Chariot Solutions

A pattern we keep seeing in health tech: teams reach for a chatbot when what they actually need is an agent that can act.

Answering a patient’s question about their appointment is a chatbot problem. Rebooking the appointment, verifying identity, and updating the provider’s daily brief is a workflow problem, and it requires different infrastructure.

We’ve been evaluating tools like Amazon Connect Health for exactly this reason. It’s built to support the workflow, not just the conversation.

As an AWS partner working in health data infrastructure, this is the kind of shift we spend a lot of time helping clients think through. The question is not simply, “Should we use AI?” It’s, “Where does an agent need to interact with a real system, and what needs to be in place for that interaction to be safe?”

For years, the healthcare data strategy was simple: collect more, centralize more, build a bigger warehouse.

AI is starting to challenge that assumption.

What if the better approach isn't bringing every piece of data into one massive repository, but giving specific healthcare roles secure access to the right data and AI capabilities at the moment they're needed?

That's the idea behind Beyond the Data Warehouse: Rethinking AI Enablement in Healthcare.

On October 14, Cliff Cavanaugh, founder of Healthjump, will join the Philadelphia AWS User Group and Tech Talks for HealthTech to explore what this alternative looks like in practice, including transformation pipelines, AWS data infrastructure, governance, and Bedrock.

Wednesday, October 14 from 6:00 to 8:00 PM
HealthVerity | 1818 Market St, Philadelphia

If you're thinking about how AI actually fits into healthcare data architecture, this should be a great conversation. Free to attend. Registration is required.

Register here:

Beyond the Data Warehouse — Rethinking AI Enablement in Healthcare Healthcare's next AI bet isn't a bigger data warehouse, t's role-specific, swappable-LLM pipelines on AWS.

Chariot Solutions is heading back to G2E in Las Vegas!

This year, Rod Biresch, Director of Engineering, and Grant Hartman, Director of Client Engagement, will be representing Chariot and connecting with leaders from across the gaming industry to talk about the technology shaping what comes next.

From AI and data engineering to cloud infrastructure and custom software, gaming organizations are navigating increasingly complex technology decisions while looking for new ways to improve operations and the player experience.

We're looking forward to hearing what's top of mind for the industry and sharing what we're seeing from the engineering side.

September 28 – October 1, 2026
The Venetian Expo | Las Vegas

Will you be at G2E 2026? Rod and Grant would love to connect while you're in Vegas.

For years, healthcare organizations have been told the same thing: centralize more data, build a bigger warehouse, and eventually you'll have what you need to unlock AI.

What if that's no longer the right approach?

Join the Philadelphia AWS User Group and Tech Talks for HealthTech for Beyond the Data Warehouse: Rethinking AI Enablement in Healthcare.

Cliff Cavanaugh, founder of Healthjump, will explore a different approach to healthcare AI: enabling specific roles and workflows with secure, transactional data pipelines instead of relying on massive centralized repositories.

Using AWS technologies including Glue, Lake Formation, data lake infrastructure, and Bedrock, Cliff will walk through what a secure, auditable, role-specific AI architecture can look like in practice.

October 14 from 6:00 to 8:00 PM
HealthVerity | 1818 Market St #700, Philadelphia, PA 19103

If you're working at the intersection of healthcare, data, cloud, or AI, this is a conversation you won't want to miss.

Save your spot: https://www.eventbrite.com/e/beyond-the-data-warehouse-rethinking-ai-enablement-in-healthcare-tickets-2000210076182?aff=oddtdtcreator&keep_tld=true

When us-east-1 sneezes, the Internet calls in sick.

AWS is remarkably reliable. But major outages still happen, and when they do, the impact can ripple across thousands of businesses and services.

The question is: Will your systems stay running?

Join Keith Gregory for a practical look at disaster recovery and resiliency on AWS, including the techniques and services that can help organizations prepare before an outage becomes an all-hands-on-deck event.

Keith will explore the technical tradeoffs behind resilient architecture, what AWS provides to help, and the financial reality of designing systems that can remain available when others aren't.

Because once an outage starts, it's already too late to build your disaster recovery strategy.

Thursday, September 24 | 6:00–7:30 PM EDT
Certara | 4 Radnor Corporate Center, Suite 350, 100 Matsonford Road | Radnor, PA

Register here:

When us-east-1 sneezes, the Internet calls in sick, Thu, Sep 24, 2026, 6:00 PM | Meetup AWS is a remarkably stable platform. I have personally seen EC2 instances that have been running for over five years. Companies large and small rely on it for just this rea

CMS interoperability requirements don't leave much room for interpretation, but the architecture decisions underneath them do and that's where most of the pain lives.

Bulk export at scale, event-driven sync instead of nightly batch jobs, auth that satisfies both security and compliance teams: these aren't edge cases anymore, they're baseline requirements for anyone touching clinical data. Whatever stack you're on, the questions are the same — where does your event bus fit, what's actually going through your API layer versus a private path, how do you keep bulk export from becoming a scheduled outage.

On AWS specifically, HealthLake gives teams a real anchor for FHIR instead of hand-rolling a server from scratch, paired with EventBridge for the event-driven pieces. We've built this pattern for clients who needed it to hold up under audit, not just look clean in a diagram.

Working through your own interoperability or healthcare data architecture challenges?
Talk to our team about building a solution designed for the realities of clinical data.

Your LLM costs are growing. Do you know where the money is going?

In his latest post, Rod Biresch, Chariot Solutions' Director of Engineering, shares practical ways engineering teams can get ahead of AI spend with better visibility, smarter model selection, and the right guardrails.

From LLM gateways and scoped budgets to model tiering, these are steps teams can take today before the API bill becomes the problem.

Read the full post: https://ow.ly/eopV50ZFFQ6

The next step for AI in healthcare is not another chatbot. It is putting AI to work inside the systems healthcare teams already use.

A chatbot layered onto a scheduling system or EHR can answer questions, but it is still sitting outside the workflow. Provider daily briefs, patient scheduling, and ambient documentation require AI that can work within those systems and help move tasks forward.

That is why agentic AI is worth watching in health tech. We are beginning to see AI move beyond conversation into areas like scheduling, identity verification, and clinical documentation.

The technical challenge is only part of the equation. PII handling, HIPAA-aware logging, security, human oversight, and clear rules for when AI should hand a task back to a person all need to be designed into the solution.

At Chariot Solutions, that is where we see the real opportunity: building AI that does useful work while fitting securely and responsibly into the systems organizations depend on.

Our engineers can help you identify the right opportunities and build solutions designed for real-world use.

What happens when an industry still managing complex clinical trial budgets through spreadsheets and email decides there’s a better way?

A leading clinical research organization (CRO) partnered with Chariot Solutions to turn that opportunity into enterprise software, bringing budgeting, real-time pricing data, CPT code integration, and negotiation workflows into one platform.

Our team also found practical ways to incorporate AI throughout development, accelerating prototyping while keeping experienced engineers at the center of production decisions.

The result? First user acceptance testing in approximately seven months and a scalable foundation built for the future.

Read the full case study to see how collaboration, engineering expertise, and pragmatic AI helped bring this CRO’s vision to life.

Read the case study: https://ow.ly/CVs250ZyEuY