In this conversation from the Google Cloud Partner AI Series, Sanjay Singh, CEO of Onix, and Colleen Kapase, VP of Channel and Partner Programs at Google Cloud, join theCUBE’s John Furrier to explore how strategic IP and deep domain expertise are reshaping AI service delivery across verticals. Singh shares how Onix has embraced a “service as software” approach, enabling 2x faster results at half the cost for Fortune 500 clients in telecom, healthcare, retail and finance. Central to this is Wingspan, Onix’s IP-led platform that streamlines data discovery, automates modernization and powers agentic AI systems with real-world outcomes.
Kapase highlights how Onix’s vertical-first focus, combined with tight integration across the Google Cloud stack, makes them a standout AI partner. Together, they unpack the rise of AI agents and the growing need for agent orchestration, contextual LLMs and simplified enterprise AI consumption. From domain-specific language models to drag-and-drop agentic workspaces, this conversation dives deep into what it takes to move from AI ambition to AI execution – at speed and scale.
Watch to learn how Onix and Google Cloud are advancing enterprise-grade AI through innovation, infrastructure and integration.
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Sanjay Singh, Onix & Colleen Kapase, Google Cloud
Exploring AI Partnerships with Onix and Google Cloud: A Deep Dive into Service as Software
Colleen Kapase, Vice President of channel and partner programs at Google Cloud, and Sanjay Singh, CEO of Onix, join to discuss the innovative approaches and partnership strategies in the Google Cloud AI Partners Series. This session highlights the fusion between technical integration and business collaboration, showcasing how these partnerships reshape the AI landscape.
In this discussion, Singh introduces Onix as a pioneer in the service-as-software space, explaining the company's nontraditional approach to building an intellectual property-led services platform that delivers outcomes faster and more cost-effectively. Kapase emphasizes the significance of having the right data foundation in AI, exploring how Onix's expertise in vertical-specific solutions aligns with Google Cloud's technological advancements. This segment facilitates a deep dive into the strategic integrations and collaboration efforts enhancing customer experiences.
Key insights from the video include the introduction of Onix's Wingspan and Onix Canopy platforms, which redefine service delivery and operational efficiency through advanced data engineering and AI automation. Both leaders share insights on how businesses from retail to healthcare leverage these innovations to drive efficiency and achieve competitive advantage. According to Singh, Onix has achieved remarkable results, boasting time-to-completion metrics that are 50% less and a third of the cost on average across key verticals such as telecommunications and financial services.
In this conversation from the Google Cloud Partner AI Series, Sanjay Singh, CEO of Onix, and Colleen Kapase, VP of Channel and Partner Programs at Google Cloud, join theCUBE’s John Furrier to explore how strategic IP and deep domain expertise are reshaping AI service delivery across verticals. Singh shares how Onix has embraced a “service as software” approach, enabling 2x faster results at half the cost for Fortune 500 clients in telecom, healthcare, retail and finance. Central to this is Wingspan, Onix’s IP-led platform that streamlines data discovery, auto...Read more
exploreKeep Exploring
What is the business model and approach of Onix?add
What role does Sanjay play in the transformation and integration of technology partnerships at Google Cloud, particularly regarding AI and service-driven approaches?add
What is the platform developed by Onix and how does it aim to address customer needs regarding technology and data management?add
What recent acquisition was made related to a customer engagement solution, and what capabilities does it provide?add
>> Welcome back, everyone, to theCUBE here at our NYSE studio. Of course, we have our Palo Alto CUBE Studios connecting Wall Street and Silicon Valley. I'm John Furrier, host of theCUBE. We're here for the Google Cloud AI Partner Series. We've got two great guests, Colleen Kapase, VP of Channel and Partner Programs for Google Cloud, and Sanjay Singh, the CEO of Onix. Thanks for coming on theCUBE, welcome to theCUBE. Thanks for coming on.>> Thank you for having us.
Sanjay Singh
>> Thank you.>> So Sanjay, your featured story here as VIP partner, AI leader. I put the VIP in there because we were just talking before we came on, you're doing tons of stuff. You're number one partner of the year in many categories, which means there's a lot of horizontal value you're creating. So first talk about what you guys do, and your relationship with Google and the partnership.
Sanjay Singh
>> So Onix, the way we have built Onix is in a very nontraditional manner. We built Onix as an IP-led services company, and not a traditional body-for-hire consulting company, which category now Gartner calls it as service as a software company. So we built that company grounds up using IP and put all of our intelligence into the platform to deliver outcomes for customers which are two times faster and half the cost. It's very experience-driven instead of the lengthy sales cycle and consulting which typically customers are used to.>> Yeah, project management.
Sanjay Singh
>> Project management.>> Slow rolling.
Sanjay Singh
>> Yeah. That's still important, but we have->> A slow part.
Sanjay Singh
>> Oh, yeah. So if you look at the plan, build, run, those three categories, our plan is done by very high-end consultants. Build is completely automated, about 80%. And run is automated as well. And for Google Cloud, we have been focusing on the Google Cloud stack. We are a Google Cloud first company. We have been like 16 times Partner of the Year. This year we have with the Data Analytics Partner of the Year in telecom verticals and healthcare verticals, in workspace collaborations, deep expertise serving some thousands plus customers. 20% plus of our customers are Fortune 500, and we deliver the most complex programs for Google and their customers jointly in a very integrated go-to-market model across the world.>> Colleen, they've got IP-led. He mentioned service as software, which I love by the way. I think that's something that really differentiates where we are with agents. How does that fit into Google Cloud? Because now there's a technology partnership, there's business partnerships. Obviously, we love the marketplace, that's great for buying stuff. But Google's got everything in between from marketplace to technology integration. Where does this fit?>> Sanjay is right in the middle of it. His timing could not have been better in terms of an investment and the focus that he has. So I've been at Google Cloud for about a year and a half, and really coming in to be part of a transformation. When we looked at AI changing the technology at Google, it also changed the type of partner that our field wanted to work with. In the past it may have been more of just a selling relationship. How do we get into the client? How do we plant the flag? But quickly that moves from a planting the flag conversation to how are we implementing the technology? How are we driving the outcomes? So Sanjay's investments in IP and services per vertical because our sales teams aren't just ubiquitous, they're very much vertical-oriented. So when he has that vertical expertise and airlines with the ability to implement and drive consumption, drive usage, most importantly drive happy customers, he's just really risen to the top, both from us from a global perspective and most importantly with our fields. That's been amazing.>> All right, Sanjay, what's the secret sauce to the IP?
Sanjay Singh
>> So we have developed a platform called Wingspan. So what this platform does is that... Let's take a step back. Let's see from a customer's lens. Customers get overwhelmed with technology and with solutions coming their way. What they want is really outcomes and something which seamlessly integrates into their business workflow. And they want something which they can see and touch in a year, no more multi-year programs anymore. Nobody has that kind of budget and patience anymore. So what Onix does is that we built a platform called Wingspan, which helps you do complete discovery and creating lineages and knowledge graphs of your data application and business user KPI, which is the holy grail of tech. So we know how the data moves, who uses it for what purposes. By combining A plus B plus C, you get a certain outcome and a KPI. For example, if you're a retailer, you combine data A plus process B to get customer 360. If you're healthcare, you do the same thing to get patient 360 and things like that. So we automated that completely with deep data engineering knowledge. Data engineering is what we bring to the table. Then on the AI side, we have been saying this for many, many years, which now the word is catching up is that there is no AI without right data and context. And with Onix's capability of having their own knowledge graph, if you have to automate your process, I know which data to train it on and I build guardrails so it won't hallucinate and it will give you ethical outputs, and as well as it's well-trained on things that you need to answer for. That's what we have solved for at scale.>> You bring up a good point there. You're grounded in data. It reminds me of the pandemic. When the pandemic hit, if you were in the cloud, you were on the right side of that. Because yeah, everyone was working at home, cloud scale and if you operated with the cloud, you were doing great. In AI, if you were grounded in data, if you did the data work, you then are on the right side of the growth right now. Not everyone can be, so take me through the two scenarios where you're working with customers where, "Hey, I've been a data hoarder, I've been playing with data, I got cybersecurity, I got all kinds of data." And then, the customer's like, "I've just got everything. I got databases." They might not be as fluent or prepared. How do you talk to those two customers? Because I think we see some people really have leaned into data platforms and data lakes, et cetera. Some haven't. They got siloed, stovepipes everywhere, just trying to figure it out. How do you solve those?
Sanjay Singh
>> That's a great question. So there are multiple approaches to it. One is that depending upon the kind of outcome the customer is looking for and at what velocity, one approach is that you modernize your legacy data estates into a modern data estates like Google, which is best in class in the industry. And then, you run a lot of analytics on top of it and then you run a lot of AI programs on top of it. The other is more of a hybrid approach, where you transform your as I call it customer-facing, customer engagement platforms first. It gives you immediate business value onto the modern stack, and then you run off the data to do AI and automation where the data is sitting, which also Google Cloud kind of supports. So we run both programs. We offer both solutions to customers. And as we say is that the AI should meet the customer in its journey where it is today. And when we implement AI, for example, if you are an ISV, I was with an ISV today, morning. And one of the biggest problem is sales and marketing. Everybody wants automation in sales and marketing to read out the Looker dashboard, to see what's happening, to pinpoint to the sales guys on the field that, "We see this deal is stuck at this level. This is the buyer. We saw that he's looking for these. This is a new solution. How do we automate all of these things?" And we kind of solve for these things at scale.>> That's the wingspan.
Sanjay Singh
>> That's the wingspan. The Wingspan is integrated platform. It's got everything in it. It's got ability to modernize data.>> Do customers deploy it or they subscribe to it, they consume it? How are they interfacing with it?
Sanjay Singh
>> Neither.>> Neither. What do they do?
Sanjay Singh
>> So we use this to deliver services to them.>> Oh, you use the tool.
Sanjay Singh
>> That's why it's a service as a software.>> So that's your secret weapon.>> Yes.
Sanjay Singh
>> Yes. So we use that and the customers don't need to increase the technical debt. Once it's done, they don't need it anymore.
Sanjay Singh
>> I would say this is what keeps Onix as sort of Partner of the Year from a data perspective; is that IP and those tools that they've created, whether it's migrating the data, which you also have a tool for or understanding how to best use the tool, how to use your data, that's what's making them->> A data mode.>> Yeah.
Sanjay Singh
>> Yeah.>> And an IP mode.>> Yeah.>> So this speaks to the Google integration. So one of the things that came out of Google Next, Colleen, and I'd love to get both of your reaction, is the technical integrations with partners has been a big part of the value-add. And this is all is value-add pieces, but the ones that are winning have an end-to-end integration. They're up and down with Google. And you mentioned discovery with Wingspan. One of the things we're seeing with these agents is evaluation. Evaluating the agents is a big topic.>> Yeah.>> And there's some math behind that, so there's now science into evaluation. It gets to your point about how you want to engage these environments with tech.
Sanjay Singh
>> That's true.>> Talk about the technology integration, because now we're talking about all new ways to create value.>> So not only is Onix a strong data partner, but to be a strong AI partner you really have to have that depth and base in data. And so, they've got that but the next step is that agent discussion. If you want to drive a good data story, you need to understand the processes that a specific customer, where their data sits, how are they going to have access to it to then drive that agent experience. That I think is where we then bring in from BigQuery to our agent space discussion. And that's something that you guys have been doing amazing with, and it's that agent-to-agent and that agent orchestration that you're driving that's really been bringing value to customers.>> Agents need that evaluation.
Sanjay Singh
>> That's true. So we have been involved with the agent space launch. We kind of worked with the product engineering team of Google to launch that platform. And one of the things that we also do is that we are changing the game in the way how customers consume AI. And I will double-click on it. So what we have done is that we have created an IP called Onix Canopy, which is a collection of ready-to-go AI agents which customers can drop ship code to. So they just download the code, they plug in their data, and it's operational. And we come in and fine-tune the overall experience of the customers to give them the right... And what we did is that we hired a lot of chief data officers and CIOs and CTOs of Fortune 10 50 companies who understand what the typical pain points are in the process of business transformation lens. And we pre-build that up front so that the customer's time to value is very, very quick.>> So you bet the business on service of software.
Sanjay Singh
>> Yes.>> That's what you're doing.
Sanjay Singh
>> Yes.>> You're basically abstracting away the picks and shovels.
Sanjay Singh
>> That's correct.>> On behalf of the customer.
Sanjay Singh
>> That's correct. And we are winning. Customers see different shared value.>> So speed and cost. Can you share the numbers again in terms of the averages you're seeing, time to completion for projects and cost?
Sanjay Singh
>> 50% less and one-third the cost typically.>> As average.
Sanjay Singh
>> Average.>> There's probably more cases across all verticals.
Sanjay Singh
>> Across all verticals.>> Regulated industries versus say nonregulated, unstructured data, structured data, does it matter?
Sanjay Singh
>> We typically work in four primary industries where we have very strong domain expertise.>> Which ones are those?
Sanjay Singh
>> Telecomm/media/gaming, healthcare/life sciences, retail/CPG, banking/financial institutions.>> Those are the hot ones.
Sanjay Singh
>> Yeah, those are the ones where we are.>> No life science?
Sanjay Singh
>> HCLS, healthcare, life sciences.>> Okay, okay.
Sanjay Singh
>> More payer, provider, and pharmacist.>> All right. It's that acronym.
Sanjay Singh
>> No, no, no problem.>> What's the acronym again?
Sanjay Singh
>> HCLS: Healthcare, life sciences.
Sanjay Singh
>> Life Sciences.>> Maybe I'll just call it life sciences. It's too hard to remember. So if you look at where the GPUs are going, look at NVIDIA, AI factories and physical AI, the convergence of digital and physical are coming together. That's why I love the retail market and I love life sciences right now, because financial service has always been there.
Sanjay Singh
>> That's true.>> So they're not really jumping off the page, they're just continuing to spend cash because they have the use cases.
Sanjay Singh
>> Now the other thing that we also did is that we recently bought a company called UJET's Professional Services Division, which basically is the only Google Cloud native CES solution customer engagement suite. What basically that means in simple English is that basically we now have the ability to deploy customer service agent automation at the front-line worker level. So it empowers the front-line workers to give better customer service, to do cross-sell, to reduce churn reduction rates, to increase CSAT, and to reduce call volumes coming into a lot of those high engagement ratio customers.>> Colleen, you have a real player here on the AI and data side, which you don't see that very often. I have to ask you on the LLM side, you've got Vertex, you've got Gemini, you've got all this goodness coming out of Google. And they're not stopping. At Google Next they simplified everything.
Sanjay Singh
>> That's true.>> They got native AI inside the product. Some of the stuff for BigQuery was amazing. So they're doing AI for their product to enable you to do better.
Sanjay Singh
>> Yes.>> What are you doing with that new stuff? Can you share? Because I would envision these verticals have their own models too.
Sanjay Singh
>> So what we have done is that there are three concepts. One is your large language model concept, then there's a small language model concept, and there's a domain-specific language model concept. Just Got 2 yesterday published a big report on DSLM, where Onix was featured as one of the partners doing domain-specific large language model. So you take a large language model and then you fine-tune it then down to your particular vertical, and then you train that particular model along with the data sets that I have context and aware of. So your entire technology becomes seamless and it becomes very efficient. Instead of training the what's trained on the internet, you kind train what is trained on your taxonomy, on your description of your assets. For example, if you're a retailer, warehouse means very different than a warehouse what it would mean in a banking industry.>> Yeah.
Sanjay Singh
>> So the ability for the AI to decipher between the two contexts is extremely critical for the AI to function.>> And the reasoning is only getting better with the context windows.
Sanjay Singh
>> Exactly. Exactly.>> Compare the scope of the order of magnitude difference between pre-AI days. Just roll back say a decade or 15 years, to pull that off to fine-tune, possible. The alternative, how would you describe that? Go back 10 years trying to do what you just said, get fine-tuned models. What kind of, be it on taxonomy build, build an ontology, write some code.
Sanjay Singh
>> Years.
Sanjay Singh
>> It'd be years and I would need a building full of servers here.>> Yeah. Yeah. Not attainable.
Sanjay Singh
>> Yeah, not attainable. Impossible.>> And now it's part of your practice.
Sanjay Singh
>> It's part of our practice, and that's what Google Platform enables. So it makes them so easy to use.>> And what was the secret on the Google side?
Sanjay Singh
>> Well, one of the differentiations that we have is yes, we have Gemini, which is by far one of the best models out there in the world. But we also have a model garden where we have other models from other providers. We have Anthropic that sits on top of the Google. We have Gemini, which is an open source, our own open source model. We have this model garden concept, and that's part of the ethos that makes Google special. It's our own offerings, but if there's other partners too, let's bring them along to the party.>> So pick your model to use.>> It gives you choices.
Sanjay Singh
>> That's true.>> So there's a use-case specific, so flexibility.
Sanjay Singh
>> And that is a differentiation, very much so to Google with our own models right next to our partner models too.>> It doesn't hurt to have a bunch of TPUs to go with it.
Sanjay Singh
>> Well, they like those.>> We're starting to see you guys really crush it on the AI infrastructure.>> They're doing fabulous.>> What's the impact on the AI infrastructure? Again, you have now supercomputing available to us average developers and leaders. That wasn't really available to get HPC. Like you said, the server, you weren't kidding. We would need a bank of servers, data centers. Now it's all cloud-based.
Sanjay Singh
>> And the impact is fairly straightforward from a speed of business transformation and innovation. What is now available for startups? It's available for ISP partners, is available of large customers to create new solutions which would take forever to even think about and provision from a CapEx perspective.>> What are your most popular engagements with customers, with your services software and with your IP-led Wingspan strategy? What's the-
Sanjay Singh
>> Four of them, four of them. One is complete, because data transformation and AI is a board-level topic. Every large and small corporation is asking the questions to the executives, "How can I be more data and AI-driven now?" Data was like a few years back. Now how can I have every business->> Before it's analytics and dashboards, now it's like real competitive advantage.
Sanjay Singh
>> Yeah. So they bring us into tell us that, "Can you help us to be an AI-first company?" And then, we go through the process value chain, find out whether it's an efficiency player or an innovation player through our engagement of discovery and solutioning along with our domain experts that we have in each of these verticals and use cases. And plus, we're a ready-to-go repository. So our sales cycle is very, very short because we->> But data is number one.
Sanjay Singh
>> Yeah. Data is number one. Second is complete modernization of the stack, AI deployment, running. And we also have something called an application modernization practice. Basically what we are saying to the world now is that if you look at a typical application that you're using on a laptop right now, you have any UI?>> Yeah.
Sanjay Singh
>> Then you have some logic in your database. You don't need the UI anymore, because you can just talk to the AI. And we are saying that AI is the new app without the UI.>> Exactly. Yeah.
Sanjay Singh
>> So we are->> It's generative too.
Sanjay Singh
>> Generative too. And then, it gives you flexibility. You're no longer hard-coded in your processes, so you can fine-tune your business process.>> All right. So that's number two. What's number three?
Sanjay Singh
>> Number three is managing all of this in an ongoing manner like AI ops, FinOps, making sure that the cost implication is taken care of. And the fourth area is all around bringing geospatial engineering and collaboration together into this. We'll be also a very big part of Google on geospatial engineering and the workspace collaboration. So we kind of bring all of these things together.>> That's really security built in too. It's a big part of all those four pillars. Colleen, what's next with these guys? They're on a fast-track. Agent space is probably going to be hot for them with Wingspan.>> Yeah.>> What's next on the relationship with these guys?>> The agentic experience is absolutely where we're at and where we're leaning into. I would say no, it's coming next. As you see one customer building out agent after agent, it's going to be agent orchestration. How do I take my hundreds of agents? When do I end of life an agent? How does another agent speak to an agent? So I think we're going to be moving into agent orchestration, agent federation, agent governance here really fast. And you guys are well-positioned to build out that capability.
Sanjay Singh
>> And what we see is that eventually our vision for the customers and for Google is that the customers of Google and ours will live on a single screen, which will be personalized to you and you'll have all AI agents doing the work personalized for you. And you'll be orchestrating it in using a drag-drop model.>> Yeah. We're going to have an era where agents will have SLAs, there'll be an HR department for agents.
Sanjay Singh
>> Of course.>> Jensen Wong's comment at CES earlier in the year was a lot of people were blown back. But if you look at where the agent conversations are, evaluations of technical conversation in all the top AI areas, evaluation of how they manage agents, agents on agents. But they have to perform tasks like a student or an HR would manage an employee.
Sanjay Singh
>> That's true.>> And if they don't complete the task, task completion.
Sanjay Singh
>> Yeah.>> But there is kind of a management layer for agents.
Sanjay Singh
>> Yes.>> That's where we're going next.>> All right. Thank you so much for coming on. I appreciate you being part of the AI Partner Series.
Sanjay Singh
>> Thank you.>> Congratulations. Love the vision, love the mission.
Sanjay Singh
>> Thank you.>> It truly is a service as technology, and at the end of the day it's service business and software. Colleen, thank you for coming on. I appreciate it too.
Sanjay Singh
>> Thank you for having us.
Sanjay Singh
>> Thank you for inviting us.>> And a joy to be here with amazing partner.
Sanjay Singh
>> Thank you, Colleen.>> Okay. I'm John Furrier with theCUBE. We are here in our New York City and New York Stock Exchange studio for the AI Series Leader Series. Thanks for watching.
