Iterate.ai

More enterprises scrapped AI projects in 2025 than in 2024. ?? S&P Global Market Intelligence found that 42% of companies abandoned most of their AI initiatives this year—up from just 17% last year. On average, nearly half of AI proof-of-concepts never make it to production. Why? Cost, data privacy, and security risks are the biggest blockers. At the same time, AI investment is at an all-time high, and most enterprises want AI to work. But there’s a critical disconnect: - AI isn’t a magic bullet. Many companies chase AI for AI’s sake instead of solving a real business problem. - Pilot purgatory is real. Two-thirds of companies struggle to transition AI pilots into full-scale production. - Failure is part of the process. AI isn’t plug-and-play—it requires iterative experimentation, and enterprises often don’t have the patience or structure for that. The most successful companies focus on clear, high-impact AI use cases, not just AI hype. They prioritize the right applications and accept that some projects will fail—but failure can lead to stronger, more refined strategies. Enterprises fail AI—when they don’t integrate it strategically. This is why we've seen such success with companies that use Interplay to build low code AI, that is scalable, secure, and 17x faster to build.

At NVIDIA's GTC 2025 conference, CEO Jensen Huang unveiled a transformative AI vision that will reshape enterprise technology strategies. Here are three critical takeaways that you need to know: 1?? AI is entering a new era of reasoning and physical intelligence. Huang emphasized we've moved from perception-based AI to generative AI, and now into agentic AI with reasoning capabilities. The next frontier: robotics powered by "physical AI" that understands concepts like friction, inertia, and cause-effect relationships. 2?? Synthetic data generation is revolutionizing AI training. Huang highlighted that human-generated training data can't scale to meet advanced AI needs. NVIDIA's Cosmos AI models generate photorealistic simulations for training robots and automated systems at a fraction of traditional costs. 3?? The hardware roadmap is accelerating. With Blackwell Ultra (2H 2025), Vera Rubin (2026), Rubin Ultra (2027), and Feynman (2028) architectures announced, NVIDIA is creating a clear path for enterprises to plan their AI infrastructure investments. The AI timeline is compressing. What seemed 5-10 years away is now 1-2 years out. Organizations without a clear AI roadmap risk being left behind as competitors leverage these technologies to automate complex physical tasks and operational workflows. Three strategic priorities are clear from Huang's talk: 1. Invest in AI infrastructure that can scale with this rapid evolution 2. Begin exploring synthetic data for training domain-specific models 3. Identify physical and operational processes that could benefit from reasoning-based AI and robotics Iterate is proud partners with NVIDIA, and how they are innovating at breakneck speed. How are you preparing for this accelerated AI timeline??

Retrieval-Augmented Generation (RAG) technology is how huge enterprises can handle thousands of customer inquiries instantly. RAG combines the power of AI with your company's proprietary knowledge base. Unlike standard chatbots that rely solely on generic training data, RAG-powered solutions can retrieve specific information from your databases, documents, and CRM systems to generate accurate, contextual responses. The benefits are huge: Cost Efficiency: RAG enables significant reduction in customer service operational costs Speed: Average response times drops Consistency: Every customer gets the same high-quality, accurate information Agent Satisfaction: Your team focuses on complex, rewarding work instead of repetitive queries Most importantly, RAG technology scales instantly with demand - no more hiring surges during busy seasons or struggling with staff shortages. We've implemented RAG-powered solutions across retail, banking, healthcare, and telecommunications, helping enterprises transform customer interactions from cost centers to competitive advantages. The best part is that implementation takes weeks, not months, with our low-code Interplay platform. What customer service challenges could AI solve in your organization? learn more about the technology: https://lnkd.in/geCDpYXw

?? AI Should Adapt to Your Business—Not the Other Way Around Many enterprises face a tough AI choice: ? Public cloud AI with limited control ? Private AI with security but restricted flexibility ?? Why not both? At Iterate.ai, we believe AI should be as flexible as the businesses using it—offering both public model access and fully private, on-premise deployments. With AI optionality, businesses can: *Choose between cloud, on-prem, or hybrid AI *Retain full control over AI data and security *Ensure compliance with GDPR, SOC2, HIPAA *Scale AI seamlessly without vendor lock-in The future of enterprise AI is secure, scalable, and built for choice. ?? Read the full blog here: https://lnkd.in/gRSV_Sip #EnterpriseAI #PrivateAI #AIInnovation #AIOptionality #SecureAI #IterateAI

?? Exciting News! Kevin Homer has been named a 2025 CRN Channel Chief for leading Iterate.ai’s channel-first strategy, expanding secure AI PC solutions through Intel, TD SYNNEX, and 50+ partners. With Generate, our privacy-first AI Assistant, businesses get powerful, local AI—no cloud required, no data exposure. Now available to 25,000+ resellers, Generate helps partners drive revenue while ensuring security. ?? AI should work for you—securely, locally, and efficiently. Congrats, Kevin J. Homer! ?? ?? Learn more: https://lnkd.in/gNvbrGe5 #AI #IterateAI #AIPC #CRNChannelChief #SecureAI #ChannelPartners

Frontline workers feel disconnected from company information systems and decision-making processes despite having the most direct customer contact. These workers typically access only 1/10th the information available to knowledge workers, creating an intelligence gap at the most critical customer touchpoints. The resulting knowledge silos prevent valuable operational insights from reaching decision-makers while frontline teams operate without the context needed for optimal performance. Iterate.ai's Frontline platform addresses this divide with AI-powered mobile applications designed specifically for associates in retail, manufacturing, and service industries. Quick-service restaurant chains using Frontline have meaningfully reduced employee turnover while improving customer satisfaction scores through better-informed, more engaged frontline teams. What technologies are you implementing to connect your frontline workers to the enterprise intelligence that could transform both their performance and your customer experience? https://lnkd.in/g8dMwPh7

Technical AI expertise remains concentrated in specialized teams, creating development bottlenecks where business units wait months for simple intelligence solutions. Data scientists spend much of their time on infrastructure and pipeline management rather than delivering the business value they were hired to create. This AI capability gap leads to shadow IT, where business teams implement potentially non-compliant solutions out of frustration with official development timelines. Iterate.ai's Interplay platform democratizes AI with a visual, drag-and-drop interface that allows business analysts to create sophisticated intelligent applications without deep technical expertise. Retail merchandising teams using Interplay have built their own inventory optimization systems in weeks, bypassing 6-month IT queue times while maintaining enterprise security and governance standards. How are you bridging the gap between technical AI capabilities and the business teams who understand exactly what problems need solving? https://lnkd.in/gPXSRVxP

Unlock the Power of Your Enterprise Knowledge with RAG-Powered AI Most AI models rely on pre-trained knowledge—but what if your AI could access and retrieve real-time insights from your internal documents, policies, and proprietary data? This is where Retrieval-Augmented Generation (RAG) comes in. With RAG-powered AI, businesses can: ? Instantly access internal knowledge for more accurate, context-specific responses ? Eliminate constant retraining by retrieving updated data in real-time ? Ensure full privacy & compliance by keeping AI models within company firewalls ? Automate customer support, sales enablement, and market research with AI that truly understands your business At Iterate.ai, we build custom AI solutions that leverage RAG to make enterprise AI more relevant, secure, and intelligent than ever before. Read the full blog here: https://lnkd.in/grEFf-FT #EnterpriseAI #RAG #RetrievalAugmentedGeneration #GenerativeAI #DataSecurity #IterateAI

Centralized cloud AI creates latency issues that make real-time applications impossible in environments where milliseconds matter for critical business decisions. When your manufacturing floor can't wait 200ms for cloud processing or your retail analytics depends on spotty connectivity, cloud dependency becomes your biggest competitive liability. These edge cases require specialized AI deployment that balances model performance with hardware constraints while maintaining enterprise security standards. Iterate.ai's Interplay addresses this challenge with edge-optimized AI nodes that can be deployed on local hardware, enabling real-time intelligence without internet dependency. Manufacturing clients using Interplay's edge capabilities have reduced defect detection time from 200ms to 15ms, transforming what was previously a manual quality control process into an automated system with higher accuracy. What processes in your organization could benefit from intelligence at the edge rather than relying on centralized cloud processing with its inherent latency?

Your customer service representatives navigate multiple different systems during a single customer interaction, creating frustrating delays and inconsistent responses. These context switches add minutes to each call while customers grow increasingly impatient with repetitive questioning and disjointed service experiences. The resulting customer dissatisfaction directly impacts retention, with many customers citing poor service experiences as their reason for switching to competitors. Iterate.ai's Generate platform transforms customer service by providing representatives instant access to intelligent document retrieval across all enterprise knowledge bases through a single interface. Major financial and insurance companies using Generate-powered customer service have reduced call times while simultaneously dramatically increasing first-call resolution rates. Is your customer experience infrastructure built for intelligence that anticipates needs, or is it still forcing representatives to hunt for information across disconnected systems?