Ajay Shrihari

Who they are

Ajay Shrihari is the founder and CEO of Coral Health — founded in September 2024 to automate healthcare's back-office, and closed a $12.5M round led by Lightspeed and Z47 in April 2026.

Person

Ajay graduated from IIIT Hyderabad in May 2021 with a BTech in Information Technology, having already done research stints at IISc (deep learning) and IIIT's own Robotics Research Center, plus an internship at CropIn building satellite imagery platforms. Out of college he went founding-track immediately — Founding Engineer, then Founding Product Lead at roles that fed into LimeChat, a social commerce platform he later advised. That arc — researcher to builder to founder, all in under five years — culminates in Coral Health, which he started in September 2024 from scratch. He posts actively on LinkedIn about the specific absurdities of healthcare administration: a notable post describes an 862-page fax covering 27 patients, the kind of operational horror story that doubles as a product pitch. The through-line is applied AI at the messy edge of operations — robotics and satellite imagery early on, now the fax machine and prior authorization queue.

Company

Coral closed a $12.5M round led by Lightspeed and Z47 in April 2026 — its most recent and significant financing milestone, roughly 18 months after founding. The platform automates prior authorizations, patient intake, and fax workflows for specialty healthcare providers, claiming 99.7% accuracy on healthcare back-office documents. A notable commercial signal: a portion of customers are paying full contract value upfront, unusual in enterprise healthcare software. The team is now hiring engineering and healthcare operations talent on the back of that raise, and Ajay has said Coral is targeting 4x revenue growth by end of 2026. The product roadmap includes an AI workflow builder so providers can design their own administrative workflows, plus a co-pilot layer to surface intelligence from processed data.

Market

Healthcare administration in the US is under structural pressure — labor shortages and falling reimbursement rates are forcing specialty providers to automate workflows they've historically staffed manually. Coral's positioning is that it works with existing EHR systems, fax lines, and payer portals rather than requiring infrastructure changes, which differentiates it from general RPA providers who can't handle the bespoke complexity of healthcare back-office documents. The competitive field is large — over 1,400 active competitors by one count — but Coral's specific focus on specialty care workflows (DME/HME, referrals, prior auth) gives it a narrower and more defensible wedge than broad-based healthcare IT incumbents.

Network

Ajay's immediate network at Coral includes Jared Gaynes and Abha Bhakoo, both peers involved in the company's interview and strategic alignment process. His earlier career touched LimeChat's founding team, where he served as a Founding Advisor after his hands-on product and engineering roles there.

Jared Gaynes· Peer / team member at Coral Health
Abha Bhakoo· Peer / team member at Coral Health

How they likely show up

Founded Coral from scratch in September 2024 and closed a $12.5M round by April 2026 → moves at founder speed; short feedback loops, not committee-driven.
Career went from deep learning researcher at IISc to founding engineer to product lead to CEO in under five years → comfort with role-switching; likely expects the same adaptability from people around him.
Posts on LinkedIn about specific operational absurdities (862-page fax, 27 patients) rather than thought leadership → communicates through concrete examples, not abstractions.
Built at the intersection of AI and messy real-world data (satellite imagery, robotics, now healthcare fax) across multiple roles → drawn to technically hard problems with clear operational stakes, not clean API problems.
Took on a founding advisor role at LimeChat alongside other work → relationship-oriented; maintains ties after moving on.

Conversation tips

→ Bring a specific operational example from healthcare administration — he engages best with concrete absurdities, not high-level market framing.
→ Reference the fax-machine angle directly; his public positioning is that Coral works *with* fax infrastructure rather than replacing it, which is a deliberate and defensible product bet worth probing.
→ Ask about the customer-paying-upfront dynamic — it's an unusual commercial signal for early healthcare software and he'll have a clear view on what's driving it.
→ He came up as an engineer and researcher before becoming a product lead and CEO; technical depth lands better than sales or process framing.
→ Don't assume he's post-technical — the 99.7% accuracy claim and the co-pilot roadmap suggest he's still close to the product decisions.

Toolbox

Openers

Open on the 862-page fax post — he published about a single fax covering 27 patients as an example of what Coral is built to handle; it's a visceral entry point into the product thesis and signals he's thought hard about the operational reality, not just the market size.
Reference the upfront contract payment dynamic — a portion of Coral's customers are paying full contract value upfront, which is genuinely unusual in enterprise healthcare software; asking what's driving that signals you've read past the press release.
Bring up the workflow builder roadmap — Coral is building a layer that lets providers design their own administrative workflows rather than consuming fixed automations; it's a meaningful architectural bet about where the product is going.

Discovery questions

You've positioned Coral as working *with* fax infrastructure rather than replacing it — how does that constraint shape what you can and can't automate, and where does it break down?
You went from deep learning research at IISc and robotics at IIIT to automating prior authorizations — what was the specific moment or insight that made healthcare back-office the problem worth founding a company around?
You're targeting 4x revenue growth by end of 2026 — is the constraint right now distribution, headcount, or the accuracy ceiling on the documents the model hasn't seen before?

Avoid

Don't lead with general AI-in-healthcare market size framing — he builds at the operational layer and will engage much more readily with specific workflow problems than with TAM narratives.

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