Local clinical speech recognition
Process live or recorded speech using supported medical terminology, custom vocabulary, diarization, timestamps, punctuation, correction, and explicit model versions.
Healthcare & Telemedicine
Private Clinical AI & Medical Documentation
Run clinical speech recognition, private language models, approved medical knowledge, document intelligence, imaging-assistance workflows, and structured draft reporting inside infrastructure controlled by the healthcare organization.
Healthcare organizations should not have to send every conversation, document, image, prompt, transcript, or workflow to external AI services. OPTIME can engineer private AI systems that combine local STT, LLMs, retrieval, document models, computer vision, structured reporting, source evidence, and clinician review while allowing approved cloud models only where policy permits them.
WHY THIS MATTERS
Clinical AI must preserve source evidence, workflow context, and professional review.
A model-generated note or report is not automatically a clinical record.
Private deployment may also be required for audio, documents, images, embeddings, prompts, model requests, caches, logs, and administrative operations.
- patient and encounter context;
- terminology;
- source material;
- measurements;
- approved knowledge;
- permissions;
- uncertainty;
- corrections;
- evidence;
- review;
- responsibility for the final record.
Build private clinical speech, knowledge, documentation, and imaging-assistance workflows that produce evidence-backed drafts for authorized clinician review.
SYSTEM PRESSURES
Why simple implementations break down.
Sensitive information leaves the organization
Cloud AI may create data-placement and operational dependencies that do not fit the healthcare environment.
Medical terminology is mishandled
General-purpose speech and language systems may misinterpret specialized terms, abbreviations, measurements, or context.
Fluent drafts hide uncertainty
Summaries may remove disagreement, tentative language, missing information, or exceptions.
Imaging assistance lacks workflow control
Model observations may be disconnected from measurements, images, evidence, report structure, and clinician approval.
Model updates change documentation behavior
Without versioning and evaluation, a model, prompt, or retrieval change can alter clinical drafts without a controlled release process.
Generated text enters authoritative systems too early
A plausible output should not become an accepted record without the required review and validation.
COMPLETE SOLUTION
Engineer the workflow as one controlled production system.
The objective is not autonomous clinical authority.
The objective is a controlled assistance system that helps qualified users prepare, retrieve, structure, review, and approve clinical information.
The complete system can connect local STT, medical vocabulary, diarization, private LLMs, document processing, retrieval, embeddings, approved knowledge, computer vision, imaging-system integration, structured extraction, schemas, deterministic checks, evidence, human review, private GPU infrastructure, model routing, lifecycle monitoring.
CAPABILITY GROUPS
What the complete Solution can contain.
Private clinical scribe
Transform approved encounters into editable drafts such as notes, summaries, histories, findings, structured fields, and follow-up items.
Approved clinical knowledge
Connect internal procedures, policies, reference material, and permitted repositories through permission-aware retrieval with source evidence.
Medical-document intelligence
Classify documents, extract structured information, preserve document identity, and route uncertain cases for review.
Imaging-workflow assistance
Integrate supported ultrasound, CT, MRI, medical-image, measurement, or imaging-system outputs into controlled analysis and documentation workflows.
Structured draft reporting
Generate outputs through defined templates, schemas, required sections, validation rules, source references, and review status.
Evidence and clinician review
Expose original source material, extracted findings, exceptions, confidence where meaningful, editable drafts, approval state, and audit-supporting events.
Private model and infrastructure operation
Operate LLM, STT, embedding, vision, document, and specialist models under controlled identity, network, storage, deployment, and lifecycle boundaries.
ARCHITECTURE FLOW
Connect the complete path from source and session to operation.
The flow begins with clinical encounter, document, image, measurement, or approved system output and preserves control through every processing and operating layer.
Clinical encounter
Clinical encounter, document, image, measurement, or approved system output
Local acquisition
Local acquisition, identity, and preprocessing
Speech
Speech, document, imaging, and extraction models
Private model routing and inference
Private model routing and inference
Approved knowledge and permission-aware retrieval
Approved knowledge and permission-aware retrieval
Structured findings
Structured findings, calculations, and draft generation
Evidence
Evidence, schemas, deterministic validation, and exception handling
Clinician review
Clinician review, correction, and approval
Approved clinical-system integration
Approved clinical-system integration
Evaluation
Evaluation, monitoring, versioning, and lifecycle control
ARCHITECTURE DECISIONS
Make important trade-offs explicit before they become operating constraints.
Local models or approved external models
Approach A
Local models
Approach B
approved external models
Selection considerations
Choose according to sensitivity, model quality, latency, availability, lifecycle control, and operating capability.
Live assistance or post-session processing
Approach A
Live assistance
Approach B
post-session processing
Selection considerations
Live workflows provide immediacy; post-session workflows can use more complete context and reduce live-path risk.
One general model or specialist components
Approach A
One general model
Approach B
specialist components
Selection considerations
Speech, retrieval, imaging, extraction, and report drafting may require different models and deterministic functions.
Free-form generation or schema-bound drafts
Approach A
Free-form generation
Approach B
schema-bound drafts
Selection considerations
Use structured generation where required fields, measurements, evidence, or review status matter.
Automated output or clinician-reviewed assistance
Approach A
Automated output
Approach B
clinician-reviewed assistance
Selection considerations
Consequential clinical records remain subject to authorized review.
REPRESENTATIVE SCENARIOS
How the Solution applies in representative situations.
Representative examples: Each workflow keeps extracted or generated clinical material connected to its source evidence and authorized clinician review.
Private ambient scribe
- Scenario
- An approved encounter is transcribed locally and converted into an editable structured draft.
Ultrasound documentation assistance
- Scenario
- Supported measurements, images, operator inputs, and contextual information contribute to a report draft for clinician review.
CT/MRI workflow assistance
- Scenario
- Approved imaging outputs enter a controlled workflow that helps organize findings, evidence, and documentation.
Private clinical knowledge assistant
- Scenario
- Authorized users search approved internal knowledge and receive source-backed results.
Medical-document processing
- Scenario
- Clinical forms or reports are classified, structured, validated, and routed for review.
ENGAGEMENT APPROACH
Move from operating context to a validated production system.
Phase 1
Clinical workflow and governance assessment
Map data, systems, users, decisions, review requirements, model-placement policies, and evidence needs.
Phase 2
Representative clinical AI pilot
Prove one difficult path such as local scribing, document extraction, or structured imaging-report assistance.
Phase 3
Production workflow integration
Implement private inference, retrieval, structured generation, review interfaces, identity, and clinical-system integration.
Phase 4
Evaluation and operational rollout
Validate quality, exceptions, model changes, access, failure behavior, monitoring, and ownership.
POSSIBLE DELIVERABLES
Evidence and implementation that can transfer into operation.
Private clinical AI architecture
Local transcription pipeline
Clinical scribe application
Permission-aware knowledge assistant
Document-intelligence workflow
Imaging-assistance integration
Structured reporting engine
Clinician-review interface
Evaluation dataset and regression framework
Private model-serving environment
Monitoring and release controls
Documentation and knowledge transfer
Final deliverables depend on the selected architecture, validation evidence, and engagement scope.
Start with the workflow, evidence, integration, deployment, performance, and operating constraints that determine whether this system can work in production.
START A TECHNICAL CONVERSATION
Discuss the system, constraints, and engineering outcome.
Share the product, workload, architecture, infrastructure, performance, security, or delivery constraint that must be resolved.
Austin, Texas
Distributed engineering teams across North America, Europe, the Caucasus, and Latin America.
[email protected]