not replace judgment.