Evidently Healthcare AI: Leadership and Company Facts

Introduction

Evidently at evidently.com describes its business as clinical-data intelligence and AI-assisted tools for healthcare organizations. Its company page names seven leaders, including CEO and co-founder Feng Niu and CTO Jaeho Shin. This guide concerns that healthcare business, rather than the similarly named model-evaluation software company.

Verified October 7, 2026. Healthcare clinical-data AI company and its seven published leadership profiles.

Detail

Which Evidently company does this guide cover?

The official company page describes tools including chart summarization, note drafting, denial-appeal management and HCC capture. Those are examples of the company’s stated product scope; they are not independently verified clinical outcomes.

The domain is an essential part of the company record. Matching only the word “Evidently” can combine people and products from unrelated businesses.

Feng Niu, PhD, CEO & Co-Founder of Evidently. Official source.

Evidently leadership team

The source publishes the following seven names and titles. The table preserves the combined President and Chief Product Officer role rather than splitting it between different people.

Name	Published title
Feng Niu, PhD	CEO & Co-Founder
Jaeho Shin, PhD	CTO
Kalie Dove-Maguire, MD	President & Chief Product Officer
Casey Conlon	Chief Revenue Officer
Kai Romero, MD	Head of Clinical Success
Andrés Krogh-Walker	Head of Marketing
Chris Cowart	Head of Growth

What the titles establish

Feng Niu is listed as CEO & Co-Founder. Jaeho Shin is CTO, and Kalie Dove-Maguire is President & Chief Product Officer. The remaining profiles cover revenue, clinical success, marketing and growth.

The page does not supply appointment dates or a detailed reporting chart. It would be inaccurate to infer that every “Head” reports to a particular executive or that the seven profiles represent the whole workforce.

How to use the company and leadership worksheets

The download separates two company facts from seven leadership profiles. Every record points to the official company page, with an explicit note that the page is undated and was checked on October 7, 2026.

A verification date is useful for refreshing a directory, but it is not an employment start date. Street addresses and undisclosed job responsibilities remain outside this guide’s evidence scope.

Download the Evidently research spreadsheet

Healthcare clinical-data AI company and its seven published leadership profiles. The worksheets retain source URLs, source dates, verification dates and specific evidence gaps. Blank amounts, dates or addresses mean the reviewed source did not establish them.

Download Evidently research Excel

Update Evidently research with BrowserAct

Start a custom Bot with the official sources below. Keep each fact connected to its source and date, then review the structured output before adding it to your company-research file.

Use Agent Built for a custom source and field list. A relevant BrowserAct template can provide a starting point when its target and output match your task. The cloud Bot is the main workflow here; CLI access is optional for developers connecting their own Agent.

1Open BrowserAct Dashboard
Click the left-side + to create a Bot, choose Build with Agent or an available Quick start entry, or paste your request into the center Agent input.

BrowserAct Dashboard for building a Evidently scraper

BrowserAct Bot creation screen, captured September 10, 2026. Interface labels may change.

2Copy the complete prompt
Review the exact target, result limit, and fields before running the Bot.

Copy the complete promptUse the green Copy button. Scroll inside the prompt to review every line.

Open these official sources:
https://www.evidently.com/company
Collect up to 100 relevant public records. Return company_domain, name, exact_title, product_category, source_date, evidence_scope, evidence_gap, source_url and checked_at.
Preserve exact company names, roles, units and source dates. Keep publication dates, event dates and verification dates separate.
Do not invent missing titles, street addresses, amounts, valuations, percentages or dates. Do not treat selected records as an exhaustive database.
Keep financing rounds, cumulative totals, valuations, tender offers and investor participation separate. Do not add overlapping amounts.
Keep operating locations, legal addresses and mailing addresses separate. Do not infer a headquarters or a complete team.
Flag conflicting or future-effective statements for manual review.
Use read-only browsing. Do not submit forms, send messages or change accounts.
Pause for manual login, CAPTCHA, 2FA or account confirmation. Do not bypass restrictions.
Return structured tables and CSV or JSON with a source URL for every row.

Scrape data from any website.

Describe the data you need. Get a Bot — a reliable, reusable scraper.

Collect source-linked Evidently records and preserve dates, categories and evidence gaps.

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3Handle login only when asked
If the site requests login, CAPTCHA, 2FA or account confirmation, pause and complete the required step manually. Do not bypass restricted access.
4Review, dedupe, and export
Compare each row with its source, remove duplicates, and keep missing fields blank. Export CSV or JSON, then open the data in Excel or Google Sheets. Review changes before replacing your previous snapshot.