Taiga

AI-native medical billing for modern practices | @ycombinator P26

[Taiga @TaigaBilling]

Check out our new feature!

[Nanda Guntupalli @nandaguntupalli]

One of the biggest frustrations we hear from prospective customers is that their current billing company feels like a black box. They do not know what is happening, where things stand, or whether the right work is actually getting done.

At first, we thought transparency meant giving clients detailed dashboards. But we realized dashboards often create the same problem in a different form: too much data, too much noise, and not enough clarity. Clients stop looking, so the work still feels like a black box.

So we built a way for clients to simply ask questions in plain English and get back clear, digestible answers and reports. Instead of digging through an EMR or trying to interpret a crowded dashboard, they can ask what they want to know and immediately understand what is happening.

As an end-to-end billing service, our goal is to make the work visible, understandable, and useful. Clients should be able to see what we are doing, understand where their revenue cycle stands, and get straightforward answers without feeling overwhelmed.

[Adam Wax @AdamWax3]

Most people want to throw AI at everything in healthcare, particularly medical billing.

AI is very helpful for documentation review, scribing, and denial management. However, I think the most important advancement in billing will not come from LLMs.

The core billing problem is data quality. Claims need to be standardized, properly formatted, and compliant with payer-specific rules before they go out.

A lot of billing companies focus on denial management. That is important, but the better goal is to prevent denials at the source. The best way to solve this is with a rules engine built from claims data, payer behavior, and outcomes.

For this payer, in this state, under this plan, with this code, modifier, diagnosis, eligibility status, provider NPI, and documentation trail, should this claim be submitted? If not, why and how do we fix it?

That answer should not come from a black box like an LLM. It should come from a transparent rules engine that checks payer rules, coding, eligibility, provider data, documentation, and past outcomes before submission.

At Taiga, we have seen a 98% touchless claim rate, meaning 98% of claims go from EHR to payment without human intervention. That frees up time spent managing denials and gets clinicians paid faster.

The goal is simpler than most think: send out claims that are easy to say yes to.

If you want to take advantage of our state of the art rules engine, book some time with us through the link in the comments.

Attached below is a picture of me as a child giving the first examination after my parents' opened up their brand new office :)

[Y Combinator @ycombinator]

🏥 @TaigaBilling is the AI-native medical billing company for modern practices.

They file claims with insurance and follow up on every single one until the practice gets paid so clinicians can focus on seeing patients.

Congrats on the launch, @nandaguntupalli and @AdamWax3!

https://t.co/TwZwMCOOoX