Aquant has been named to Destination CRM’s2025 Top 100 CRM Companies list, recognizing the most innovative companies transforming how organizations engage with customers and drive outcomes. This year’s list spotlights pioneers in agentic AI, the underlying intelligence that enables systems to reason, plan, and adapt.
Aquant’s recognition highlights its leadership in delivering agentic AI solutions tailored to the complex needs of service organizations in manufacturing, including medical devices, industrial equipment, and other industries. Rather than relying on generic chatbots or copilots, Aquant’s platform functions as a true AI-powered service partner – guiding users through diagnostics, recommending the right parts and fixes, identifying training opportunities, and enabling smarter decision-making at every step of the service lifecycle through customized, user-specific insights.
“Our AI doesn’t just answer questions, it understands complex equipment, learns from our customers’ best technicians, and gets smarter with each interaction,” said Assaf Melochna, CEO and Co-founder of Aquant. “We’ve built technology that acts with purpose in high-stakes environments, and we’re proud to be shaping the future of service with agentic AI.”
According to CRM Magazine, agentic AI represents the next major shift in CRM, with far-reaching implications across industries. Backing this trend, Gartner predicts that by 2029, agentic AI will autonomously resolve 80% of routine customer service issues and cut operational costs by 30%. By 2030, 50% of all service requests will be initiated by machine customers.
TheWall Street Journalrecently put it bluntly: “Everyone’s talking about AI agents. Barely anyone knows what they are.” And yet, “agentic AI” has quickly become the latest buzzword in the tech world. Every company claims to need it. Every tool claims to offer it. But what is it really, and what does it mean for industries where fully autonomous AI may not be viable or even desirable?
What Is Agentic AI? And How Is It Different from an AI Agent?
When you hear the term, you might picture an AI system that autonomously executes tasks from start to finish, making its own decisions along the way. While that’s partially true, agentic AI isn’t a binary concept, it exists on a spectrum, with varying levels of autonomy and decision-making capabilities.
Essentially, agentic AI is the underlying intelligence that allows systems to reason, plan, and adapt. But autonomy alone doesn’t equal sophistication. In some industries, it is essential to have oversight of humans to ensure higher trust, explainability, and control standards. In others, full autonomy is not only sufficient, it’s preferred.
An AI agent, on the other hand, is a specific system designed to complete a task or set of tasks. The key distinction? Agentic AI is the underlying intelligence that enables reasoning, planning, and adapting. AI agents are the applied manifestations of that intelligence. Not all agentic AI results in fully autonomous agents, which can be a good thing, especially in industries where liability, safety, and physical action are at play.
That’s why the most effective agentic AI isn’t defined by how independently it operates, but by its ability to adapt to the situation at hand. Sometimes it acts on its own. Sometimes it defers. The smartest systems aren’t just capable of thinking, they’re capable of knowing when to think and act by itself and when to involve a human.
Breaking Down the Agentic AI Spectrum
Rather than thinking in binary terms (“agent” or not), it’s more useful to map agentic AI across a continuum from simple automation to systems capable of dynamic reasoning and real-world decision-making.
Far Left: Basic Workflows, No AI
These systems are completely static and deterministic. They execute predefined tasks the same way every time, regardless of context or data.
Scripted macros that perform a fixed series of actions in tools like spreadsheets
Logic-based automation triggers (e.g., “If X, then Y”) that require no learning, adaptation, or reasoning
Zero flexibility or awareness of changing conditions
Left: Scripted AI Systems
These systems add some surface-level “intelligence,” but still follow rigid workflows. They can mimic user actions and automate repetitive tasks, but only within narrowly defined boundaries.
Bots that mimic human clicks and keystrokes
Predefined chat flows with no contextual understanding
Middle: Human-Centered AI Support
Here, AI begins to demonstrate what the majority consider real agentic capabilities, reasoning, adapting, and augmenting human expertise. These systems assist with decisions, analyze data, and streamline complex processes, but always with human oversight.
AI tools that recommend next steps or surface insights
Systems that identify training gaps or triage cases based on context
Virtual assistants that automate common workflows while escalating edge cases to humans
Right: Autonomous Agents
These AI systems handle multi-step tasks and operate across digital environments with minimal human intervention. They can reason, make decisions, and adapt dynamically, but still within predefined enterprise guardrails.
Agents that complete end-to-end workflows by interacting with multiple systems, like CRMs, ERPs, and ticketing tools
Systems that detect issues, execute fixes, and escalate when needed
AI that responds to natural language prompts by performing a sequence of digital actions
Far Right: Fully Autonomous Agents
At the extreme end, these agents require little to no human input. They reason, plan, and act independently in complex environments, including software development, logistics, and real-world operations. Fully autonomous agents are emerging in specific, high-control environments, but for most enterprises, we’re still in a phase where autonomy must be paired with oversight.
Systems making real-time decisions in physical environments (e.g., autonomous vehicles) without a human-in-the-loop
Autonomous robots in warehouses (e.g., Kiva robots at Amazon) that navigate, retrieve, and sort items based on real-time demand and spatial awareness
AI agents managing network traffic in real time (e.g., load balancing, bandwidth allocation) to maintain performance without human intervention
This spectrum-based view of agentic AI aligns closely with recent Gartner research, which outlines a fragmented but rapidly evolving landscape of AI agent platforms, from prebuilt, no-code solutions to sophisticated agent-training environments. Gartner emphasizes that not all AI agents are created equal, and that organizations should start with practical use cases before pursuing fully autonomous systems. This reinforces our belief that the smartest AI isn’t necessarily the most independent, it’s the most adaptable, contextually aware, and human-aligned.
Redefining “Advanced”: It’s Not About Autonomy Alone
Autonomy is often seen as the pinnacle of AI advancement, and in many ways, it is. Building systems that can reason, plan, and act independently is a massive technical achievement. But in enterprise environments, the most advanced AI isn’t always the most autonomous, it’s the most contextually intelligent.
In high-stakes industries, “advanced” should mean:
Deep semantic and contextual understanding
Seamless integration across complex systems and fragmented data
The ability to support, not override, human decision-making
Automation with precision, not automation for its own sake
The Maturity Gap: Tech Is Ready, Enterprises Aren’t (Yet)
The technology is here (or nearly here). But the real question is: Are enterprises ready to release the guardrails? In most cases, the answer is no, and the horizon of when enterprises will feel ready is unclear. Accountability, compliance, and risk mitigation still matter more than pure autonomy.
Until enterprises mature their readiness, we must accept that more autonomy doesn’t always translate into better decisions. In fact, the smartest use of AI right now often involves “training wheels”, systems that assist and adapt without taking over.
Smarter AI Doesn’t Mean Less Human
Agentic AI isn’t a race to eliminate people, however, it’s an opportunity to amplify human expertise with more intelligent, more situationally aware tools. The real breakthrough isn’t building AI that acts alone. It’s building AI that knows:
When to act
When to collaborate
And when to defer
In industries where the cost of a wrong decision is measured in lives, safety, or millions of dollars, the future isn’t full autonomy. It’s intelligent collaboration between humans and machines, with autonomy used purposefully, not blindly.
A 2024 report from IHL Group paints a clear picture: 80 percent of AI projects fail. Despite large budgets, strong interest, and growing momentum, most AI initiatives in retail never move beyond the pilot phase. PwC’s 2024 Hopes and Fears survey adds another layer to this reality. While employees are becoming more optimistic about AI’s potential to improve productivity and job satisfaction, many still feel uncertain. They’re unsure how the technology will be rolled out and whether their organization is ready to lead that kind of change. A 2023 article in Harvard Business Review echoes this concern, pointing to unrealistic expectations, poor strategic alignment, and the challenge of integrating AI into existing workflows as major causes of failure.
Generative AI is still in its early stages, and some of these struggles can be chalked up to growing pains. But when you look more closely, it becomes clear that technical complexity is not the main reason projects fall short. Building AI systems is hard. It requires deep expertise, cross-functional collaboration, and significant investment, but even the most advanced AI products will stall if the organization isn’t ready to adopt and operationalize them.
The deeper problem is organizational. Too often, teams are unclear about the problem they are solving, underestimate the resources required, or face pushback from employees who feel left out of the process.
IT and AI leaders need more than technical know-how. They need to be able to guide their organizations through change. This is not just about launching a product or delivering a new capability. It is about creating clarity, building trust, and helping people adjust their behaviors.
John Kotter’s 8-Step Process for Leading Change offers a clear structure for navigating transformation. It outlines what is needed to move from a compelling vision to actual, lasting results.
Here is a quick look at the eight steps:
From the perspective of an IT/AI leader, it helps to think about these steps in two phases.
The first three are about creating the conditions for change. You set the tone, rally the right people, and clarify the path ahead. These steps are often led by senior leadership and happen early in the process.
The remaining five steps are about delivering results. They involve turning the vision into reality, keeping people engaged over time, and ensuring the change sticks. This part is slower, more complex, and much more people-driven. It touches teams across the entire organization and requires a strategy that prioritizes people just as much as it prioritizes technology.
Publications like Harvard Business Review and Business Insider point to a recurring theme. Employee resistance to AI comes from uncertainty and a lack of understanding. People feel anxious when they don’t fully grasp what the technology does or how it will affect their role. They worry about losing control or being replaced. As Harvard Business Review puts it, “when employees feel like change is being done to them rather than with them, resistance is inevitable.” Business Insider adds that AI rollouts fall flat when organizations fail to show employees how the change benefits them personally, not just the business.
In the absence of clear communication, fear often fills the gap. Employees may question the new tools’ accuracy, reliability, or usefulness. They may disengage, push back, or quietly revert to old habits.
This is why a people strategy matters. Here are five core principles to help shape it:
1. Create a Positive First Experience
When my kids were young, I was nervous about taking them to the dentist for the first time. We found a clinic in Brookline, Massachusetts, that had carefully designed the experience to put children at ease. The waiting room felt warm and welcoming. The staff explained everything calmly and clearly. At the end of the visit, the dentist gave my son a printout of a selfie with him to hang on the wall in his room, and a small keepsake box for his baby teeth. He had two teeth removed that day without even realizing it. Eight years later, my kids still enjoy going to the same dentist.
Your AI launch should be no different. Design the training and onboarding experience to be simple, supportive, and confidence-building. A positive first interaction creates momentum and builds trust that lasts.
2. Identify Change Ambassadors
Every organization has people whom others look to for guidance. These individuals are not always senior leaders. They might be team leads, long-time employees, or well-respected peers. Identify and involve them early. When these individuals show confidence in the change, others are more likely to follow.
3. Make the Benefits Personal
Every employee is asking the same question: how does this help me? Speak directly to their day-to-day experience. Show them how the technology will reduce friction, save time, or make their work more meaningful.
But don’t stop there. Personal benefit goes beyond efficiency. In many cases, the most compelling motivator is professional growth. Using the new tool might give someone an edge in the job market by helping them learn a technology that is increasingly in demand. This is especially relevant for technical roles, like field technicians or IT professionals, who see these skills as building blocks for future opportunities.
There is also room to design internal career advantages. For example, you can tie usage of the tool to certifications, access to new roles, or eligibility for promotion. One Satellite TV organization I worked with made this tangible. They introduced a new field system alongside upgraded vans for technicians. The new tool allowed each technician to manage their van stock independently. The message was clear: they were now running their own mobile business. It wasn’t just about learning a new system, it was about taking ownership, gaining autonomy, and building credibility inside the company.
When employees see personal value, whether that means more control, new skills, or career momentum, they become far more likely to engage with the change.
4. Teach People How It Works
One of the best examples I’ve seen came from a customer who built a Star Wars-themed scavenger hunt into their AI rollout. Each day for five days, users received clues that led them through specific features of the product. The challenge was playful and engaging, but it also taught users how the system worked and gave them a chance to explore it at their own pace. Their change management leaders were the best I’ve seen. Thoughtful, creative, and deeply aligned with what PwC’s 2024 survey found that employees are far more likely to embrace AI when they receive support and training.
5. Design for Operational Fit
Even the most advanced AI solution will fail if it doesn’t fit seamlessly into how work actually gets done. Integration is not just a technical hurdle. It is an organizational one. If teams need to switch between systems, duplicate inputs, or interrupt their normal flow, adoption will stall. As Harvard Business Review notes in the article Keep Your AI Projects on Track (Krishnan, Singh, and Xu, November 2023), “AI must be embedded in core business processes to deliver value. It shouldn’t be treated as an add-on or a side experiment.”
To succeed, AI tools should be designed with real workflows in mind. This means mapping out the daily tasks of the people expected to use the system, identifying where AI can reduce friction or add clarity, and building integrations that feel intuitive and supportive. When the solution fits into the existing rhythm of work, it becomes useful, usable, and sustainable.
In Summary
AI transformation is not simple. It is a complex, high-stakes undertaking that requires both technical excellence and organizational maturity. Projects often fail, not because the technology is flawed, but because the change it demands from people is underestimated.
Employees resist change when uncertain about what it means for their role, when communication is vague or top-down, and when they are left out of the journey. Fear and skepticism fill the void where clarity and support are missing.
Successful transformation depends on more than rollout plans and training sessions. It calls for a comprehensive people strategy that is as deliberate and well-resourced as the technical one. That includes crafting positive early experiences that build trust, identifying respected change ambassadors, making the impact of AI tangible and relevant to each individual, and investing in real learning. Not just tool demos, but a deep understanding of how the system works and why it matters.
Frameworks like John Kotter’s 8 Steps for Leading Change remind us that change must be led thoughtfully. Usually, leaders do an intense job in the early stages, such as building the business case for change, creating urgency, and aligning the right stakeholders. But the real test lies in what comes next. The remaining steps, which involve broad-based action, sustained momentum, and cultural integration, require a longer horizon and far deeper engagement across the organization.
This work is not easy. But when done with intention, empathy, and discipline, it is possible to turn AI from a stalled initiative into a sustainable, people-powered transformation.
In business, one thing is certain – change. Market conditions shift. Competitive landscapes evolve. Leadership priorities adjust. What mattered six months ago may no longer be the focus today.
Yet, most AI systems operate as if strategic goals are fixed. They’re built to optimize for one outcome at a time – whether it’s reducing costs, increasing efficiency, or improving customer satisfaction. But what happens when priorities shift?
Without agility, AI quickly becomes a liability rather than an asset. That’s why the future of AI isn’t just about intelligence – it’s about adaptability.
With KPI Optimization, AI finally moves at the speed of business. This feature allows organizations to define their goals and key performance indicators (KPIs), ensuring that every AI-driven recommendation and insight is tailored to those objectives in real time – without costly reconfiguration or retraining.
Balancing Multiple, Often Conflicting, Business Goals
Organizations often need to balance multiple, sometimes conflicting, priorities. For example, service leaders need to reduce costs while improving first-time fix rates. These goals can often be at odds with each other—cutting expenses might mean limiting parts usage, but a lack of the right parts could result in repeat visits and lower fix rates.
This is where AI excels. A truly intelligent system can optimize for multiple KPIs simultaneously, ensuring that no single objective is prioritized at the expense of another. AI-powered optimization ensures businesses can make smarter trade-offs, dynamically adjusting recommendations to drive efficiency without compromising service quality or customer satisfaction.
The Role of AI in Change Management
Organizations don’t have the luxury of waiting for technology to catch up with strategy. Business leaders and employees are constantly adjusting to shifting corporate goals. AI should do the same – if not faster.
Effective change management hinges on alignment – ensuring that people, processes, and technology are moving in the same direction. But traditional AI models, locked into static processes, often slow progress rather than accelerate it.
A truly agile AI platform should:
Evolve alongside leadership priorities: Whether the focus is on operational efficiency today or market expansion tomorrow, AI should dynamically adjust to support new KPIs.
Eliminate friction in decision-making: AI should be an enabler, not a roadblock. When teams pivot, AI should pivot with them, providing insights that support immediate business needs.
Reduce the burden on IT and data teams: Instead of requiring costly model retraining or rule-based updates, AI should seamlessly adapt without additional investment.
For example, a service team at a medical device manufacturing company might begin the year optimizing for First-Time Fix (FTF) rates, ensuring technicians resolve equipment issues on the first visit. But by mid-year, economic pressures may shift focus toward reducing parts spending or improving workforce productivity. With KPI Optimization, AI can immediately realign to these new priorities – without requiring a lengthy, expensive overhaul.
This level of adaptability isn’t just beneficial – it’s essential for staying competitive in any industry.
Why AI Must Measure and Benchmark KPIs Specific to Your Domain
Not all KPIs are created equal. Businesses across different industries operate under unique conditions, making generic AI models ineffective at truly driving impact. A domain-specific AI solution must be able to measure and even benchmark KPIs that are tailored to the industry it serves.
Benchmarking against industry-specific metrics allows businesses to understand how they compare to peers and identify areas for improvement. AI that is trained on domain-relevant data can optimize toward the KPIs that matter most—ensuring insights are meaningful, actionable, and aligned with real-world operational goals.
Without this level of specificity, AI models risk making recommendations that might work in theory but fail in practice. The ability to benchmark performance against industry best practices is a game-changer, helping organizations continuously improve and refine their strategies.
The Future of AI: Agility Over Static Intelligence
For AI leaders, investors, and businesses evaluating AI platforms, one question should take center stage:
Can your AI adapt as fast as your business evolves?
Rigid AI models that require extensive intervention to accommodate change will quickly become obsolete. The next generation of AI – one that aligns with strategic goals in real-time – will define the future of industry-wide digital transformation.
This shift will separate AI companies that truly understand business from those that merely provide technology. Investors should look for AI solutions that prioritize adaptability, because the companies that embrace agile AI will be the ones driving market leadership in the years ahead.
Adaptability isn’t a luxury – it’s a necessity.
If AI isn’t keeping pace with business evolution, it’s already falling behind.
Assaf Melochna is the President and co-founder of Aquant, where his blend of decisive leadership and technical expertise drives the company’s mission. An expert in service and enterprise software, Assaf’s comprehensive business and technical insight has been instrumental in shaping Aquant.
The AI startup landscape is booming. New companies pop up every day, all claiming to have game-changing technology that’s going to transform entire industries. But the truth is, very few of them actually make it past the flashy demo phase.
As a founder who’s been building an AI company for the past eight years, I’ve learned that scaling isn’t just about having a great model — it’s about making a series of smart, sometimes tough decisions, and ensuring those decisions are proven in the battlefield of real-world deployments.
So, what separates the AI startups that scale from the ones that stall? Here’s what I’ve learned along the way.
1. Solve Specific Business Problems
Your AI is only as strong as the problems it solves. It’s not about having the flashiest model or the most advanced algorithms, it’s about solving a specific business problem and delivering real, measurable value to your customers.
The best way to do that? Start with a strong data strategy. Your product is only as good as the data behind it — and if you’re relying solely on publicly available or generic datasets, you’re already at a disadvantage.
That said, I’ve seen some innovative startups use public datasets to augment their historical data in creative ways. For example, 90% of Americans live within 10 miles of a Walmart store. Mapping out 10-mile radius polygons around each store could help define service territories based on population density and access.
But in most cases, the AI startups that successfully scale gain their competitive edge by using high-quality, domain-specific data that’s tightly connected to the real-world problems they’re solving. It sounds simple. Many do think it’s simple. McKinsey published that only 3% of companies who adopted AI projects could do it at scale — meaning only 3% achieved at least 20% of their EBIT from AI. The other 97%? They either tried and failed, or found only local success.
How we did it? We powered our product with data that no one else has, whether that’s through exclusive partnerships, unique data collection methods, or customer-generated data that compounded in value over time. This proprietary data makes our product better. You can do it too.
What can you do to create a top-notch data-strategy:
Reduce Noise. Curated, well-labeled, and domain-specific data beats massive but noisy datasets.
Continuously improve data pipelines. Scalable companies treat data collection, cleansing, and enrichment as a core capability, not an afterthought.
Incorporate real-world feedback loops. Successful AI companies ensure their models learn and adapt from actual customer interactions and evolving use cases.
Create defensibility. Whether through unique data partnerships, customer-generated data, or first-mover advantage in a niche domain, scalable startups make their data assets difficult for competitors to replicate.
We first applied AI to help a medical device customer reduce equipment downtime. Their service data was a mess — unstructured notes, shorthand, and inconsistent terms. But by building AI that understood their specific service language, we not only solved their problem but created the foundation for a scalable product that adapts to any service organization’s unique data.
Takeaway: The best AI startups don’t just build great models, they solve meaningful problems with data no one else can access.
2. Keep Pace with AI Innovation Without Chasing Every Trend
In AI, innovation moves fast, and scalable startups know how to strike the right balance between staying cutting-edge and staying focused.
You need to keep an eye on emerging models, new techniques, and advances like small language models (SLMs), multi-modal AI, agents, or retrieval-augmented generation (RAG). But you also need the discipline to avoid jumping on every new trend just because it’s popular. The most successful AI startups build a clear framework for evaluating new technology — asking questions like:
Does this advance actually improve our core product or customer outcomes?
Is it mature enough for production, or still experimental?
Will it introduce unnecessary complexity into our stack?
I’ve been at this for a while and I know how hard it can be to not chase every trend – especially when it feels like everyone else is and you’re at risk of falling behind. But over time, I’ve learned the importance of evaluating new models and techniques without blindly adopting them. At Aquant, we only bring in what actually improves the precision, adaptability, or efficiency of our service intelligence platform. That mindset – focused innovation – helps us stay ahead without getting distracted.
Takeaway: Scalable AI companies don’t just adopt the latest technology – they adopt the right technology for their product, customers, and long-term vision.
3. Infrastructure Built for Scale, Not Just Speed
What I see a lot is people who can build the first 60% of a product very fast. But the remaining 40% – that’s where the real art comes in, requiring a lot of work, resources, and patience. It’s like building a new house. Putting up the frame can happen relatively quickly, but turning it into a home takes far more time, care, and attention to detail.
It’s easy to build a flashy prototype or train a model with off-the-shelf tools. But scaling that model into a reliable, cost-effective product that supports thousands of customers across geographies, industries, and regulatory environments? That’s an entirely different challenge.
What scalable startups do differently:
Design for deployment flexibility. They plan for multi-cloud, hybrid, and on-prem deployments – because customers’ infrastructure preferences vary widely.
Optimize for cost-efficiency early. Running AI models can get expensive fast. The best startups build in cost optimizations around inference, data storage, and compute from the start.
Embed monitoring and governance. Scalable AI companies know that AI products aren’t static – they continuously monitor performance, detect drift, and ensure compliance with evolving regulations.
Plan for extensibility. Whether it’s adding new data sources, expanding to adjacent use cases, or adapting to new regulations, scalable infrastructure makes it easy to evolve without rebuilding from scratch.
Takeaway: Scalable AI companies design infrastructure with the assumption that every part of the system — from model training to inference to data governance — will need to grow and adapt over time.
It’s not about how fast you launch – it’s about how well you scale
The next generation of AI leaders won’t be defined by how fast they launch – but by how well they scale.
This is something I’ve seen firsthand throughout my career, from my time in the intelligence community where we had to turn massive amounts of data into clear, actionable insights, to my years working with service organizations trying to make sense of their service data. Shahar and I started Aquant because we knew AI could solve real business problems, if it was powered by the right data, tailored to the right domain, and built to evolve as customer needs changed.
Scaling an AI company is never just about the technology – it’s about staying focused on solving the right problems, making smart bets on innovation, and building a foundation that can adapt and grow for years to come.
SiliconAngle’s Top AI-enabled Product category recognizes cutting-edge AI-enabled or AI-enhanced products that have significantly impacted and transformed how people do their jobs in their respective sectors. This award recognizes solutions that demonstrate exceptional effectiveness, creativity and adaptability in addressing their sector’s key challenges and driving progress.
“Winning SiliconANGLE Media’s Top AI-Enabled Product award reaffirms our commitment to building an AI platform service professionals can trust,” said Assaf Melochna, President & Co-Founder of Aquant. “Aquant offers a seamless experience that goes beyond issue resolution, proactively guiding users with the right answers and continuously learning from expert insights. By tailoring solutions to each role and machine, Aquant transforms service into a strategic advantage that drives efficiency and elevates customer experiences.”
The Tech Innovation CUBEd Awards recognize exceptional achievements in technological advancement, highlighting the diverse contributions of the companies and individuals shaping the future of B2B and B2B2C technology. This technology awards program recognizes the most innovative companies (public, private and startups), visionary leaders and groundbreaking products that are pushing the boundaries of what’s possible. Aquantwas selected from a competitive field of nominees by a panel of industry experts and technology leaders.
“The winners of our inaugural Tech Innovation CUBEd Awards represent some of the boldest thinkers and determined innovators in the tech industry,” said Dave Vellante, co-founder and co-CEO of SiliconANGLE Media. “Each person, company and product honored has proven that true breakthroughs happen when we dare to challenge traditional conventions and pursue ambitious visions.”
Service data is inherently complex, and Aquant is purpose-built to make sense of it. It helps service teams – whether it’s technicians, call center agents, managers or even end-customers – quickly find the best solutions to whatever problems they’re facing. Instead of digging through manuals or trying to track down an expert, they can just ask Aquant, and it pulls from all kinds of data—machine history, past repairs, technician notes, expertise from top technicians, you name it—to give the most relevant, expert-level recommendations.
“Today, we honor excellence across the full spectrum of innovation—from the visionary leaders who inspire us, to groundbreaking products that transform industries, to the companies that make it all possible,” said John Furrier, co-founder and co-CEO of SiliconANGLE Media. “Our awards program celebrates the courage to think differently, the persistence to overcome obstacles, and the vision to transform bold ideas into real-world impact.”
SiliconANGLE Media is a recognized leader in digital media innovation, bringing together cutting-edge technology, influential content, strategic insights and real-time audience engagement. As the parent company of SiliconANGLE, theCUBE Network, theCUBE Research, CUBE365, theCUBE AI and theCUBE SuperStudios — such as those established in Silicon Valley and the New York Stock Exchange (NYSE) — SiliconANGLE Media transforms the way technology companies connect with their target markets. Founded by tech visionaries John Furrier and Dave Vellante, SiliconANGLE Media has built a powerful ecosystem of industry-leading digital media brands, with a reach of 10+ million elite tech professionals, 4+ million SiliconANGLE readers and 250,000+ social media subscribers. The company’s new, proprietary theCUBE AI LLM is breaking ground in audience interaction, leveraging CUBE365’s neural network to help technology companies make data-driven decisions and stay at the forefront of industry conversations.
Today, Aquant announced the launch of Aquant AI for Service Professionals—the next generation of its enterprise SaaS platform. This latest release delivers a seamless, AI-powered experience that allows service professionals to ask any question and get the right answer at the right time for every service interaction.
More than just a troubleshooting tool, Aquant AI is a true end-to-end platform, providing expert-level guidance across every step of the service process—from diagnosing issues to accessing documentation, ordering parts, analyzing workforce trends, and reviewing technical schematics. The AI proactively anticipates users’ needs, offering suggestions even when they don’t know what to ask. And with continuous learning, the platform refines its accuracy over time, integrating real-world expertise and user feedback to ensure responses remain relevant and reliable.
What’s new in this next-generation release?
By leveraging Agentic AI, Aquant enhances the service experience with:
A Seamless Experience: A unified environment where any service professional can ask a question and receive an answer tailored to their specific job and machine. Aquant AI intelligently analyzes user intent and asset history to apply the right capabilities.
Beyond Issue Resolution: Service isn’t just about fixing problems. Aquant AI equips professionals with the right information and tools to get the job done efficiently, whether it’s accessing service manuals, ordering parts, or reviewing schematics.
Proactive Guidance: Oftentimes, users don’t know how to articulate the question that will get the right answers to the challenges they face. The platform removes the guesswork by suggesting relevant questions and guiding users to the best solutions, even before they ask.
Continuous Learning: Aquant improves relevancy over time, continuously capturing user feedback and expert insights to keep answers accurate and aligned with real-world service scenarios.
“Aquant AI is ready for anything. It is built to be as dynamic as the service teams who rely on it,” said Assaf Melochna, President and Co-Founder at Aquant. “The latest innovations to the platform ensure that every call center agent, field technician, service leader, and even end-customer always has the best solution at their fingertips. With Aquant AI, service teams are more prepared, more efficient, and more empowered than ever before.”
How has the platform improved?
With this latest release, users can now “Ask Aquant” and receive immediate, AI-powered guidance—all within a single, seamless interface. The platform dynamically routes users to the right tools and information, breaking down silos and ensuring data flows effortlessly across service applications. Whether tapping into structured documentation, unstructured technician notes, or expert knowledge, Aquant delivers precise, role-specific answers that adapt to the user’s experience level, asset complexity, and service history.
By providing expert-level insights and continually evolving with user input, Aquant AI for Service Professionals accelerates problem resolution, improves decision-making, and drives greater efficiency across service operations – turning service from a cost center into a revenue generator through smarter, more proactive service delivery.
AI is evolving fast, and DeepSeek-R1 is proof that AI innovation isn’t just about bigger datasets – it’s about smarter design. Unlike traditional models that rely on sheer scale, DeepSeek focuses on reinforcement learning, fine-tuning, and data distillation to improve reasoning without ballooning in size.
For organizations looking to invest in AI this year, DeepSeek’s emergence raises a critical question: Should you build your own AI, or is it more strategic to leverage existing models?
DeepSeek as a Reality Check for AI Development
Many organizations assume that building their own AI is the best way to stay competitive. However, the reality is far more complex. DeepSeek offers a valuable case study in what it really takes to develop AI successfully:
AI success isn’t just about scale. DeepSeek proves that intelligence comes from better training and fine-tuning, not just bigger datasets.
Building an AI model is only the beginning. The real work starts with continuous training, updates, and reinforcement learning.
Efficiency beats sheer size. DeepSeek’s data distillation techniques show that smaller, specialized models can outperform larger ones in certain tasks.
While DeepSeek’s advancements are impressive, they haven’t yet outpaced the most advanced U.S. models. Dario Amodei, CEO of Anthropic, pointed out that DeepSeek’s latest model is roughly on par with U.S. models that are 7-10 months older but hasn’t surpassed them. Compared to Claude 3.5 Sonnet – one of Anthropic’s flagship models – DeepSeek trained its model at a lower cost, though not as significantly lower as some have suggested.
What This Means for Organizations Investing in AI
For companies considering AI investments in 2025, DeepSeek highlights a pivotal shift: AI success isn’t about raw computational power but about efficiency, customization, and strategic deployment.
Here are key takeaways for business leaders evaluating their AI strategy:
The build vs. buy debate is evolving. DeepSeek shows that smaller, well-trained models can compete with larger ones, but building in-house still requires significant expertise and resources.
Customization is often the smarter path. Instead of building from scratch, leveraging and fine-tuning existing AI models can offer better cost efficiency and faster time to value.
Industry expertise is critical. Great AI isn’t just about engineering – it needs domain-specific knowledge and real-world data to deliver meaningful impact.
AI requires continuous investment. Whether you build or buy, AI is never “done.” Ongoing training, updates, and monitoring are essential to maintain performance.
The Bottom Line
DeepSeek’s rise is a wake-up call for organizations investing in AI. It’s not just about who has the biggest model – it’s about who can deploy AI in the most effective, scalable, and cost-efficient way. As businesses refine their AI strategies in 2025, the key to success will be striking the right balance between innovation, investment, and operational efficiency
Last week, Aquant’s Sales, Customer Success, and Leadership teams came together in New York City for our annual Sales Kickoff — a chance to align, strategize, and build momentum for the year ahead. But this year wasn’t just about business goals. It was about reinforcing what makes Aquant truly special: our people, our innovation, and our commitment to customer success.
One Aquant, One Purpose
Our theme this year — “One Aquant” — was more than just a slogan. It was a feeling that resonated throughout every session, conversation, and moment of the event. We’re stronger when we work together, and this kickoff reminded us why collaboration across Sales, CS, Product, and Engineering is the key to helping our customers thrive.
Unforgettable Moments & Game-Changing Insights
Here are some of the biggest highlights from the two-day event:
🔹 Jaw-dropping product innovation – Our Innovation Lab showcased exciting new capabilities that will elevate how we serve our customers. 🔹 Powerful customer insights – Hearing directly from Wallace Mahaffy at Terex and Mike Galon from Coca-Cola and Marc Noble from Waters reinforced why we do what we do — and how we can continue delivering even greater value. 🔹 Unified messaging & strategy – With major updates to our positioning, demo strategy, and sales approach, we’re set to tell an even more compelling Aquant story. 🔹 Celebrating our people – From the Awards Ceremony to team dinners, we took time to recognize the dedication and talent that drive our success.
Looking Ahead
With renewed energy and a clear mission, we’re entering 2025 stronger than ever. The insights we gained, the connections we strengthened, and the innovations we unveiled are setting the stage for an incredible year ahead.
A huge thank you to everyone who helped make this kickoff a success. Now, let’s go out there and get it done right—together.
Assaf Melochna
