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Overview
đź“– AWS re:Invent 2025 - Autonomous Enterprise: Redefining the Future of Business with AI (BIZ101)
In this video, Rajiv and Todd Carey from AWS discuss Digitate's ignEO platform for autonomous enterprise operations. Three customer stories illustrate the solution: Tapestry retail processing over a million orders during Black Friday, a medtech company reducing demand planning from weeks to hours for prosthetic limb deliveries, and a CPG company managing direct store delivery for hundreds of millions in orders. The ignEO platform combines unified observability, AI-driven insights, and closed-loop automation through five agentic agents to eliminate business disruptions caused by IT issues. Currently managing 34.2 million workloads globally with 87% noise reduction and 300 million+ annual automations, Digitate announced a partnership with BMC Control-M on AWS Marketplace.
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Main Part
Real-World Business Challenges: Three Stories of Technology-Driven Disruption
Thank you. Good afternoon everyone. In the next 20 minutes, we are going to talk about how to change the future of business through autonomous enterprise operations. The way we have structured this discussion is that Todd will come and talk about what we are doing together as a partnership with AWS. Before we start the presentation, I want to thank one of my customers here from Kenview. He is a vice president from Kenview and a user of Digitate. Thank you very much for being our customer. Praveen has been a great help, and anyone who wants to discuss with him after the event would be happy to spend some time with you. This morning, we also announced a partnership with Control-M, which is the first inaugural launch along with AWS on the marketplace as a multi-product SaaS that I launched today. Please go and check it out.
Coming to this presentation, we will talk about three stories that will give you a sense of how customers are using our solutions. We will tell you what the product is about, then solve the story, and then hand it over to Todd. The first example is a premium retail company called Tapestry, known for their bags which some of you might have seen or used. They had an issue where some orders were not reaching on time, not being processed, not being fulfilled, and not being delivered on time. This resulted in a loss of revenue and a loss of customer experience.
Another example we will talk about is a company in the medtech space. They manufacture prosthetic limbs, and if those limbs and the committed date to the surgery room in the hospital are not confirmed, the surgery may not happen and the patient may not get their limbs. The third example is a scenario in Bangalore where a cricket match, a T20 cricket match, is going on. Many retailers nearby are stocking up because they expect significant sales. However, the stock is not fulfilled because there is a glitch, and the intended CPG company is not able to fulfill the orders due to certain order processing issues.
In all three examples, there is something common as a business problem. Somewhere in the IT layer, something did not perform the way it was designed to perform. That is the challenge we see across the industry. When the business calls and says something did not work or my order did not get processed, there is an IT problem that has happened. The majority of business problems today are actually technology issues, and that is what we are trying to solve and make autonomous in the industry.
If you look at the example of Tapestry, which I mentioned, their challenge was that they have 37 different storefronts operating globally. Orders come through those storefronts, they have an order management system, they have a fulfillment process, they have financial transactions to close, and then shipping happens. In the whole journey, there are different hops where data flows through different systems as a technology layer and within some of the applications themselves, for example, SAP systems and other systems.
In that journey of data flow, we have deployed unified observability to ensure that at every hop, the business knows exactly how the order is processing. If something goes wrong at the technology layer, the technology team also knows which component of technology has failed or not performed to expectations, and that is why the problem has been created. We have data from last week itself because last week was a very heightened activity period due to Black Friday sales happening everywhere in the world. In the last one week, they processed more than a million orders. If Digitate was not there, almost worth three million dollars of their orders either would not have been processed, they might have been delayed, or they might have been canceled by consumers because they might not have made the promised date.
That is the value we are bringing in this situation. We are bringing unified observability across the process flow from order to cash and underlying technology vertically from the technology perspective to ensure operational autonomy is involved and delivered to them every day, every time an order comes through. The second example, which I talked about, is a company whose challenge was that they grew through acquisition several years ago. It is a long-standing brand. They build, as I said, prosthetic limbs. They are across the supply chain and demand planning specifically.
They have to confirm a promised date for a specific limb to a patient and to the surgeon. If you look at that area, it's very humble to speak about because you need four things: a doctor, a hospital, a patient, and a limb. All three are available, but the limb is not available, so the promised date is not there. That's the challenge this customer faced where the order flow of data was happening through 70 different interfaces because they had grown through acquisition with different applications and different kinds—SaaS, on-premise, and antiquated legacy applications all running through those systems.
For them, the demand planning process has been reduced from weeks to now hours, and they're able to promise the date to the hospitals so that the surgery can happen. That's a big help not only to my customer here, but also to the end user because that's very important for their life.
The third example I talked about is where direct store delivery is a very critical process for some CPG companies. In that process, typically route sales would go take the orders, see the stock on the shelf, understand what is required and how it's selling, and punch in some orders there. That order flows through different systems, comes to the CPG company and their manufacturing units, and ships through the truck to directly stock on the shelf. That is a very high frequency process where if you miss an order or it's not delivered—as simple as a manifest has not been generated—your truck may not fill the full orders and move. You have lost two parts in that process.
First of all, the shelf is empty for the retailer. The retailer has not only lost the revenue, but the CPG company has associated revenue loss as well, and you also lose the brand and market share in a very competitive market. On that journey, on the business value, what we're monitoring on the whole journey from the order taker to the manufacturing plant to the shipping fulfillment and the restocking, we have mapped on IgEO, which is the platform from the state to ensure on a weekly basis we process a couple of hundred million dollars of orders on this platform to ensure it moves autonomously in the journey.
These three stories are from different kinds, and what matters as a business underneath all this is that the real solution is the technology, which is the problem. Autonomous is the new operating model, and I'm sure some of you who come from the IT background want to eliminate business disruption which happens because of IT.
Igneo Platform: Achieving Operational Autonomy Through Observability, AI-Driven Insights, and Closed-Loop Automation
A lot of business folks call you and say, "Look, I gave you all the tools. Why did this happen again?" The second part is, if we can't fix those problems proactively, I should know first as an IT person before the business partner knows, and I can tell them in advance that I know about it and we are fixing it. If you can't do that, the last and least part you can do is, if I know somebody called me and a problem has been there, can I recover as fast as possible so that the problem feels like a blip?
To achieve these three elements in order of priority, we have built a platform called IgEO which has three broad things. One is the observability, which I talked about from the horizontal observability to understand the layer of data processing which happens through different applications, and the underlying vertical stack observability to understand how the problem occurs and proactively create visibility and avoid those problems from happening in the first place.
We also hook up on the second part, which is the AI-driven insight. We hook up with a lot of telemetry data, log data, and other elements which come from the environment through your monitoring tools to understand what can happen and predict what's going to fail. For example, this application, the way the usage is increasing, six weeks from now the performance of this service is going to degrade. It gives you some latitude to understand what's going on. A batch job cycle can tell you at three a.m. in the morning and say, "Look, by six a.m. your sales reconciliation report is not going to come. You will miss that KPI." That's what this AI-driven insight gives you—the time so that you can recover from those situations before the problem has already hit you.
The third part is that problems do occur, we recognize them, and that's where the closed-loop automation comes in. When a problem has resurfaced, it understands the context and the whole hierarchical model of your enterprise as an IT blueprint, and it can recover and isolate the problem at machine speed. Typically today, a lot of enterprise customers put teams which are siloed in nature, and they come together. Typically, first time they will say the network guys are at fault.
Eventually they come down to some database guy and say, "Look, the long running query is the problem." They fix it, and it takes maybe one to two to four hours. That's where it runs at machine speed to isolate the problem. We have built almost 10,000 automated actions to take action and fix this problem within minutes. So a situation which takes maybe an hour, two hours, or five hours to fix can be solved in a couple of minutes, sometimes three minutes, sometimes ten minutes.
All three elements of the capability that we have built on this platform, I applied to your cloud operations, your typical data center, if you have a data center in your environment, custom applications, SAP systems, batch jobs, and devices like laptops and desktops. That pretty much covers eighty to ninety percent of operations, and that's what our vision has been—to go behind this and make all the journey autonomous so that the business can perform the way it's supposed to perform.
To deliver all this, we have these five agents. The whole architecture is agentic. If you look at some of the personas which we deal with in the enterprise today—the SREs persona, the CXO personas, a lot of cloud operations which goes on, a lot of IT operations teams handle—their objectives are very clear: move as fast as you could, avoid and eliminate incidents. These five agents work on a very simple principle where they can work on their own.
These agents are made through six sub-agents. All agents go through a perception agent, a reasoning agent, an action agent, and a learning agent. We also have built a control agent and an LLM-based model agent so that it can augment some of the teams because maybe Igneo does not have full knowledge of everything that goes into the enterprise. What we have built here from the agentic platform perspective is not only a deterministic model where it can go and fix certain problems from the logical reasoning perspective, but it can also take something analytical through your LLMs. We use one of the other platforms from an LLM today, and we can bring it together from the logical and analogical model to fix the problems that go on in your enterprise.
Just to give you some data points today, we are very focused on Global 2000 customers. We believe the enterprise is the segment where we thrive well. The impact and value of this platform is much higher. The value they derive is much bigger. We are almost managing 34.2 million workloads globally in twenty different regions. We process almost 1.2 billion events on this platform with almost eighty-seven percent noise reduction. Just imagine the help it gives to the command center teams everywhere. We almost have three hundred million plus automations which are working today on the platform every year, and of course we are compliant to certain standards, global standards which any enterprise needs today—SOC 2 Type 2 and others.
On that journey, AWS has been a great partner. We have launched today morning with BMC Control-M, but I'll ask Todd to talk about some of the partnerships we have gone through in the last several years.
AWS Partnership and Marketplace Integration: Delivering Next-Generation Solutions at Scale
Thanks, Rajiv. Great stuff. Good afternoon. My name is Todd Carey. I lead our Global Solution Partners sales teams worldwide. That's two main areas: looking at our biggest partners and strategic partners as customers, also as investors and sellers, and how we go to market together. That really is responding to a lot of the market signals, the things that we see, things that customers demand. It's amazing to hook up with partners like Digitate where they're looking at things, they're building things, but most importantly they're delivering things.
When we feel like delivering things like outcomes, when we start talking about on-time deliveries, weeks to hours, full trucks—these are use cases that end customers really want to hear about, and in fact just as important as our field sales teams want to hear about. So when you're an end customer or you're an AWS or you're another partner, what good looks like definitely is Digitate, and these are the partners that we really lean in with and support from a go-to-market standpoint, from a solution building standpoint. We figure out how we can scale together. It's a great success story.
Digitate Igneo leveraging the marketplace, BMC leveraging AWS Marketplace—this is really the next generation of sales platform. If anyone got to hear the keynote on Marketplace and all of the new announcements that we're making, you know, from a next generation sales and a next generation experience and engagement, you take Igneo, you take Digitate and AWS, and it's an extremely powerful combination of delivering value on time and most importantly delivering outcomes.
We look at the type of partner that we want to look for, we want to invest in, and Digitate certainly represents that. We're always exploring how we unlock value, how we accelerate time to value, and we accelerate time to market as a complete package of partnership and relationship. Super excited to be supportive of Digitate and all that they're doing in the market. Just as a quick say hi and a huge endorsement of all the things that we're doing with Digitate. So very excited, and I think we're going to open up to some Q&A.
Sure, if you have a question we can answer. There are some questions. Happy to field all of those. Was it that thorough? Rajiv, good stuff. Able to deliver that with no questions. Well, I thank you very much. I think that's a wrap. Thank you, thank you. All right, good job. Thank you.
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