Business News | 50 Million AI Interactions a Month: Why Markytics.AI Built Its Own Infrastructure Instead of Renting It
Get latest articles and stories on Business at LatestLY. New Delhi [India], September 2: Most AI companies are, underneath the branding, a wrapper. A product layer sitting on top of someone else's model, running on someone else's rented compute, billed by the token. It's the fastest way to ship something that works in a demo. It's also a dependency most of those companies never talk about out loud: if the underlying API changes its pricing, its rate limits, or its terms of service, the product built on top of it changes too -- whether its customers agreed to that or not.
VMPL
New Delhi [India], September 2: Most AI companies are, underneath the branding, a wrapper. A product layer sitting on top of someone else's model, running on someone else's rented compute, billed by the token. It's the fastest way to ship something that works in a demo. It's also a dependency most of those companies never talk about out loud: if the underlying API changes its pricing, its rate limits, or its terms of service, the product built on top of it changes too -- whether its customers agreed to that or not.
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Markytics.AI made a different, more expensive bet early on: build the stack from the ground up, own the infrastructure it runs on, and answer to no third party's roadmap.
No rented servers, no third-party APIsThere is no OpenAI or Anthropic API call sitting quietly in the middle of a Markytics.AI production interaction. The models, the speech infrastructure, and the servers they run on are the company's own -- not leased capacity on someone else's cloud, but AI infrastructure Markytics.AI has invested roughly half a million dollars in building and owning outright.
That's a harder, slower path than renting GPU time from a hyperscaler. It's also the only path that gets a bank or NBFC a straight answer to the question they always ask: whose infrastructure is our customer data actually touching? For Markytics.AI, the answer is simple, because there's only one name on it.
"Renting compute gets you to a demo. It doesn't get you to 50 million interactions a month without someone else's outage becoming your outage," said Nikhil Patil, Chief Technology Officer at Markytics.AI. "Owning the stack was the only way we could actually guarantee uptime at the volume our customers run at."
What that infrastructure is actually doingThe number that matters isn't a benchmark score. It's throughput. Markytics.AI's platform is currently handling roughly 2 million AI interactions a day -- north of 50 million a month -- across the banks, NBFCs, and cooperative institutions it's deployed in.
50 million interactions a month, and not one of them touched a third-party API.
That scale is the real pitch to an enterprise buyer, more than any feature list. A bank's technology team doesn't just want to see a platform work in a pilot with a few hundred conversations -- they want evidence it holds up at the volume their actual customer base will throw at it, without falling over, without a rented API silently rate-limiting it during a peak collections cycle. Scale, sustained without incident, is what turns a promising pilot into a signed enterprise contract.
Enterprise and government: where the volume gets seriousWithin that overall throughput, two segments carry a disproportionate share of both the difficulty and the payoff. Large enterprise accounts -- big banks and NBFCs running Markytics.AI across multiple functions at once -- account for the bulk of the platform's highest-value activity, the accounts where a single customer relationship spans collections, cross-sell, service, and audit simultaneously.
Government is the more striking number. Markytics.AI's government deployments alone now handle more than a million calls a month -- inside a segment where most AI vendors, domestic or global, don't have a single live production account, let alone volume at this level. Government procurement is slower and more demanding than commercial BFSI in almost every respect: longer approval chains, stricter data-residency requirements, more parties who have to sign off before a pilot becomes a production system. Clearing that -- and then sustaining a million-plus calls a month on top of it -- is a different kind of proof point than a commercial logo on a website.
Agents built on top of owned infrastructureThat infrastructure is what the platform's agents actually run on: a collections agent working queues at volume, a cross-sell agent that reads a conversation for buying signals a generic CRM field would miss, an audit agent that turns every interaction into a structured, searchable record instead of a monthly sample review, plus service, credit, and relationship-management agents rounding out the roster. None of them are separate products stitched together -- they're workloads running on the same infrastructure stack, which is part of why adding a new one doesn't mean starting the technical integration over from scratch.
The business case for owning the stackThat infrastructure investment is starting to show up on the other side of the ledger, too. Markytics.AI is currently at $1.5 million in annual recurring revenue, with the company tracking toward $3 million in the near term -- growth pulled largely from institutions that needed to see this kind of scale and ownership before they'd sign anything.
That's the loop the infrastructure bet was designed to create: owning the stack made the scale possible, the scale made enterprise buyers comfortable committing, and the commitments are now showing up as recurring revenue rather than one-off pilots.
"Every enterprise account we bring on asks the same question in a different way: what happens if something goes wrong at 2 a.m.," said Shilpa Pahlwani, who heads Customer Success at Markytics.AI. "Being able to say we own the infrastructure end-to-end changes that conversation completely -- it's usually the point where a prospect stops evaluating us and starts planning the rollout."
"We didn't want to be one API outage away from an SLA we couldn't hold," said Mohit Kokil, founder of Markytics.AI. "If we're asking a bank to run their customer conversations through us at this volume, we'd better own every piece of what happens to them."
What's nextThe roadmap keeps compounding on the same infrastructure rather than replacing it: more compute capacity to keep pace with interaction volume, more agents added to the same owned stack, and continued investment in the underlying models rather than a dependency on someone else's next release. The bet is that owning the infrastructure isn't just a cost decision -- it's the reason the platform can promise an enterprise customer something a wrapper never can.
More on how Markytics.AI is approaching this can be found at www.Markytics.com.
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