Startup profile for AKASA: latest funding, latest known valuation, total disclosed funding, investors, status, sources, and why the company is interesting. Part of the Venture Capital Tracker startup directory.

Startup profile · funding coverage

AKASA funding, valuation and investors

Generative AI platform automating healthcare revenue cycle operations.

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Funding, valuation & investors

Answer-first snapshot

Latest funding

Series C

$120M · June 2024

Latest known valuation

Not publicly disclosed

Total disclosed equity funding

$180M

Excludes debt, grants, acquisitions, secondaries, and IPO proceeds.

Current status

private

growth · South San Francisco, California

Investors in latest funding

Other: Andreessen Horowitz , Coatue

Sources: latest funding Last verified: 2026-07-25

Overview

AKASA uses generative AI to automate healthcare revenue cycle tasks — medical coding, prior authorization, claims, and denials — for hospitals and health systems.

Why AKASA is interesting

Unified automation for medical coding, prior auth, and claims — deployed across 650+ hospitals in customer base per company site.

Product & use cases

AKASA uses generative AI to automate healthcare revenue cycle tasks — medical coding, prior authorization, claims, and denials — for hospitals and health systems.

  • Medical coding and claims automation for hospital revenue cycle teams
  • Prior authorization workflow automation
  • Denial prevention and revenue recovery for health systems

Key facts

  • Series C (Jun 2024): $120M; a16z among investors (CB Insights / VCBacked)
  • Series B (Mar 2021): $60M led by BOND with a16z and Costanoa (AKASA press)
  • Series A (2019): led by a16z Bio + Health
  • Deployed across 650+ hospitals and 6,500 outpatient facilities per company reporting

Funding history (newest first)

Series B

2021 $60M

Source: Series B (2021): $60M led by BOND with a16z and Costanoa participation (company press)

Series A

2019

Source: Series A (2019): led by a16z Bio + Health (a16z announcement)

Investors in our directory

Funds linked from AKASA's profile — open a fund page for stage focus and related deal articles.

Competitive landscape

Hospital revenue cycle remains labor-intensive for coding, prior auth, and denials. AKASA's GenAI platform with expert-in-the-loop targets health systems representing $120B+ in net patient revenue. Competes with AI-native RCM startups and legacy vendors like Waystar; moat depends on EHR integration depth and coding accuracy on clinician gold sets.

  • Thoughtful AI direct

    AI RCM automation competitor.

  • Olive AI adjacent

    Prior RCM automation entrant (wind-down); AKASA focuses on unified GenAI platform.

  • Epic revenue cycle modules incumbent

    EHR-native RCM; AKASA integrates within existing EHR workflows.

  • Waystar incumbent

    Legacy RCM software; less AI-native than AKASA's GenAI approach.

Notable stories

  • Series A (2019): led by a16z Bio + Health (a16z announcement)
  • Series B (2021): $60M led by BOND with a16z and Costanoa participation (company press)

Industries

Healthtech AI & Machine Learning

Related funding articles

Venture Capital Tracker pieces that cover AKASA's financing or category context.

FAQs about AKASA

Practical answers founders, operators, and investors typically search for.

AKASA uses generative AI to automate healthcare revenue cycle tasks — medical coding, prior authorization, claims, and denials — for hospitals and health systems.
Directory-linked investors include /fund/andreessen-horowitz, /fund/coatue.
series-b stage, private status. Series A (2019): led by a16z Bio + Health (a16z announcement)
Private as of July 2026 (status: private).
See fundraisingRounds for verified dates and disclosed amounts.
See competitors section — named alternatives with honest positioning notes.
Not publicly disclosed unless noted in highlights.
HQ not publicly listed on profile.

By Venture Capital Tracker

Last updated:

Editorial note: AI tools assisted with research, structure, or drafting. Venture Capital Tracker retains human editorial responsibility for factual accuracy, relevance, and source quality before publication.