Eric Evans and Charlie Clayton from Hanabyte join Ingram Micro's AWS Partner Spotlight to discuss how regulated organizations can move fast in the cloud without sacrificing security or compliance. They break down Hanabyte's "cradle to grave" approach — from mock audits and knowledge transfer to blurring the line between compliance and engineering — and how their Ingram Micro partnership helps clients navigate NIST, CMMC, FedRAMP, and HIPAA requirements.Key Takeaways🐕 "Shepherding," not gatekeeping — True to their German Shepherd mascot, Hanabyte guides regulated clients through cloud adoption end-to-end, but their explicit goal is teaching clients to operate the system themselves — not locking them into a managed service.📋 Compliance isn't a binder nobody reads — Instead of building shelf-ware documentation, Hanabyte runs clients through mock audits, deep technical discovery, and continuous knowledge transfer so compliance actually holds up when a real auditor shows up.⚙️ GRC engineers blur the lines — Hanabyte's "compliance engineers" combine deep regulatory knowledge (CMMC, FedRAMP) with hands-on engineering, accelerating implementation using six years of battle-tested, audited automation.💰 Compliance is a business driver, not a checkbox — Every engagement starts by understanding the real business impact of not modernizing — lost contracts, fines, or an inability to compete — to build the ROI case leadership needs to approve the work.Timeline00:00 – Welcome & introductions00:33 – Is "move fast" vs. "stay compliant" really a real tension?02:06 – Making compliance more than a checkbox exercise03:55 – Shared responsibility & the "shepherding" philosophy05:43 – First decisions that matter most for regulated AWS clients07:13 – Why compliance is a continuous process, not one-and-done08:04 – Selling to different personas: engineers vs. the C-suite09:35 – Where advisory ends and implementation begins11:41 – First move for a nervous, modernization-minded leader13:13 – Understanding what's really at stake for the business

Nathaniel from InfoCap joins this AWS Partner Spotlight with Ingram Micro to unpack what makes agentic AI fundamentally different from both traditional workflow automation and today's generative AI — and why accountability, not capability, is the real challenge in government adoption. Nathaniel explains how InfoCap builds observable, auditable AI systems that reduce case backlogs while keeping humans firmly in the loop on final decisions.Key Takeaways🤖 Agentic AI acts, it doesn't just answer — Unlike earlier AI that generated predictions or answers, agentic AI actually performs the work itself — completing complex casework, applications, and adjudication tasks that previously required a human.🔀 Not the same as workflow automation — Traditional workflows follow predefined "if this, then that" paths; agentic AI operates within guardrails and policies but chooses its own path through a near-limitless set of possible steps.🔍 Accountability is the real bottleneck — The hardest part of deploying agentic AI in government isn't the technology — it's defining who is accountable when an agent's decision is wrong, since "you can't hold an agent accountable."⚖️ Agents do the digging, humans make the call — InfoCap's approach has agents handle the research and due diligence behind a decision (often 95% of the work) while leaving the final, accountable determination to a human.Timeline00:00 – Welcome & introduction00:21 – What makes agentic AI different from recent AI adoption01:10 – How agentic AI differs from traditional workflow automation02:10 – Where work actually gets stuck today03:56 – When does a human need to be in the loop?05:15 – Moving beyond the "black box" — observability & accountability07:29 – How InfoCap builds accountable, transparent automation08:35 – How agentic AI reduces backlogs without sacrificing oversight09:35 – Where InfoCap fits in the government AI landscape

David from Global Paradigm joins Ingram Micro's AWS Partner Spotlight to explain how their "Next Gen MSP" model goes beyond traditional break-fix IT support — pairing strategic, transparent cloud guidance with their AI-powered help desk product, Inteledesk. David shares how this approach is cutting mean-time-to-repair by up to 60% and helping mid-market organizations turn IT into a true business asset.Key Takeaways🔧 Beyond "keeping the lights on" — Instead of just maintaining legacy systems on contract, Global Paradigm proactively advises clients on when it's time to modernize — even if that means recommending change over the status quo.🤖 AI-powered help desk, 60% faster — Inteledesk cuts through repetitive "is it plugged in?" troubleshooting scripts, delivering a 30–60% reduction in mean-time-to-repair by getting straight to the root issue.🔍 "Glass box," not black box — Unlike traditional MSPs that obscure who's working on your account, Global Paradigm gives full visibility into staffing and process — with the explicit goal of eventually equipping clients to run it themselves.🔒 Security matches your most sensitive data — AI tools like Inteledesk are protected with the same security rigor as customer databases or healthcare records, addressing a top concern for regulated industries.Timeline00:00 – Welcome & introduction00:29 – What makes Global Paradigm different from a traditional MSP?02:35 – Ending "drive-by selling" in IT services03:42 – Introducing Inteledesk, their AI help desk product06:07 – Why mid-market companies choose Global Paradigm over larger MSPs07:13 – "Glass box" transparency vs. the traditional black-box MSP08:45 – Securing AI tools for regulated industries09:59 – What a typical engagement looks like (18 months to full results)11:07 – Seeing results in the first 90 days

Ingram Micro sits down with Moses and Nick from TGIX to explore why so many AI initiatives stall out in proof-of-concept and never reach production. They break down TGIX's holistic approach to AI transformation — from infrastructure and data pipelines to security — and share real examples of helping public sector organizations modernize for the AI era.Key Takeaways🧩 Stitching it all together — Most AWS partners specialize in one lane (migration, data, security). TGIX's differentiator is a holistic view across infrastructure, data pipelines, and applications so nothing gets left disconnected.🏗️ Foundation before flash — Organizations chasing the newest AI models without solid technical foundations rarely scale past proof-of-concept; the ones that succeed invest in infrastructure first.🚗 Old architecture, new workloads — Cloud environments built pre-AI weren't designed for it (the "two-lane road" / "EV on horse-and-carriage roads" problem), so refactoring is often unavoidable, not optional.🎯 Work backwards from mission outcomes — TGIX starts every engagement by understanding the client's business/mission drivers, then identifies infrastructure and data gaps before recommending any AI buildout.Timeline00:00 – Welcome & introductions00:29 – What problems is TGIX solving?01:19 – Why AI requires a full technology overhaul02:13 – TGIX's nonprofit + GenAI competency combo03:33 – Do cloud environments need refactoring for AI?05:15 – What an AI transformation process looks like07:19 – Balancing speed vs. doing it right08:26 – Why you can't just "layer" AI onto old systems09:56 – Real transformation example: Medicare/Medicaid fraud detection11:47 – Staying ahead of AI instead of playing catch-up

What does "off the grid" actually mean — and is anyone ever truly disconnected? In this episode of the Jon Myer Podcast, JD, founder of CommandProm, breaks down what life is really like living off-grid on unzoned land in the Rocky Mountains of Montana.JD walks through how a school-bus RV "snowbirding" habit turned into a permanent, mortgage-free life off-grid: buying raw land on a whim, converting a one-room cabin into a full living space, and building a solar power system from scratch — all without giving up modern tech like Starlink internet, an Xbox, a 4K TV, or even AC.🚌 Off-grid didn't happen overnight — it started with weekend school-bus trips that grew into a full move. ⚡ Watts, not voltage, are what actually matter when sizing a solar system. 💰 Grid power quoted at $40K — solar ended up cheaper and more capable. 🔋 40,000 watts of battery storage covers overnight use, even in winter. 📡 Starlink + incoming fiber prove off-grid doesn't mean offline. 🛒 The real tradeoff is convenience — grocery runs are a half-day trip, not a quick errand. 🪓 Don't romanticize it — off-grid living is real work, so check local zoning first.YouTube Timeline (Chapters)00:00 – Introduction: What does "off the grid" really mean?00:35 – Where JD lives now: remote Montana, 5 miles from the mailbox01:44 – How it started: snowbirding in a converted school bus03:04 – Finding the land on Zillow and buying it on a whim07:34 – Life without indoor plumbing: outdoor bathroom & shower in Montana winters08:38 – Electricity reality check: $40,000 grid quote vs. DIY solar10:44 – Understanding watts: sizing a solar system that runs AC, dryer, and well pump13:49 – Battery bank capacity and surviving a week with zero sun17:11 – Staying connected: Starlink, incoming fiber, and remote work23:13 – JD's advice for anyone considering going off-grid

In this AWS Partner Spotlight, Ingram Micro sits down with Jagdish and Sunil from Prijal to unpack their AWS journey — from Kubernetes and observability solutions in production to cutting-edge Edge AI and conversational business intelligence powered by Amazon QuickSight. Hear how Prijal leveraged Ingram Micro's Accelerated Partner Program to compress their go-to-market timeline and unlock new customer opportunities.Key Takeaways🚀 Proven in production — Prijal has real, live AWS workloads (EKS-based containerized apps, observability tools) running for 1.5+ years, cutting mean time to repair by ~70%.🌐 Edge AI for disconnected environments — Prijal pushes cloud-trained ML models out to edge locations (defense, healthcare, utilities, manufacturing) so operations continue even when cloud connectivity drops, then syncs data back to keep models sharp.💬 Conversational BI with QuickSight — Instead of static dashboards, execs can ask natural-language questions on the fly and get dynamic visual answers — no waiting on the dashboard team.🤝 Ingram Micro as an accelerator — The Accelerated Partner Program gave Prijal templates, hands-on guidance, and executive access that dramatically shortened their path to AWS's next partnership tier.Timeline00:00 – Welcome & introductions00:32 – Who is Prijal?01:52 – AWS technical capabilities & certifications03:32 – Bringing "information dominance" to the cloud04:38 – What is Edge AI?06:58 – Edge AI in action: defense, utilities, manufacturing08:25 – Conversational BI with Amazon QuickSight11:41 – How Ingram Micro shaped Prijal's go-to-market strategy13:17 – "A game changer" — the partnership payoff14:06 – Closing thoughts

Ray from Project Host joins Ingram Micro's AWS Partner Spotlight to discuss how their 20+ years supporting government and regulated cloud workloads has evolved into a fast, flexible path to FedRAMP and DoD authorization for ISVs. Ray shares how Project Host recently helped clients achieve full FedRAMP authorization in under four months — and what their growing AWS and Ingram Micro partnership unlocks for software vendors trying to break into the federal market.Key Takeaways🏛️ From best-kept secret to go-to partner — After 20+ years quietly serving regulated cloud workloads, Project Host recently earned AWS FedRAMP High certification and is now leaning hard into visibility through the AWS and Ingram Micro ecosystem.⏱️ 4 months, not 2–3 years — Using a reintroduced sponsorship model, Project Host took ISV clients like Siemens and Jamis through full FedRAMP authorization in under four months — a new record compared to the traditional 9–12+ month (or multi-year) timeline.🤝 An extension of your team, not a consultant — Unlike a "figure it out yourself" or pure-consulting model, Project Host takes end-to-end ownership of compliance — from kickoff through continuous monitoring and annual re-certification.💰 80% of ISV clients are already on AWS — With authorization now in hand, Project Host is positioned to capture a large share of ISVs trying to sell software into federal agencies, backed by Ingram Micro's discounting and support infrastructure.Timeline00:00 – Welcome & introduction00:35 – Project Host's 20-year journey & FedRAMP High certification01:49 – Balancing compliance requirements with business growth04:01 – Lessons learned supporting regulated cloud environments05:03 – Who Project Host's typical customers are (ISVs)05:46 – What the compliance journey looks like start to finish07:27 – Typical timelines: 9–12 months vs. a 4-month fast track08:57 – How AWS and Ingram Micro complement Project Host's platform10:51 – Trends shaping the future of government cloud compliance13:03 – What sets Project Host apart

Sanjay from CloudGov — the engineer who led the launch of AWS Control Tower — joins Ingram Micro's AWS Partner Spotlight to unpack why cloud costs spiral out of control, why AI spend behaves nothing like traditional cloud consumption, and how CloudGov's Commitment Shield and Token Shield products bring real-time financial and security governance to multi-cloud, AI-driven enterprises.Key Takeaways🏗️ Governance without killing agility — Sanjay's Control Tower work at AWS proved you can give developers fast, self-service cloud access without sacrificing security — a principle CloudGov now applies to multi-cloud and AI governance.💸 Three teams, no accountability — Engineering doesn't see cost, finance doesn't understand usage patterns, and procurement isn't in the room when technical choices are made — creating a governance gap no single team can close alone.🏎️ "There's only one minute in a minute" — Unlike fixed cloud compute, AI token consumption has no ceiling, misaligning provider incentives (sell more tokens) with customer incentives (spend wisely) — the "Ferrari to the grocery store" problem.🔒 AI needs its own security layer — Traditional endpoint security tools weren't built to catch PII/PHI/PCI leaking into AI prompts; Token Shield intercepts sensitive data at the token level before it ever reaches the model provider.Timeline00:00 – Welcome & introduction00:27 – Lessons from launching AWS Control Tower02:18 – Applying that pattern to multi-cloud & AI governance03:05 – Why cloud costs are so hard to see and control05:31 – Why disciplined teams still overpay (the commitment problem)07:47 – Why AI spend behaves differently than cloud spend09:46 – The "Ferrari to the grocery store" problem & Token Shield10:57 – The deeper risk: who's using AI, on what data13:09 – Why Ingram Micro was the right distribution partner