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Modernizing federal health care through connected data

EXPLORE OUR EHR MODERNIZATION EXPERTISE

Decades of in-house expertise. One mission: to captivate.

CURIOUS? MEET CAPTIVATE.
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Improving quality, outcomes and access to health care

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Less artificial, more intelligent: Aptive's creative team on AI

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Discover the power of informed decision making

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Big ideas start with a single SPARK

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BETTER HEALTHCARE

Aptive is a federal health company. We work inside the country’s largest, most demanding health systems, where the programs are consequential and the margin for error is small. We partner with health agencies to design and deliver solutions built to perform under that kind of pressure, for the agencies depending on them and the people those agencies serve.

That means putting the right clinical expertise and tools in place when it matters. It means thinking boldly and executing with precision. And it means holding ourselves to one standard: did health care get better? We are not just keeping pace with what federal health care demands, we are part of defining what it becomes.

 

Founded: 2012
Headquarters: Alexandria, Virginia
Employees: 400+ nationwide

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09.21.26

7. Model Context Protocol (MCP) Servers in Enterprise AI Architecture

Security researchers have flagged real vulnerabilities in the MCP ecosystem, but nearly all of them trace back to public, third-party servers. In the last paper of Aptive's Agentic AI Governance Series, Ian Meinert separates the risks that actually carry over to self-hosted MCP from the ones that don't, and lays out what federal contractors still owe their agencies as proof they got it right.

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09.14.26

6. Multi-Model and Multi-Agent AI Workflows: Architecture, Risk and DevSecOps Controls

Multi-model and multi-agent AI architectures are now the default way serious enterprise and federal AI work gets built. That power comes with real tradeoffs: expanded attack surface, harder audit trails and governance gaps most teams underestimate. Article 6 of Aptive’s Agentic AI Governance Series lays out the risk taxonomy, a control baseline mapped to NIST 800-53 and the architecture patterns that hold up in regulated environments.

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