ClinicFrame vs Okara: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of ClinicFrame and Okara — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
ClinicFrame
ClinicFrame
Ambient AI medical scribe that transcribes visits in real time and delivers a structured SOAP note within seconds.
Key features
- Ambient Voice Capture: Runs on the clinician's laptop and records the patient visit in real time without requiring extra hardware, in person or on telehealth.
- Specialty-Trained Transcription: Reaches 96% transcription accuracy across 15+ medical specialties using models trained on specialty-specific clinical language.
- Structured Note Generation: Produces SOAP notes for medicine and DAP/BIRP notes for behavioral health seconds after the visit ends, plus configurable custom templates.
- EHR Copy-In: Notes can be pasted into Epic, Athenahealth, Cerner and Healthie in under 30 seconds, so it slots into existing charting workflows.
- HIPAA-Aware Handling: Audio is discarded after the note is generated and a signed Business Associate Agreement is available on request.
- Flat Monthly Pricing: Priced per clinician per month with no per-hour surcharges, replacing traditional human scribing services.
Best for
- Primary Care Documentation: A family physician sees back-to-back patients and finishes the day with SOAP notes already drafted, cutting after-hours charting time.
- Behavioral Health Notes: A therapist runs 50-minute sessions and gets DAP or BIRP notes generated automatically instead of writing them up between clients.
- Telehealth Visits: A remote clinician on a video call has the conversation transcribed and structured without a separate device or human scribe on the line.
- Multi-Specialty Clinics: A clinic covering cardiology, dermatology and psychiatry gets specialty-appropriate note formatting from a single tool.
- Scribe Cost Reduction: A practice that has been paying hourly for human scribes switches to a flat monthly software subscription.
Okara
Okara
Encrypted private AI chat with 20–30+ open-source and proprietary models, persistent shared memory, and secure workspaces for professional use.
Key features
- Multi-Model Support: Access 20–30+ open-source and proprietary models (examples include Llama, Qwen, DeepSeek, Kimi, OpenAI, Claude, Gemini) and choose the best model per task without managing model infrastructure.
- Encrypted Shared Memory: Persistent, encrypted conversation memory that preserves context across sessions while protecting user data and reducing the need to re-provide context.
- Hosted, No-Infra Setup: Managed platform removes the requirement to self-host or provision complex model infrastructure, letting teams use open-source models out of the box.
- Secure Workspaces: Team and workspace features designed for sensitive workflows, enabling controlled sharing, collaboration, and auditability for regulated environments.
- Vertical Solutions: Prebuilt configurations and compliance-focused tooling tailored for finance, government, and scientific research use cases handling confidential data.
- Tiered Model Access: Upgradeable access controls that allow organizations to unlock additional or premium models and manage which models are available to users.
- Encrypted, privacy-first chat interface for interacting with language models
- Support for 20–30+ open-source models (examples: Llama, Qwen, DeepSeek, Kimi)
- Persistent memory and context retention across sessions
- Prebuilt solutions and workflows for finance, government, and scientific teams
- Accessible without requiring users to manage model infrastructure
- High-performance workspace optimized for sensitive datasets and experiments
- Model selection/upgrade options to access additional models
- Open-source-powered backend components
Best for
- Private Financial Analysis: Analysts and accountants use encrypted chats with model selection to analyze sensitive financial data, generate reports, and run scenario planning without exposing client information.
- Government Decision Support: Public sector teams leverage secure workspaces and encrypted memory to draft policy notes, review documents, and collaborate on sensitive workflows while maintaining compliance.
- Research Collaboration: Scientists and labs store experiment context in encrypted shared memory, run literature synthesis and data summarization with preferred open-source models, and collaborate securely across teams.
- Secure Knowledge Management: Organizations retain private chat histories and context to build internal knowledge assistants that answer questions from proprietary documents without leaking data.
- Model Evaluation and Selection: Teams compare outputs across multiple open-source and proprietary models on the same prompts to select the best-performing model for specific tasks without infrastructure overhead.
- Financial analysts and accountants querying sensitive financial records with privacy guarantees
- Government officials and agencies needing encrypted, auditable AI-assisted workflows
- Research scientists managing datasets, experiments, and papers in a private workspace
- Teams that want multi-model experimentation without operating model infrastructure
- Professionals requiring persistent conversational context for complex tasks