Mantys Made Revenue Honest. Then It Pointed AI at Hospitals.

Ask a SaaS founder what their annual recurring revenue is and watch what happens. The confident ones give you a number. The honest ones give you a number and a wince. Somewhere in a shared drive is a spreadsheet, and inside that spreadsheet is a formula only one person understands, and that person is on vacation. Mantys was built for that wince.

Founded in 2022 and picked for Y Combinator's Winter 2023 batch, Mantys started with an unglamorous premise: the number a company reports as its revenue and the number that is actually true are rarely the same. Its first product was an ARR reporting and forecasting tool that connected to a company's CRM, billing system and accounting software, then did the arithmetic in real time instead of once a month by hand.

That line does a lot of work. Contracted ARR is what customers have signed. Billed ARR is what has gone out on invoices. Realized ARR is what has actually been earned. Unearned ARR is money collected for value not yet delivered. Most teams track one of those four and quietly hope the others agree. They usually don't - especially once usage-based pricing, mid-cycle upgrades and multiple currencies enter the picture.

This is not pedantry. The gap between those numbers is where board decks go wrong, where forecasts drift, and where a company can believe it is growing faster or slower than it actually is. A founder who confuses billed revenue with earned revenue can hire against money that has to be given back. The whole category of "revenue recognition" exists because the timing of when money is promised, invoiced, collected and earned almost never lines up. Mantys' bet was that this reconciliation should not be a monthly act of manual labor performed by whoever is best at spreadsheets.

01 / THE PROBLEMThe week that disappears every month

The pain Mantys targeted is specific and measurable. As a SaaS company scales, revenue data fragments across systems that were never designed to talk to each other. Finance teams, per Mantys' own launch materials, end up spending five to seven days a month reassembling that data into a picture leadership can act on. It is slow, it depends on individuals, and it breaks the moment pricing gets interesting.

Mantys' answer was to treat revenue as a data-engineering problem rather than an accounting chore. Pull from HubSpot for the deals, Chargebee for the billing, the ledger for what actually landed, clean the mess, and present one live view of Contracted, Billed, Realized and Unearned ARR - broken down by customer and cohort, with expansion, contraction and churn visible in the same place.

The product also let teams model scenarios. Instead of forecasting by editing a fragile spreadsheet and praying nothing downstream broke, a finance lead could ask what-if questions - a price change here, a churn assumption there - and see the effect in minutes. The value was less about any single chart and more about removing the person-shaped bottleneck: the dependency on one analyst who happened to hold the whole model in their head. When the numbers refresh themselves, the business stops being hostage to a calendar reminder and a caffeinated afternoon.

02 / THE PEOPLETwo founders who know where the bodies are buried

The founding team explains the product. Kriti Arora, Mantys' CEO, spent time at a growth-stage venture fund, where part of the job was helping portfolio companies build their management information systems and plans. She saw, over and over, businesses running on spreadsheets that cracked under their own weight. Mudit Dangi, her co-founder, built data pipelines and parsers at the quant firm DE Shaw - which is to say he spent years making messy data behave.

Put those two backgrounds together and Mantys reads less like a hunch and more like an inevitability. One founder knew exactly which numbers startups get wrong; the other knew how to build the plumbing to get them right. That combination matters more than it looks. Plenty of teams can build a dashboard. Far fewer have sat on the investor side of the table and watched a company's real numbers arrive late and wrong, then had to explain the discrepancy to a partnership. Arora had lived the demand side of the problem before building the supply side.

It also shaped who Mantys could credibly sell to. When your pitch is "we will compute your revenue more accurately than you do," the buyer's first instinct is skepticism - finance leaders do not hand over the crown jewels to a tool that might quietly get the math wrong. Founders who can speak the language of deferred revenue, cohort churn and multi-entity consolidation clear that trust bar faster than a generalist would.

03 / THE CUSTOMERSCompanies already drowning in the problem

Mantys' early customers were not experiments. They were scaled SaaS companies across the Asia-Pacific region and the United States - reportedly more than ten Series A and later businesses. Among the named ones: NextBillion, which used Mantys to pull data from HubSpot and Chargebee into a real-time view of its contracted, billed, churned and forecasted ARR; plus MoEngage and Locus. These are companies whose spreadsheets had graduated from inconvenient to genuinely risky.

That customer profile is telling. A seed-stage startup does not feel this pain - its revenue fits on a napkin. The ache starts at Series A and beyond, once there are enough customers, contract types and billing quirks that no single person can hold the picture in their head. Mantys aimed precisely at that inflection point, where a company is big enough to be confused by its own numbers but not yet big enough to have a data team building internal tooling to fix it. Selling into that window means your best prospects are the ones already visibly struggling with the exact problem you solve - the shortest sales conversation there is.

Where it fits · The revenue stack

Mantys sat in the seam between systems

SOURCE

CRM · Billing · Accounting

HubSpot, Chargebee and the ledger - three systems, three versions of the truth.

MANTYS

Clean, reconcile, model

Data cleaning, multi-entity and currency handling, scenario what-ifs.

OUTPUT

One live revenue view

Contracted, Billed, Realized, Unearned - by customer and cohort.

04 / THE DIFFERENCENarrow and honest beats broad and vague

The obvious competitor was never another dashboard. It was the spreadsheet itself - free, familiar, and infinitely flexible right up until it isn't. Mantys also sat in a field with financial-planning and metrics tools like Mosaic, Aleph, Runway and Pigment. Its wedge was narrower than "plan your whole business." It was: get your revenue number right, automatically, even when your pricing is complicated. Usage-based billing, unbilled entries, multi-entity structures and currency conversion were features, not edge cases.

05 / THE TURNSame obsession, new arena

Then Mantys did the thing startups are quietly warned against and openly celebrated for: it moved. Today the company's public front door leads with a different sentence - "Automate Eligibility Verification with AI" - and points at healthcare. The target is revenue cycle management, and specifically the grind of checking whether a patient's insurance actually covers what is about to happen.

Read past the change of industry and the DNA is identical. Insurance eligibility verification is manual, repetitive, error-prone and expensive when it goes wrong - a back-office process where the reported answer and the true answer often diverge until someone spends hours on the phone. It is, in structure, the same problem as the four-flavored ARR question, wearing hospital scrubs.

In its new arena Mantys lines up against healthcare RCM automation players and incumbents - a crowded, well-funded space where AI is the word of the moment. Whether it wins there is an open question. But the framing is consistent enough to be a strategy rather than a scramble: find the least glamorous, highest-friction operational task in an industry, and automate the drudgery out of it.

There is also a cold market logic to the move. SaaS revenue analytics is a good problem, but it is bounded - it sells to finance teams at software companies, a real market with real limits. Healthcare administration, by contrast, is an ocean of manual work. Eligibility checks alone run into the hundreds of millions of transactions, many still handled by staff toggling between payer portals and phone trees. A tool that shaves minutes off each one compounds quickly. The founders' instinct for messy operational data found a much larger swamp to drain.

06 / THE MODELBoring markets, durable software

Mantys is a B2B software company. The original product was sold as a subscription to finance and leadership teams at growing SaaS businesses; the healthcare direction targets providers and revenue-cycle operations. Backing came through Y Combinator's W23 batch and a seed round with investors reported to include Y Combinator, Spark, NuVentures and NEON. The team is small - roughly seven people by most listings - and split between Bengaluru and Dubai.

If there is a lesson buried in Mantys' short history, it is that a moat can be a skill rather than a category. The company's edge was never SaaS metrics specifically. It was a founding team that understood messy operational data and refused to accept that "it just takes a week" is a law of nature. That instinct travels. It went from finance to healthcare without changing shape.

Mantys is still early, still small, and now competing in a much bigger, more contested market than the one it started in. But the question it keeps asking is a good one for any operator to borrow: where is your team quietly paying a tax in hours and errors, on work nobody would ever brag about doing?