AI · Healthcare · Full Stack
Clinico
AI
An AI-powered medical scribe that listens to doctor–patient conversations, generates structured clinical documentation, and enables context-aware questions through RAG-powered AI.
Type
AI / Healthcare
Role
Full-Stack & AI Engineer
Focus
RAG · AI Agents · Clinical Documentation
01 / Overview
Turning conversations into structured clinical intelligence.
Clinico AI is an AI-powered medical scribe designed to reduce the administrative burden associated with clinical documentation.
The system records doctor–patient conversations, converts speech into text using AssemblyAI, and processes the conversation through AI pipelines to generate structured clinical notes.
Doctors can select or create custom documentation templates, including SOAP notes, discharge summaries, referrals, and specialty-specific workflows.
The system also provides context-aware question answering, allowing clinicians to query previous conversation content using retrieval-augmented generation.
02 / Problem
Clinical documentation is expensive in time.
Doctors spend a significant portion of their working day documenting patient encounters instead of focusing entirely on patient care.
Manual documentation is repetitive, time-consuming, and can introduce inconsistencies or omissions.
Clinico AI was designed to automate this workflow while allowing clinicians to remain in control of the structure and content of the generated notes.
03 / Solution
From conversation to structured clinical documentation.
Record
Capture doctor–patient conversations through the clinician interface.
Transcribe
Convert the recorded conversation into text using AssemblyAI.
Generate
Process the transcript through AI agents and RAG pipelines to generate structured notes.
Query
Allow clinicians to ask context-aware questions about the conversation.
04 / Architecture
Modular AI architecture.
Clinico AI separates the clinician interface, retrieval layer, AI processing pipeline, transcription service and persistence layer into modular components.
05 / AI Pipeline
Combining agents, retrieval and structured generation.
Retrieval-Augmented Generation
Conversation data is embedded and stored using Supabase pgVector so relevant context can be retrieved when clinicians ask follow-up questions.
LangGraph Agents
LangGraph coordinates AI workflows and allows the system to break complex documentation tasks into structured processing steps.
Quality Checking
Generated clinical notes pass through a quality checking layer designed to evaluate completeness, factual consistency and safety before being presented to the clinician.
06 / Technology
07 / Engineering
Engineering decisions.
Modular Services
The frontend, retrieval layer and AI processing components are separated so individual parts of the system can evolve independently.
Vector Search
pgVector provides semantic retrieval over conversation data, allowing the Q&A system to retrieve relevant context instead of relying only on the current prompt.
Custom Documentation Templates
Instead of forcing every clinician into a fixed output format, the system allows templates to define how generated documentation should be structured.
Infrastructure as Code
Docker and Terraform provide reproducible environments and make the infrastructure easier to manage and deploy consistently.
08 / Impact
50–60%
Documentation time reduction
30%
Fewer note-taking errors
1–2h
Less after-hours work
RAG
Context-aware retrieval