Ali Hussain of Tabs discusses the company’s approach to rethinking finance with agent-driven workflows and a commercial context graph that powers billing, collections and revenue recognition. The episode explores artificial intelligence agents and how a durable context graph supports finance automation. The conversation with John Furrier of theCUBE and framed by theCUBE Research examines product strategy, mid-market adoption patterns, practical agent deployments and how proprietary context enables agents to act with business-specific knowledge across customer relationship management and enterprise resource planning and contract data.
Hussain notes that agents accelerate contract-to-cash processes and reduce days sales outstanding by roughly 20 days while improving cash forecasting through aggregated customer behavior data. They highlight the mid-market as a high-opportunity segment for AI finance adoption. theCUBE analysts point to the growing role of frontier model partnerships such as Anthropic and OpenAI and emphasize the need for secure durable context graphs to capture enterprise value.
Keywords: finance automation, commercial context graph, billing, collections, revenue recognition, contract-to-cash, cash forecasting, mid-market adoption, AI agents, customer relationship management CRM, enterprise resource planning ERP, frontier model partnerships, Anthropic, OpenAI
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Ali Hussain, Tabs
Ali Hussain of Tabs discusses the company’s approach to rethinking finance with agent-driven workflows and a commercial context graph that powers billing, collections and revenue recognition. The episode explores artificial intelligence agents and how a durable context graph supports finance automation. The conversation with John Furrier of theCUBE and framed by theCUBE Research examines product strategy, mid-market adoption patterns, practical agent deployments and how proprietary context enables agents to act with business-specific knowledge across customer relationship management and enterprise resource planning and contract data.
Hussain notes that agents accelerate contract-to-cash processes and reduce days sales outstanding by roughly 20 days while improving cash forecasting through aggregated customer behavior data. They highlight the mid-market as a high-opportunity segment for AI finance adoption. theCUBE analysts point to the growing role of frontier model partnerships such as Anthropic and OpenAI and emphasize the need for secure durable context graphs to capture enterprise value.
Keywords: finance automation, commercial context graph, billing, collections, revenue recognition, contract-to-cash, cash forecasting, mid-market adoption, AI agents, customer relationship management CRM, enterprise resource planning ERP, frontier model partnerships, Anthropic, OpenAI
In this interview from the Mixture of Experts AI Agent Conference, Ali Hussain, chief executive officer and founder of Tabs, joins theCUBE's John Furrier to discuss how AI agents are eliminating the manual labor bottlenecks that have long plagued mid-market finance teams. Hussain explains how Tabs built a proprietary commercial context graph — mapping contracts, usage and payment behavior — that gives agents the deep understanding needed to automate billing, collections and revenue recognition in seconds rather than weeks. He highlights why the mid-market, co...Read more
exploreKeep Exploring
Why was Tabs founded, and what part of finance does it aim to solve using AI agents?add
What is your company's approach to applying AI in the enterprise — specifically, what problem are you solving, why focus on building a context graph from commercial data (contracts, usage, etc.), and how does that impact the development and usefulness of AI agents?add
How do you define the mid-market—what revenue/size range and key characteristics describe companies in that segment?add
How does your contract-to-cash product address cash-flow challenges—specifically late customer payments and improving cash forecasting?add
How are your partnerships with frontier AI model vendors (e.g., OpenAI, Anthropic) playing out for your company, and what is your execution playbook for leveraging them?add
>> Welcome back. I'm John Furrier, host of theCUBE, here at theCUBE's NYSE studio. Of course we have our Palo Alto studio connecting Silicon Valley and Wall Street. This is the Mixture of Experts series, where we're also doing a preview of the Agent Conference happening in May, Simon Chan's community meetups turning into a full-blown industry conference. We have one of the distinguished speakers and top 50 in agentic worldbuilders here in theCUBE, Ali Hussain, CEO founder of Tabs, a very successful company, growing very fast. Ali, thanks for coming into theCUBE, and hey, congratulations on being in the top 50 on the Agent Conference leaderboard. Explain.
Ali Hussain
>> Yeah, no. John, first of all, thanks for having me. I think Simon's put together an amazing conference. I had the pleasure to speak last year, and I can't believe how big of an event it's become for all the builders.
John Furrier
>> You guys had a top-50 list? What was that?
Ali Hussain
>> Yeah. Their conference basically went out and looked at who are the 50 companies that are really pushing agentia forward, and so we were honored to be a part of the 50, and one of the very small handful in the finance stack.
John Furrier
>> What I like about what Simon was doing in that community. First of all, it's a very elite community to build. There's a lot of founders and entrepreneurs there. But as the year went on last year, it grew into builders, founders, operators and investors coming together, and I think the May event was looking like it's going to be pretty big, but that's really where the market's going. You start to see the confluence of all three of those tribes coming together. Certainly geeking out on agentic is certainly fine because there's some great value there, but you gotta operate these things. And also there's a lot of money coming in.
Ali Hussain
>> A hundred percent, and I think they nailed it because at the time, not many people were using the A word, agent. They were still talking about AI and LLMs and all of that, but this really feels like the year.
John Furrier
>> It's funny. Trey from Tennr, the CEO co-founder, was on in here earlier today. He said he wouldn't let anyone say generative AI or use the agent word in his company. And then he's like, "Actually, it's actually working," so now they're totally on it. But it is kinda mainstream. We're seeing use cases earlier in the year at CES and then specifically NRF, the retail side, you're starting to see companies that were using agents are getting traction.
Ali Hussain
>> Yep.
John Furrier
>> So take us through what you're seeing on your side, because you guys have traction in your business.
Ali Hussain
>> Yeah.
John Furrier
>> Take us through how you guys are implementing it and what's the impact.
Ali Hussain
>> A hundred percent. I mean, Trey and Tennr's a great example. They're actually a customer of Tabs. And as you know, they've grown substantially and they need to scale revenue. They're putting in a lot of amazing infrastructure for R&D, but they don't want to make their finance team look like the finance team of the past. They're a relatively lean G&A function. I think they now hired their first finance person. And so a lot of that has been their forward-looking view of using agents like the ones we build to handle a lot of their finance functions.
John Furrier
>> Talk about that impact. I want to dig into it before we get into some of the more specific use cases. You've hit on this trend with Tabs where you don't need to overbuild. This is where productivity is shining with AI and agents right now. Talk about what you guys do and what you do in terms of closing the gap on scaling up the efficiencies. Talk about Tabs.
Ali Hussain
>> Yeah. So Tabs was founded out of the belief that finance is going to change a lot in the world of AI, and we really focused on where most of the human capital in finance sits. So when you think of most businesses, it's not the entire stack. It's mostly sitting in accounting. And in accounting, most humans are doing billing, collections, revrec. And so for us, we were always fixated on this is a really hard problem. Before AI, it was just impossible without a huge team of people to handle. And so we've been hyperfixated on let's chase where most of the human inefficiency is in finance and try to incorporate agents. And so our agents handle all of those workflows in revenue, billing, collections, revrec.
John Furrier
>> What was the first area you focused in on, dialed in on? What was the initial focus, and how did you guys get through that?
Ali Hussain
>> Yeah, it's really interesting. I mean, in 2023, people are talking AI and they were talking maybe like downstream use cases. For us, it was a data question. And so our original wedge was how do we build the most compelling context graph for an enterprise, which we think is around commercial data, contracts, usage, et cetera. And if we can nail that using AI, it makes everything easier. It gives you a much broader surface area to build a ton of technology, but ultimately that's the context that agents need to understand a business. And if you nail that, the agent part's actually less difficult, but if the agent doesn't have the knowledge that a human would have and it's constrained to data that's sitting in a system of record, it's therefore kind of a headless agent, and so that's where we've been hyperfocused.
John Furrier
>> I mean, that's financial infrastructure.
Ali Hussain
>> Yes.
John Furrier
>> Basically, data infrastructure.
Ali Hussain
>> A hundred percent.
John Furrier
>> And having that graph, it's like roads.
Ali Hussain
>> Yes.
John Furrier
>> You can map out the context and let the agents make decisions. Is that right?
Ali Hussain
>> Exactly. Exactly. You can build the roads faster, but also you can give an agent so much proprietary context. We would take a human days, weeks, teams to understand what an agent can now do in seconds.
John Furrier
>> It's funny. In a lot of startups and a lot of big companies, there's two areas I want to get your thoughts on. They tend to look at the finance either as we start, "Oh, we'll get to it later, build the product." Bigger companies throw people at the problem. How did you guys get through those dynamics? Because sometimes it's just focus on the startup side, but then the big companies are like, "This is the way we do. We handle accounts receivable this way." How did you drive through that? What was the key strategy?
Ali Hussain
>> A little bit of trial and error, but we actually found that the mid-market right now is the best place to deploy AI. I think you're right. The startups and the smaller companies, they either don't have the scale or the pain in finance. They have other challenges, and enterprise is still a little slow-moving or in test mode. But I would tell you, John, the mid-market, last 12 months, even H2 of last year, incredible. They are leaning into AI. They're a little bit more agile and they just can't solve problems with just people anymore, but they have real problems.
John Furrier
>> Define the mid-market for folks that don't know that segment. What's the size, scope, revenue? What's it look like?
Ali Hussain
>> Yeah. I mean, look, it can vary a little bit by sector, but I typically say 10 million is like your entry point. Some would go a little bit higher, but 10 million, you're starting to feel some financial pain, or the risk is getting there.
John Furrier
>> The processes aren't there, things are breaking.
Ali Hussain
>> And then it goes all the way to a billion. Ten million's to start to play in the mid-market. Five hundred to a billion is like, all right, I'm very much in the mid-market.
John Furrier
>> So either rapid growth, or they have processes that weren't hardened for the scale or process flows.
Ali Hussain
>> Spot on.
John Furrier
>> We've seen those.
Ali Hussain
>> Yeah. Growth is one. Two is just they're seeing a lot of lack of talent. There's just not that many accountants anymore. The CPA is incredibly hard to get. And if you get one, I can tell you, you don't want to be doing billing all day. And then the third is there's a lot of change happening in pricing, complexity. A lot of these companies are now under audit pressure by their new PE sponsor or their new acquirer, and they just don't have the time to wait nine months to get their legacy system to work in that way or go through a bunch of professional service work.
John Furrier
>> All right. I gotta ask, because every company wants more cash.
Ali Hussain
>> Yeah.
John Furrier
>> Cash flow, cash is king, as they say. How does that play into your product, contract to cash?
Ali Hussain
>> Yeah.
John Furrier
>> Take me through that. What would be the use case?
Ali Hussain
>> Yeah, so there's two big challenges with cash. One is it just always comes in late. So most B2B works on a 30-day cycle or Net 30 terms. Prior to coming onto Tabs, they're typically in the high 50s or 60s. Agents and AI can help accelerate that. I may never nail 30, but we can bring it down about 20 days by just optimizing, using AI, kind of the process flow. So that's number one. More interesting, though, is the most common metric missed at the board level is cash forecasting. Most even finance teams don't know, across all their customers, when they're going to pay, and so they use human assumption and Excel to predict. An agent has unlimited data. In a system like Tabs, they know the prior behavior of the customer. They can compute so much faster. But then when you have as much data as a technology vendor like us, we can predict across many merchants the average days to pay, and so we get a lot better at prediction of cash as well.
John Furrier
>> Yeah. And that's huge because that's forecasting, that's hiring decisions. That's the key there.
Ali Hussain
>> Exactly.
John Furrier
>> Talk about the agent side, because a lot of people are talking about agents, starting to see Linux Foundation pick up both MCP and A2A, a lot of technical standards, a lot of builders. That's evolving. There's some hardening areas. We talk to a lot of folks that lock in on workflows first, and so you're starting to see real execution in agents. How are you looking at that at Tabs, and how does that translate into value?
Ali Hussain
>> Yeah. So we looking at the vector of what's technologically possible, but where's the customer most comfortable to use it. And so as you think about the domains we play in, there's billing, revrec, audit. When you think about that framework, we think the lower-labor, kind of annoying parts of the day-to-day, the billing side where you would have these huge accounts receivable teams, is where we think 2026 is very ripe for change. But we're not that far off from the audit and revrec side either, but I think there's a little bit of let's get everyone comfortable. Let's see where our friends at Anthropic and others take us as partners, and the rest phases over the next.
John Furrier
>> Talk about the partnerships with the frontier models.
Ali Hussain
>> Yeah.
John Furrier
>> Obviously huge value coming out of them. How's that playing out for you? What's that look like? What's been the execution playbook?
Ali Hussain
>> Yeah. I think, one, I mean, look, it's great. There's more optionality today. I mean, we get to work with vendors like OpenAI, get to use different tools like Cursor. Obviously, we now share investors with Lightspeed with Anthropic, so we're really at the forefront of using a lot of the different products coming out of Claude. And so I think there's a lot of optionality, but I would say the last four to five months have just gotten people with the latest models, particularly out of Claude, are just making us all be more efficient, and it's not just engineering anymore. I as a CEO, every interview I do. I just had an interview with the head of HR. I didn't care downstream. I wanted to know how they were going to bring AI to make their team or their playbook a lot more efficient.
John Furrier
>> So when you look at the frontier models, there's obviously the product advantage that you guys get on your side, but also the workers, the main experts who are using the products.
Ali Hussain
>> Yes.
John Furrier
>> So there's a double win there, because these frontier models are becoming the user interface to the systems behind it. So if you have the graph, the context graph ... what'd you call it?
Ali Hussain
>> The commercial graph, yeah.
John Furrier
>> Commercial graph. I mean, that's essentially plumbing, if you will, or roads. The added value for that democratization skill.
Ali Hussain
>> Yes.
John Furrier
>> If I'm a finance person using the system, I could see benefits coming down the road where it's like, "I'm going to use a frontier model with Tabs."
Ali Hussain
>> Yes. You're spot on. That is exactly what happened.
John Furrier
>> Take me through that.
Ali Hussain
>> So today we are seeing finance professionals more forward-looking on using the frontier models themselves, one because I think they just have such G&A pressure, but also CFOs are the steward for the rest of the org. If they don't walk the walk, it's going to be hard to get the sales team and the marketing team and the product team to do the same. On top of that, they are so forward and I would say just interested in a way that we haven't seen in technology before. That said, you bring up a good point. They need plumbing. They're not going to get a lot of support right now necessarily from their engineering teams, who are busy doing other things, and so they've leant on us to give them the context graph, but also in a secure environment, run a lot of the workflows between different systems, email, CRM, ERP. But then we can help MCP that data in a clean and simple way so that I'm not here to constrain what reporting you want to do, how you want to break that data up by product, et cetera. And so I think systems like us are democratizing and giving access to the data they need. They can do whatever they want on top of that.
John Furrier
>> Ali, earlier in the week I interviewed the co-founder and chief product officer of Canva, Cameron Adams. He's a great guy.
Ali Hussain
>> Yeah.
John Furrier
>> He had a comment. I want to get your reaction. He goes, "Well, the big thing for Canva." It was a couple M&A deals, because they're doing really, really well. He says, "The big thing for us at Canva," I'm paraphrasing. He said, "We went from being a customer to the frontier models to having our own model and partnering with it. We went from being a customer to a partner." That was interesting. He expanded on that by saying, "Yeah, we definitely leveraged it, but years ago we said we have to have our own platform, but we're not going to stop working with them." They became partners. So he went from ... he didn't say wrapper, but there's a lot of distinction between how AI is working with the frontier models. What's your reaction to that, because I think he nailed it.
Ali Hussain
>> I completely agree.
John Furrier
>> Are you guys going down that same road?
Ali Hussain
>> A little bit. If you think about our context graph, as much as we're leaning on the frontier models, there's parts of contracts, et cetera, where LLMs are actually not the solution. There's a lot of classical machine learning happening, others that are, "What are our IP that make our products unique." But on top of that, our CTO co-founder Deepak, he's been back and forth with the Anthropic team I think three times in the last 60 days. We're collaborating. I think the smart research labs know that. Healthcare, finance, legal, these are massive industries that are going to change, and they want to do it in a much more partnership mindset.
John Furrier
>> Yeah, it's great. And it also gives you durability in terms of the headroom. I want to talk about the origination story. Did you just wake up one day and say, "Hey, I'm going to tackle finance"?
Ali Hussain
>> Yeah.
John Furrier
>> It's not obvious, but it's clearly a problem and a huge market. What was the origination story? Take us through that.
Ali Hussain
>> I think, I mean, one of it's just like shared and lived experience. I've lived most of my professional career as an operator. Last company, I was CEO of a company called Latch. We dealt with hardware, software, all different type of business models, and I really saw prior financial systems, particularly ERP, have a lot of limitations. But it was hard for me to pinpoint why some of these systems were so problematic, and I realized a lot of it ties to complex data and how hard it is to pipe in and out. And so when I left, I knew I wanted to go after a really big market. Everything tied to ERP, some would argue, is the largest enterprise spend category. There's more spent on ERP than the market caps, even last year of Salesforce and HubSpot combined. And so it's like, how do we really think about a big market? But then on top of that, I saw a lot of the advancements in AP spend and in pairing benefits, and it just didn't make sense to me that the revenue thing couldn't be solved. And when we saw the moment with the LLM, it finally made sense. Data could be re-architected. Major pain points could be rethought. And then what I couldn't have predicted is the speed this is happening and the agent story on top, and I'm just incredibly fortunate to be in the right place at the right time really.
John Furrier
>> Yeah. One of the common themes, Ali, in this AI, some of the success stories like Tabs, is that it enables and accelerates to really do things faster. So from six months to six days, six years to six months. I mean, this is like the order of magnitude. It's almost magical when customers see a demo. So do you have those moments, or can you share a story where a customer's just like literally falling out of their chair thanking you, throwing a parade for you? I mean, what's been the reaction?
Ali Hussain
>> Yeah. I mean, when you look at the difficulty it used to be between a company closing a deal with their customer, and the time it took and the amount of humans to get to a paid invoice, I mean, it was like, if you remember in middle school, how a bill gets made in Congress. You have to go all over the place, and now we get on a demo. I was on a demo yesterday, and they just load up their contract and the AI and our context graph reads it. It computes a complex contract into a very clean invoice. It shoots it off. It allows someone to pay and it reconciles directly from your Chase account. That's magical. These are simple moments that you would have imagined three teams and a bunch of CSI work to figure out what's going on, and now the agents just take it all the way to payment.
John Furrier
>> There's a crime scene here.
Ali Hussain
>> Yeah. "Where's the document?"
John Furrier
>> "Where's the money? Did they pay?" I mean, there's a lot of manual labor that is error-prone.
Ali Hussain
>> Yes.
John Furrier
>> Just human error.
Ali Hussain
>> A hundred percent. I mean, humans on average when it comes to billing, like we're talking 10 to 15% errors. These are not like single digits, and like on a multimillion dollar contractor, renewal clause and amendment, there are real dollars that are leaking through this process.
John Furrier
>> Well, great story. Congratulations. Share some momentum stats for the folks watching, where you guys are at. Obviously just closed a Series B, your last round of financing. What are your plans? You're hiring? How many customers do you have? Give some stats to share the momentum.
Ali Hussain
>> The company's been now around just under three years. Started selling product about 18 months ago. Moved from first six months, getting to our first seven figures in revenue to now getting to the eight figures of revenue part, which is exciting. Racing towards our 500 customers. Get to support some of the fastest, most compelling companies in the world, like Tennr you mentioned earlier, but then on top of that, get to work with a lot of traditional businesses in marketing, accounting, et cetera. And then on top of that, just opened up our beautiful headquarters in Soho, 130 employees, growing rapidly. And so anyone who wants to come join a fast and highly intense but fun place to work, come join us.
John Furrier
>> Of course. IBM has this thing called Client Zero, where this is a better word than dogfooding or drinking your own champagne. There's been other metaphors, but you guys use Tabs for tabs.
Ali Hussain
>> I am our AR clerk. Until today, you'll get a kick out of this, John. I have every employee in Slack with me with me and our agent, and every morning they watch my interaction. If the agent gets it right, the agent's having a bad day, I'm having a bad day. Everyone, it feels like theater behind us, they watch me dogfood the product. I chase every invoice. I find what's working in the product, the magical moments, but also we find where we fall apart together and we work together to improve that.
John Furrier
>> Ali, great to have you on. One more thing I want to touch on. You mentioned small and medium-sized enterprise, mid-market. We're seeing interesting things happening right now with AI. Either the startups are native with AI or this mid-market, and then you have the enterprise. I was just at MWC in Barcelona last week and I put out a report on hyperconverged edge, just more about AI factories at the edge, but that's going to be about a two-, three-year transition of the big telcos. But the small, medium-sized market of the business is booming. So it seems that the underserved market for a lot of this AI value are the growing businesses or the mid-market, as you said.
Ali Hussain
>> Yes.
John Furrier
>> This seems to be a feature, not a bug. It's almost like it's ripe to get in there. It seems to be a great spot for AI companies to come into.
Ali Hussain
>> For anyone building right now, the SMB. I mean, obviously at the end of the day all of us as AI companies have to make money. The mid-market is now the most, I believe, willing to pay, has real labor challenges, has need to cut a lot of spend, especially in G&A right now, and it's just ripe for building very .
John Furrier
>> And they don't have the budgets to go to the big monolithic systems.
Ali Hussain
>> They don't have budgets. They can't pay all these big ...
John Furrier
>> They're underserved....
Ali Hussain
>> firms. They're underserved. And some of them are finding some really good moments and building and scaling, but they're looking to AI to make this more efficient.
John Furrier
>> Well, as you continue growing, expand that market base. Enterprise will be ready in a year, maybe about a year.
Ali Hussain
>> Maybe 18 months.
John Furrier
>> Oh, no. It's tons of headroom. Congratulations on Tabs. Thanks for coming on.
Ali Hussain
>> Really appreciate you having me here.
John Furrier
>> I'm John Furrier here with theCUBE. We're previewing the Agent Conference coming up in May, as well as our Mixture of Experts series. As agents come in, the productivity, the labor challenge, and just the ability to get things done fast, to reduce the time it takes to do things really with the human in the loop, is going to be a very big deal, certainly in all markets, mid-market and the enterprise. We're doing our part to bring that to you. Thanks for watching.
John Furrier