Next Phase is pleased to announce it was awarded a contract for the Missile Defense Agency Scalable Homeland Innovative Enterprise Layered Defense (SHIELD) indefinite-delivery/indefinite-quantity (IDIQ) contract with a ceiling of $151B. This contract encompasses a broad range of work areas that allows for the rapid delivery of innovative capabilities to the warfighter with increased speed and agility.
Imagine trying to fly a plane without checking the runway. It wouldn’t end well. The same goes for large language models (LLMs). The first critical step is infrastructure planning: selecting the right compute resources (CPUs, GPUs, TPUs) and cloud architecture to power your AI brain.
Whether you’re working on AWS, GCP, Azure, or an on-premises setup, your foundation determines everything from cost ceilings to latency floors.
Quick Thought: Do you need real-time responses, or can you tolerate a few seconds of delay? Your answer should drive infrastructure decisions such as autoscaling policies, instance types, and memory optimizations.
Once the runway is built, it’s time to get fast. Inference optimization is about reducing response times using techniques like model quantization, distillation, and intelligent caching.
Think of it as Formula 1 tuning for your AI engine. Every millisecond saved is a dollar earned.
Pro Tip: Don’t run a GPT-4 when a distilled version of GPT-2 will suffice. Know when to deploy the big guns and when to stay lean.
You don’t always need to retrain your LLM. Often, you just need to reframe the prompt. Prompt engineering is the secret sauce of today’s AI systems, cleverly crafted queries that guide models to produce accurate, safe, and brand-aligned responses..
From zero-shot to few-shot to chain-of-thought prompting, the right phrasing makes all the difference.
Fun Analogy: It’s like talking to a genie. Be vague, and you might get a monkey paw situation. Be specific, and you get exactly what you wished for.
A model that works in your development environment isn’t necessarily the same one you need to manage 10 million users. Scalability and deployment choices determine how smoothly your LLM-powered service grows. Should it live in the cloud, on the edge, or behind a secure firewall in a data center?
This isn’t just a technical decision. It’s a business one.
Watch Out: Some models aren’t licensed for production or require specific GPU hardware. Also, latency differs significantly between mobile and desktop platforms.
The final piece is balance. Tradeoffs between cost and performance are inevitable. Faster models are expensive, while cheaper ones underperform. Smart design means knowing where to draw the line.
Is your user base paying for instant results? Or can your product afford to sacrifice speed for affordability?
Reality Check: Even trillion-dollar companies have budgets. Thoughtful architecture matters.
LLM system design isn’t a one-time checklist, it’s a dynamic and evolving strategy. From compute decisions and optimization techniques to prompting and deployment, each element contributes to the user experience.
The most effective LLM systems don’t just work, they scale, they save, and they impress.
Now that you know the blueprint, go build something brilliant!
Infrastructure as Code (IaC) revolutionized cloud deployments by making infrastructure programmable, repeatable, and version-controlled. However, in today’s fast-moving digital landscape, static templates and manual change reviews are no longer sufficient. We are entering the era of IaC 2.0, where infrastructure is not only coded, but also intelligent.
IaC 2.0 fuses traditional declarative configurations with AI-driven insights to enable predictive optimization, real-time validation, and adaptive provisioning. This evolution does not merely build infrastructure, it learns from it.
While traditional IaC tools such as Terraform, Pulumi, and CloudFormation have laid the foundation, they are largely based on static logic. AI-enhanced IaC introduces contextual intelligence, learning from usage patterns, performance metrics, and historical incidents to proactively improve infrastructure design and reliability.
IaC 2.0 represents the next generation of infrastructure automation.
AI models analyze historical telemetry data to forecast workload demands. These insights help the system automatically suggest or implement infrastructure changes, such as scaling, instance type replacement, or region relocation before performance issues arise.
By integrating with AI-powered policy engines (ie Open Policy Agent (OPA) with machine learning enhancements), configurations can be validated against security standards, compliance requirements, and best practices as they are written, eliminating vulnerabilities before deployment.
AI-enhanced IaC tools can detect, categorize, and prioritize configuration drifts based on impact. For instance, the system can distinguish between a harmless version bump and a critical drift that compromises availability, then recommend or auto-execute an appropriate resolution.
With observability wired into the provisioning logic, the system can detect anomalies or failures and respond automatically. It may revert to a known good state or apply corrective patches, significantly reducing mean time to recovery (MTTR) and manual intervention.
IaC 2.0 is not intended to replace engineers, it is designed to amplify their capabilities. By automating low-level decisions, predicting issues before they arise, and enforcing best practices in real time, AI-enhanced IaC empowers teams to move quickly without breaking processes.
In this new era, infrastructure is no longer a static script it is a responsive, intelligent system. The future of cloud operations belongs to those who can build infrastructure that learns, adapts, and continuously improves itself.
Next Phase Solutions and Services, Inc. (Next Phase) and its SBA Mentor, Consolidated Safety Services, Inc. (CSS), proudly announce the formation of NeXcss, an SBA-approved 8(a) Mentor-Protégé Joint Venture built to advance mission outcomes across the federal landscape.
NeXcss unites two proven leaders with complimentary strengths and a shared commitment to public service. The Joint Venture combines Next Phase’s expertise in digital government modernization, AI/ML platforms, spectrum engineering, human-centered design, and agile delivery with CSS leadership, and their wholly-owned subsidiary, Riverside Technologies, in environmental intelligence, satellite and remote sensing, applied field science, regulatory compliance, and emergency response. Together, the JV brings over four decades of award-winning delivery across science, technology, and national mission environments.
Grounded in public -purpose and measurable outcomes, NeXcss delivers integrated, science-driven digital solutions that connect data, domain, and decision-making, enabling federal agencies to make better-informed policy choices, modernize with confidence, mitigate risk, and build resilient systems that benefit the nation.
NeXcss is now open for partnership and engagement. Learn more about how we are accelerating impact across government missions.
Imagine a world where artificial intelligence (AI) doesn’t reside in a single location but is instead spread out, accessible, efficient, and secure. That’s the power of a distributed AI ecosystem. Let’s explore this exciting concept step-by-step, in clear and simple terms.
Traditional AI relies on centralized systems. Think of it as putting all your eggs in one basket. This approach is risky, potentially inefficient, and often leads to performance issues.
Identify your goals. Are you improving response times? Enhancing privacy? Scaling AI? Reducing operational costs? Clearly defined objectives will guide your decisions.
Platforms and tools that support your objectives such as AWS, Azure, or Google Cloud Platform (GCP), and explore edge computing and federated learning frameworks.
Use encryption, authentication, and secure APIs to safeguard communication between distributed nodes.
Deploy edge devices where they can maximize efficiency. These devices collect and preprocess data locally, reducing reliance on data centers and minimizing bandwidth usage.
Train AI models across distributed data sources without compromising privacy. This approach allows for smarter, faster, and safer model training while avoiding the cost of data centralization.
Use redundancy and automated monitoring tools to maintain uptime and system health. This ensures your ecosystem remains resilient and financially predictable.
Distributed systems are not without obstacles, including added complexity, synchronization needs, and evolving security risks.
A distributed AI ecosystem isn’t just innovative, it’s practical, scalable, and cost-effective solution for businesses aiming to harness the power of AI.. By distributing resources efficiently, organizations can significantly reduce operational costs, enhance performance, and achieve financially sustainable AI adoption at scale.
Follow this blueprint to unlock the full potential of distributed intelligence—and build smarter, faster, and more resilient systems for the future.
Next Phase is proud to announce a new partnership with American Rivers, a leading nonprofit organization dedicated to safeguarding clean water and restoring healthy rivers nationwide. Next Phase will assist in advancing American Rivers’ data strategy and governance initiatives, further enabling the organization’s use of information to drive conservation outcomes with national impact.
For over 50 years, American Rivers has worked to protect and restore rivers, ensuring that communities have access to clean water, vibrant habitats, and resilient ecosystems. The ability to connect people, partners, and resources across the country to accomplish the mission is vital to their success. Using enabling technologies and modern data strategies to make those important connections, American Rivers demonstrates their commitment to streamlining identification of the people, partners and resources to execute the mission. “We’re honored to partner with American Rivers. By investing and modernizing data practices, American Rivers is laying the foundation for greater collaboration, transparency, and mission success and it is exciting to play a small part in that success,” said Lisa Wolff, President and CEO, Next Phase.
Through this partnership, Next Phase is helping American Rivers design and implement a tailored data strategy that will, among other benefits, empower teams to make faster and more confident decisions in support of river conservation.
Next Phase remains committed to supporting organizations, like American Rivers, that harness the power of data to solve critical challenges. For American Rivers, this work ensures that science, advocacy, and community action are informed by data-driven insights, helping to accelerate efforts to protect and restore the rivers that sustain life for many.
To learn more about American Rivers’ mission and how you can support their work, please visit www.americanrivers.org and consider making a donation to help safeguard clean water and healthy rivers.
Next Phase Solutions and Services, Inc. (Next Phase), an SBA Certified 8(a), and Economically Disadvantaged, Woman-Owned Small Business (EDWOSB) focused on engineering, science and IT services, and Consolidated Safety Services, Inc. (CSS), Inc., a leader in health and safety services, are pleased to announce our collaboration in the U.S. Small Business Administration (SBA) Mentor-Protégé Program.
This strategic partnership is designed to strengthen Next Phase’s capabilities and competitiveness in the federal marketplace while expanding CSS’s reach and impact across federal agencies. The SBA Mentor-Protégé Program pairs small and disadvantaged businesses with experienced companies to provide business development support, enhance technical expertise, and create new opportunities for growth in the federal technology and services marketplace.
“Next Phase is thrilled to have CSS as a mentor. We know that we will benefit tremendously from their guidance and assistance,” said Lisa Wolff, President, Next Phase. “Our strong reputation and track record for delivery in the Federal sector at NOAA, NASA, DHS, and HHS make Next Phase a valuable protégé and partner to CSS.
SBA-recognized mentors are given limited small business standing, and as an approved SBA mentor, CSS will offer its significant capabilities to a greater range of Federal agencies. “We are excited to elevate our combined services to further advance the capture, analysis, and application of science-based data in the health and environmental sectors,” said Jolanda Janczewski, Ph.D., President and Chairman of the Board for CSS.
As part of the mentor-protégé relationship, CSS will provide Next Phase with comprehensive support in key areas such as health and safety services, environmental management, and human health. This collaboration will also include guidance and direct support in various areas, including industry certifications, market entry strategies, and federal contract administration and compliance. CSS’s deep expertise and robust resources will empower Next Phase to strengthen its core competencies and position itself for long-term success in delivering mission-critical services to government clients.
Founded in 2011, Next Phase Solutions and Services, Inc. is a small business with a proven record of delivering solutions that exceed client expectations. The company specializes in business analysis, data analytics and IT modernization, with a mission to help government agencies solve complex challenges and achieve their strategic goals. Next Phase prides itself on building lasting partnerships that anticipate client needs and deliver impactful results that advance their mission. Learn more about Next Phase’s work.
Founded in 1988, Consolidated Safety Services, Inc. (CSS) is a 100% employee-owned, science-based solutions firm dedicated to helping government, commercial, and academic clients address complex environmental, safety, and health challenges. For more than three decades, CSS has leveraged its deep expertise, ranging from space science and remote sensing to field data collection; risk assessment; emergency preparedness; facilities operations; and environmental resource management, to deliver innovative, mission-critical support across more than 28 federal agencies. Learn more about CSS projects.
As government agencies embrace multi-cloud strategies, they gain unprecedented flexibility and access to best-fit tools across providers. Multi-cloud environments allow teams to quickly spin up specialized resources and scale rapidly to meet mission needs. It’s no surprise that multi-cloud is widely seen as the future state for federal IT, delivering strong ROI and agility. However, these same qualities (diverse services, fast provisioning, and autonomy for project teams) also create unique security challenges. Siloed cloud environments and inconsistent controls can lead to dangerous blind spots, fragmented data, and increased risk if not managed in a unified way. To protect critical systems and data in a multi-cloud world, agency cyber leaders must rethink their approach in a few key areas: centralizing operations and data visibility, empowering security teams with automation, and implementing smart governance with the right tools. Below, we explore each of these strategies and how they help tailor security to multi-cloud’s unique challenges.
In the past, launching a new server or application required lengthy coordination, equipment had to be approved, installed, and configured by multiple teams. Today, in the cloud, a single developer can spin up a server in minutes with self-service access. This speed is great for innovation, but if cloud projects are launched without the security operations team’s awareness, it can result in isolated pockets that the central security team cannot see or control. Such “shadow IT” blind spots pose substantial risk, since an enterprise cannot secure what it cannot monitor in real time. As one expert noted, a person can launch a new cloud instance almost instantly, “…but unless project teams are perfectly in sync with their agency’s cyber operations, that kind of velocity can easily lead to isolated environments and blind spots” scworld.com. In a multi-cloud enterprise, especially when some operations are still on-prem, it’s critical to consolidate and centralize security operations across all environments.
Unified operations means the security team has a single vantage point across on-premises systems and every cloud in use. A centralized Security Operations Center (SOC) with multi-cloud reach allows analysts to monitor activity in real time across all providers, rapidly detect incidents, and take immediate action enterprise-wide. In practice, this could involve deploying a multi-cloud security platform or “single pane of glass” that aggregates telemetry from AWS, Azure, Google Cloud, and any private clouds, couple with other relevant agency data. Centralized monitoring and management tools are essential for effective security management because they provide real-time visibility into all cloud environments and enable quick incident response. Rather than each project team using separate, siloed security controls, a centralized approach offers a consistent suite of security services (e.g. identity management, network monitoring, threat detection) managed by the core security group for everyone’s use. This ensures uniform compliance and reduces duplicate efforts.
When the security operations are consolidated, incidents can be contained faster because the central team has authority and tooling across the entire network. If a breach is suspected on one cloud platform, the SOC can immediately investigate and if needed, quarantine resources in that cloud as well as others, without waiting on disparate teams. Centralizing operations also means centralizing the data and logs those operations rely on, leading to the next key point.
Real-time monitoring is only part of the battle. An effective security program also needs historical awareness of everything that has happened in the environment. Comprehensive logging, and long-term retention of those logs, is crucial in multi-cloud security. Every authentication, configuration change, network flow, and admin action across all clouds may become important in a future investigation. Indeed, when a security incident arises, having a complete record of past activity is indispensable for forensic analysis. Investigators will ask questions such as: When did the intrusion begin? How long did attackers have access? Which systems did they touch and what data was exposed? Answering these requires digging through logs that might be months or years old. As cybersecurity professionals often caution, organizations “don’t know what information they will need to analyze in the future” so the safest course is to log everything and keep it scworld.com.
Accumulating years of logs from multiple cloud platforms results in a massive volume of data, potentially straining storage capacity. But with today’s abundant and affordable cloud storage options, including low-cost archival tiers, there is little excuse not to retain logs. The cost of storage is trivial compared to the cost of missing evidence during a breach investigation. Agencies should establish policies to forward all logs into a centralized repository, such as a cloud-based data lake or security information and event management (SIEM) system, and to keep those logs for a sufficient duration (often dictated by compliance, but longer, if possible, for advanced threat hunting). Modern cloud-based logging makes it possible to aggregate data from all providers into one searchable interface, avoiding the trap of separate dashboards per cloud which create blind spots and slow down incident response. When logs are centrally stored and normalized, security teams can perform enterprise-wide threat hunting and analytics on demand. For example, if unusual behavior is detected on one server, analysts can query the centralized logs to see if similar patterns occurred elsewhere in any cloud environment. If a zero-day attack is announced that leaves specific traces, the team can quickly search through historical logs from all clouds to identify any signs of compromise. This broad and deep visibility dramatically improves an agency’s security posture in a multi-cloud setup. 
Storing every log and monitoring every cloud generates an overwhelming amount of information, more than any human team can manually analyze in real time. Federal security teams are already stretched thin due to the cybersecurity talent shortage, so augmenting human analysts with automation and machine learning is essential. Advanced tools can sift through billions of events to flag anomalies, freeing up humans to focus on critical decisions. As threats grow in sophistication and volume, leveraging automation and AI-driven analytics is the only way to keep up. In fact, automation and AI are now seen as force multipliers that help organizations stay ahead of attacks with the growing threat landscape and cybersecurity staff shortage by automating tasks, detecting threats in real time, and enhancing security.
In short, automation and AI act as force multipliers for a security team, allowing them to cover a much larger and more complex multi-cloud footprint than they otherwise could. By automating the heavy lifting of data crunching and initial incident handling, agencies can respond to threats faster and more consistently. Agencies can augment and empower their cyber workforces through automation, machine learning and artificial intelligence, extending capacity of limited IT staff.
Even the best people and tools can be undermined by one of the biggest risks in cloud security: simple human error. In complex multi-cloud environments, it’s all too easy for someone to misconfigure a setting that leaves data exposed. For example, a developer in a hurry might deploy an application but forget to enforce encryption on an S3 bucket or inadvertently leave a management interface open to the internet. Traditional governance (i.e. security policies communicated in documents or training) can outline best practices, but expecting every individual to perfectly follow every rule 100% of the time is unrealistic. Mistakes will happen. Due to this, agencies are increasingly turning to technical enforcement of security policies, which essentially embedding compliance into the technology stack so that the platform automatically prevents or corrects human mistakes.
One effective approach is the use of pre-approved, security-hardened cloud environments provided as a service to teams. In this model, the central IT organization offers a cloud platform (or “landing zone”) that has all the necessary security controls and configurations baked in. Developers and engineers can build their systems on this platform, gaining the speed and flexibility of the cloud, while the platform itself ensures that certain risks are mitigated by default. Misconfigurations are less likely because the environment comes pre-configured to meet federal security requirements. In practice, this might look like automated guardrails: for instance, any new storage bucket created on the platform is automatically encrypted and tagged, network settings are automatically set to government-approved defaults, and only hardened container images can be deployed.
Real-world examples in government illustrate the power of this approach. Health and Human Services is a prominent example of a DevSecOps platform where development teams get a ready-made cloud environment with continuous security baked in (identity management, zero-trust controls, software security scans, etc.). In other words, the platform itself enforces the rules – technical governance supplements traditional policy. When every project is developed in a centrally managed, security-hardened cloud sandbox, the margin for error narrows significantly.
To implement this strategy, agencies should consider developing or adopting a secure cloud foundation (either in-house or via a vendor) that all teams can leverage. Key features should include: guardrail policies for network, identity, and configuration that apply across all cloud accounts, continuous compliance scanning against frameworks like NIST or FedRAMP, and one-stop self-service tools that make doing the secure thing the easy thing for developers.
Some agencies partner with industry providers to get this capability quickly. For example, an advanced observability and security platform like ForeSite360 can serve as a unified solution to many of these challenges. ForeSite360 is an AI-driven enterprise observability platform that provides deep situational awareness across diverse IT ecosystems. It enables organizations to monitor and analyze the health of all their infrastructure, cloud services, IoT devices, and applications in real time, all through one interface.
Unlike piecemeal monitoring tools, an integrated platform like this leverages AI/ML analytics to correlate events and enforce policies uniformly. By deploying such a platform, agencies gain a 360-degree view of their multi-cloud and on-prem environments and can automate compliance and security across the board. In effect, ForeSite360 serves as the centralized nervous system for multi-cloud security (and on-prem systems), reducing downtime through predictive analytics, improving mean time to resolution by pinpointing issues faster, and proactively flagging misconfigurations before they become incidents. This kind of shared “secure-by-design” platform is an option for IT leaders looking to elevate their cloud security posture.
As the shift to multi-cloud accelerates, federal IT and security leaders must work hand-in-hand to manage the transition in a way that both enables the mission and safeguards it. The most successful multi-cloud adopters will be those who take a strategic, unified approach rather than treating each cloud in isolation. This means integrating existing cloud environments under central oversight, while moving toward a shared services model for the future. In summary, agencies should strive for maximal, unified visibility of assets and activities across clouds/on-prem, invest in automation and AI to cope with scale and complexity, and embed security governance into technology platforms to minimize human errors. Multi-cloud environments are complex, but with the right strategy and tools, that complexity becomes manageable. By implementing the practices outlined above and leveraging platforms like ForeSite360 to tie them all together, government organizations can confidently ride the multi-cloud innovation wave without compromising on security. The result is a cloud environment that is agile yet controlled, centrally observed, open to innovation yet resilient against threats. In the era of multi-cloud, a proactive and platform-driven security strategy is not just advisable. It is non-negotiable for mission success.
Contact us at sales@npss-inc.com or visit foresite360.io to learn more about ForeSite360.
Next Phase Solutions and Services, Inc. (Next Phase) is proud to announce its role as a subcontractor to I.M. Systems Group, Inc. (IMSG) on the ProTech 2.0 Weather Domain Indefinite Delivery/Indefinite Quantity (IDIQ) contract awarded on August 29, 2025. This five-year contract will support the National Weather Service’s (NWS) mission to provide weather, water, and climate data, forecasts, and warnings for the protection of life and property and the enhancement of the national economy.
Under the ProTech 2.0 Weather Domain, IMSG and Next Phase will assist NWS in evolving and modernizing its services to meet the nation’s growing need for timely, accurate, and actionable intelligence. With societal, technological, and economic challenges becoming increasingly complex, NWS is embracing agility, flexibility, and innovation to advance its vision of a weather-ready nation.
As a subcontractor to IMSG, Next Phase will bring proven expertise in cloud services, data management, and human-centered design to help the National Weather Service modernize systems and services, ensuring the delivery of reliable, actionable information to various stakeholders.
“Our team is honored to support IMSG and the National Weather Service in their mission to enable a weather-ready nation,” said Lisa Wolff, President and CEO, Next Phase. “This work aligns with our commitment to harness innovation and technology to solve critical challenges and deliver meaningful impact across the country.”
With this contract, Next Phase continues to expand the company’s federal mission support portfolio, leveraging our expertise to help solve challenges through science, technology, and data-driven solutions.
Stay tuned for more updates as Next Phase collaborates with IMSG and NWS to advance weather forecasting capabilities, enhance decision-making, and better protect communities across the nation.
Next Phase is an 8(a), WOSB, that has been a proven, trusted, and adaptive partner to the government organizations for over 14 years.
Next Phase Solutions and Services, Inc. (Next Phase) is pleased to announce our selection as a contract awardee by the State of Arizona across all four service categories for data and data management initiatives. This award empowers Next Phase to provide critical expertise to state agencies, universities, commissions, boards, and cooperative buyers in support of Arizona’s mission to modernize data, streamline processes and increase efficiency through modern digital services.
Under this multi-award contract, Next Phase will support the Arizona Chief Data Officer with a wide range of data-driven initiatives to help Arizona modernize its systems and achieve measurable impact across state government and entities that receive funding from the State of Arizona.
The State of Arizona has an on-going need to deliver flexible, agency-specific solutions that span multiple domains. As a proven, trusted, and adaptive partner to government organizations for over 14 years, Next Phase is positioned to collaborate across the State’s diverse agencies and funded programs, ensuring that data and AI are leveraged effectively to provide meaningful and actionable solutions.
“This opportunity to support the State of Arizona across all four categories underscores our team’s unique breadth and depth in delivering trusted data foundations, scalable architectures, actionable intelligence, and transformative AI that unlock new possibilities for agencies and the communities they serve,” said Sunil Arte, Strategic Solutions Architect Lead at Next Phase. “We look forward to partnering with Arizona departments, agencies and Co-Op Buyers to modernize data capabilities, improve efficiency, and enhance services that directly benefit Arizona residents.”
These wins mark a significant expansion of Next Phase’s state government portfolio and reinforces our reputation as a trusted partner for data strategy, modernization, and innovation in all landscapes. By aligning with Arizona’s vision, Next Phase will help drive improved outcomes, enhanced accountability, and stronger data-driven decision-making across the state and Co-Op Buyers across the country.
Stay tuned for more updates as Next Phase works alongside Arizona to advance enterprise data management, analytics, and AI capabilities for lasting impact.