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HEALTHCARE AI SERVICES

Put AI to Work Without Compromising Patient Trust

Tech.us helps healthcare organizations use AI in real clinical and operational settings while keeping patient data protected and the people responsible in control.

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1,500+ Projects Delivered
26+ Years of Engineering
30+ Industries Served
Patient data Built for protected health data
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OUR SERVICES

Healthcare AI Built for Real Clinical Environments

AI has to fit the way care is delivered, not create another disconnected system for staff to manage. We build around your existing workflows with the controls healthcare demands.

AI Readiness and Strategy Consulting

Find out where AI can make a practical difference before committing to development. We review your current environment and available data to identify use cases that are realistic enough to move forward.

Custom AI and Machine Learning Development

Build models around problems specific to your organization using your own data. Each solution is tested against relevant cases before it is introduced into a live healthcare environment.

Generative AI and LLM Solutions

Use generative AI for areas such as clinical documentation, summarization, and patient communication. Solutions are grounded in trusted information so the output stays closer to the sources your staff already relies on.

AI Agents and Workflow Automation

Take repetitive administrative work away from clinical and operational staff without automating decisions that need human judgment. Review points are built into workflows where the outcome carries clinical or financial weight.

AI Integration With EHR and Clinical Systems

Bring AI into the systems your staff already uses through integrations based on standards such as HL7 and FHIR. This reduces the need for another standalone tool sitting outside the clinical workflow.

Model Validation, Monitoring, and MLOps

Track how models perform after deployment instead of assuming launch-day accuracy will last. Monitoring helps identify drift so performance can be reviewed as data or operating conditions change.

SELECTED WORK

Healthcare Technology Built Around Real Operating Needs

Explore selected work where Tech.us has turned complex healthcare requirements into technology designed to fit the environments where people actually use it.

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AI-Powered Takeoffs From Complex Construction Drawings

A precast concrete manufacturer partnered with Tech.us to bring AI into the estimating workflow. The system uses OCR, computer vision, and generative AI to identify, extract, and visualize structures, pipes, and components from complex construction drawings.

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Wellington Hamrick Precast AI takeoff automation tool shown on laptop and tablet

Mobile Fleet Visibility Built for Field Operations

SkyHawk by TELUS needed a streamlined mobile experience for its Connect Anywhere platform. Tech.us built the core experience around real-time asset tracking, fleet activity, secure configuration, and map-based operational visibility.

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SkyHawk by TELUS mobile app screens showing fleet tracking and login

Personalized Financial Technology Built to Scale

Tech.us helped bring Wealth Mastery to life as a digital platform that puts personalized financial planning tools directly in users' hands while supporting a large and growing audience.

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Tony Robbins Wealth Mastery app shown on tablet and phone

WHY TECH.US

Healthcare AI Has to Earn Trust Before It Earns Adoption

A useful healthcare AI system has to fit the workflow and stand up to the scrutiny that comes with sensitive data and consequential decisions.

Healthcare Software Experience

Our healthcare work spans areas such as EHR systems, medical imaging, remote monitoring, and billing. That experience gives us context for how AI has to fit within a broader healthcare environment.

Built Around Existing Clinical Workflows

AI should reduce friction rather than give clinicians another place to work. We design around the systems and workflows already in use so adoption does not depend on rebuilding the way people do their jobs.

Validation Before Live Use

Models are tested against relevant real-world cases before they are put in front of staff or patients. If the results are not ready for the intended use, the solution does not move forward.

Healthcare Data Handled With Care

Protected health information is treated as sensitive from the beginning of development. Security and privacy requirements influence how the solution is designed rather than being added after the build.

Interoperability Experience

We work with standards such as HL7 and FHIR when AI needs to exchange information with EHRs or other clinical systems. The goal is useful information flow without creating more fragmentation.

Proven Technology Delivery

Tech.us brings more than two decades of engineering experience and 1,500+ completed projects to engagements that need disciplined implementation, not just AI experimentation.

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Clinical Safeguards

Keep People Accountable for Decisions That AI Can Support

Healthcare AI should make information easier to use without making responsibility harder to see.

Protect PHI From the Start

Protected health information is handled in line with HIPAA and HITECH requirements, with encryption and controlled access built into the development approach.

Keep Human Review in the Workflow

AI can surface information or complete routine work. Clinical and financial decisions that require accountability remain with the people responsible for them.

Test for Bias Before Deployment

Model performance is evaluated across patient groups so uneven results can be identified before the system is put into regular use.

Plan for Applicable Regulation

When a solution may fall under FDA oversight as Software as a Medical Device, that pathway is considered while the system is being designed rather than after development is complete.

Make Outputs Easier to Understand

Where the use case allows it, we favor approaches that help clinicians and administrators understand why a model produced a particular result.

Keep Actions Traceable

Access controls and audit trails provide visibility into how sensitive systems are used so activity can be reviewed when needed.

Start With a Healthcare AI Use Case Worth Building

Find the opportunity that can work with your data and hold up in the environment where your people will use it.

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HEALTHCARE SOLUTIONS

AI Solutions for Work That Carries Real Clinical and Operational Weight

We build focused healthcare AI solutions where better use of information can remove workload or help people act sooner.

Clinical Documentation Automation

Clinical Documentation Automation

Turn clinical conversations into structured draft notes that clinicians can review rather than spending the same time creating documentation manually.

Medical Imaging and Diagnostic Support

Medical Imaging and Diagnostic Support

Analyze medical images to highlight areas that may warrant closer attention, giving specialists another source of support while final interpretation stays with them.

Clinical Decision Support and Risk Stratification

Clinical Decision Support and Risk Stratification

Bring relevant patient information forward and identify higher-risk cases so care teams can focus attention where it may be needed sooner.

Revenue Cycle and Prior Authorization Automation

Revenue Cycle and Prior Authorization Automation

Reduce repetitive work across administrative processes such as prior authorization and revenue cycle tasks while keeping exceptions or approvals with staff.

Patient Engagement and Virtual Assistants

Patient Engagement and Virtual Assistants

Handle routine scheduling, reminders, and common patient questions through AI assistants that can hand the conversation back to a person when needed.

Healthcare Data and Interoperability

Healthcare Data and Interoperability

Connect clinical systems and prepare data for AI use through standardization or de-identification where required, creating a more dependable foundation for downstream solutions.

HOW WE WORK

Move From a Valid Use Case to AI That Can Be Used in Practice

Healthcare AI moves forward only when the data can support it and the solution can be validated for the environment where it will operate.

Define the Use Case

We start with the problem and determine whether AI is an appropriate way to solve it. Scope is made clear before development begins.

Review Data and Compliance Requirements

We assess whether the available data can support the use case and how it needs to be handled. Privacy requirements are addressed before the model is built.

Build and Validate

The solution is developed and tested against relevant cases. Performance has to meet the needs of the intended use before deployment.

Deploy and Monitor

We introduce the solution into your environment with monitoring in place so changes in model performance can be identified after launch.

WHO WE BUILD FOR

Healthcare Organizations With Different Care and Data Environments

Our healthcare AI work can support organizations across care delivery, diagnostics, research, insurance, and life sciences.

Clinics and Hospitals

Medical Laboratories

Healthcare Centers

Pharmaceutical Companies

Diagnostic Centers

Government Health Departments

Health Insurance Providers

Health Information Exchanges

Clinical Research Organizations

Biotechnology and HealthTech Companies

TECH STACK

Technology That Fits the Healthcare Environment Around It

The technology behind a healthcare AI system depends on where the data lives and how the solution needs to fit into existing clinical systems.
We choose the stack around reliability and security while accounting for the environment in which the AI will operate.

Agentic AI & Orchestration

Agentic AI Agentic AI
AI Agents AI Agents
Multi-Agent Systems Multi-Agent Systems
Agentic Workflow Automation Agentic Workflow Automation
Model Context Protocol (MCP) Model Context Protocol (MCP)
Agent-to-Agent Protocol (A2A) Agent-to-Agent Protocol (A2A)
Agent Memory and Reasoning Agent Memory and Reasoning

RECOGNITION

Recognition Backed by Consistent Technology Delivery

See the industry recognition Tech.us has received for its work across software engineering and technology development.

Mobile App Daily — Top Web Development Company
DesignRush — Top AI Development Company
Mobile App Daily — Top AI Company
TechReviewer — Top Mobile App Developers
TechReviewer Award

FAQ

Healthcare AI Questions

Tech.us handles protected health information in line with HIPAA and HITECH requirements. Security measures such as encryption and controlled access are considered throughout development rather than being added only at deployment.

No. Healthcare AI is built to support the people responsible for care rather than make consequential decisions on their behalf. Human review stays in the workflow where clinical or financial judgment is required.

Yes. AI can be integrated with EHR and clinical systems using healthcare interoperability standards such as HL7 and FHIR. This allows the solution to work with existing information flows instead of o as a disconnected tool.

Models can be tested across different patient groups before deployment to identify differences in performance. Issues that could affect how reliably the model serves those populations need to be addressed before the solution moves forward.

It depends on what the system does. When a solution qualifies as Software as a Medical Device, FDA oversight may apply. That regulatory pathway should be considered from the beginning of the project.

Models are tested against real cases relevant to the intended use before deployment. Validation is used to determine whether performance is strong enough for the environment where the system will operate.

Performance continues to be monitored after launch because models can change in effectiveness as incoming data changes. Monitoring and retraining processes can be used to identify and respond to model drift.

The timeline depends on the use case and the readiness of the underlying data. Integration requirements can also affect the scope. A realistic timeline is defined during discovery rather than assumed before those factors are understood.

Build Healthcare AI Your Teams Can Actually Use

Fit AI into the systems your people already rely on without creating another layer of work to manage.

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