Directory/AIS (Applied Information Sciences)
AIS (Applied Information Sciences)

AIS (Applied Information Sciences)

Growth Partner
Reston, Virginia, United States
Locations
  • Reston, Virginia, United States
Region
  • United States and Canada
  • Europe
  • Asia Pacific
Service languages
  • English
Services Offered
  • Platform Consolidation
  • Migration Factory
  • Security POC
  • SDLC Transformation
  • Training and enablement
GitHub/Dedicated Engineers
  • 101–500 developers
Support
Mandy Kaur

AIS is a Microsoft Solutions Partner that helps regulated enterprises build the data, engineering, security, and governance foundation required to scale AI from experimentation to measurable business value.

For more than 30 years, AIS has partnered with organizations across Insurance, Utilities, Energy, Healthcare, Financial Services, and Federal sectors to modernize technology platforms, improve operational efficiency, reduce risk, and accelerate innovation.

AIS combines expertise in Data & AI, Agentic Engineering, GitHub-powered software delivery, cloud modernization, and cybersecurity to help organizations move from isolated AI pilots to production-scale adoption. Our approach focuses on enabling secure, governed, and sustainable AI capabilities that integrate into core business and technology operations.

AI Readiness & Data Foundation

Successful AI initiatives depend on trusted, well-governed data. AIS helps organizations modernize data estates, establish governance frameworks, improve data quality, and create AI-ready platforms using Microsoft technologies such as Fabric, Azure Data Services, and modern analytics architectures.

Our data modernization and AI readiness capabilities enable organizations to transform fragmented enterprise data into trusted assets that support decision-making, automation, advanced analytics, and AI-driven business processes.

Agentic Engineering

AIS helps organizations design, build, govern, and operate AI agents that deliver measurable business outcomes. Our Agentic Engineering approach combines agent architectures, context engineering, governance controls, security practices, and software engineering discipline to enable reliable enterprise AI adoption.

Rather than focusing solely on AI tools, AIS helps establish the operating models, architecture patterns, governance frameworks, and delivery practices required to scale AI across the enterprise while maintaining compliance and business oversight.

Secure Enterprise Scale

AIS helps organizations deploy AI and modern applications securely within highly regulated environments. Our cybersecurity, governance, and cloud modernization expertise helps clients balance innovation with security, compliance, and operational resilience.

From strategy through implementation and operations, AIS enables organizations to adopt AI responsibly while accelerating business transformation and reducing enterprise risk.

GitHub and AIS (Applied Information Sciences)

AIS helps enterprises transform software delivery through GitHub, modern engineering practices, AI-assisted development, and secure software supply chain management.

Rather than treating GitHub as a developer tool alone, AIS uses GitHub Enterprise as the foundation of a modern engineering operating model that improves developer productivity, software quality, security, and time-to-market.

AIS helps clients implement:

  • GitHub Copilot and AI-assisted software development
  • GitHub Enterprise governance and operating models
  • GitHub Advanced Security
  • Secure software supply chain practices
  • Agentic application and AI solution development
  • Context engineering and reusable knowledge frameworks
  • Modern DevSecOps platforms and workflows

By combining GitHub capabilities with enterprise governance, security controls, and engineering best practices, AIS helps organizations improve delivery velocity while maintaining quality, traceability, and compliance.

Our experience in highly regulated industries enables clients to modernize software development without compromising security or regulatory requirements. The result is a more productive engineering organization capable of delivering innovation faster, reducing operational complexity, and creating a sustainable foundation for AI-enabled software development.

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