Private markets
Robotics · Humanoid Robotics

Apptronik

Builds general-purpose humanoid robots and robotic systems.

Research status
Research coverage
Headquarters
Austin, TX
Coverage
2 product lines mapped
Last reviewed
September 2026
Product map

What the company builds

Products and operating platforms help connect company-level news to the revenue pools, customers, competitors, and supply chains it may affect.

01

Apollo

General-purpose humanoid robot designed for useful work.

02

Robotics platform

Actuation, controls and embodied-AI development stack.

01 / THE BUSINESS

How Apptronik creates value.

Builds general-purpose humanoid robots and robotic systems. The research focus is whether customer adoption can support attractive economics after development, delivery and ongoing service costs.

SectorRobotics
Founder coverage3 profiles
Research checkedSep 18, 2026

02 / PEOPLE BEHIND THE COMPANY

Meet the builders.

Co-founder

Jeff Cardenas

Jeff Cardenas is a co-founder of Apptronik. The company’s founding and leadership material provides context for its work in humanoid robotics.

Explore founder

Co-founder

Nick Paine

Nick Paine is a co-founder of Apptronik. The company’s founding and leadership material provides context for its work in humanoid robotics.

Explore founder

Co-founder

Luis Sentis

Luis Sentis is a co-founder of Apptronik. The company’s founding and leadership material provides context for its work in humanoid robotics.

Explore founder

Selected founders; historical founding roles are distinct from current management positions.

03 / CAPITAL & OWNERSHIP

Follow the financing.

Selected disclosed events, newest first. Announcement values describe that transaction and date; they are not current share prices. Debt, equity and secondary transactions are identified separately.

  1. Equity

    Series A-X extension

    Disclosed amount$520M
    Not disclosedNot disclosed

    Named participants: B Capital · Google · Mercedes-Benz · PEAK6 · AT&T Ventures · John Deere · Qatar Investment Authority

    The extension brought the total Series A to more than $935M. Do not add the extension to that total again.

    Read the announcement

Explore the named investors.

Names refer to the cited transactions. They do not establish current ownership, ownership percentages, or endorsement of this website.

04 / IN THEIR OWN WORDS

Watch. Listen. Form your view.

Interviews reflect the speaker’s perspective at publication. Company statements are not independent verification.

05 / EDITORIAL ANALYSIS

The opportunity. The open questions.

Our interpretation of the business model and cited sources, not a valuation or a recommendation.

What could work

The case to investigate

  • Commercial partnerships, real-world pilots, and production readiness.
  • A stronger product position becomes economically meaningful when customers renew, expand and pay enough to cover delivery costs.
What could go wrong

Pressure-test the thesis

  • Hardware scaling.
  • Competitive intensity.
  • Customer ROI.

What to watch next

01

Pilot conversion — what changed in the latest original disclosure?

02

Manufacturing partnerships — what changed in the latest original disclosure?

03

Autonomy performance — what changed in the latest original disclosure?

YOUR RESEARCH WORKSPACE

Keep the questions that matter.

Use this checklist to record what you have investigated. Your notes stay in this browser.

06 / RESEARCH TRAIL

Go straight to the source.

Research checked Sep 18, 2026. Selected public disclosures, not a complete capitalization table or securities-filing search. Missing information is left unconfirmed. No investment availability or IPO date is implied.

Why SMYC is watching

Commercial partnerships, real-world pilots, and production readiness.

Private-company information is incomplete, episodic, and difficult to verify. Treat every data point as a starting point for diligence—not a substitute for company-approved materials, legal review, or investment analysis.

Add to my private current

Signals to monitor

Pilot conversionManufacturing partnershipsAutonomy performance

Risks to underwrite

Hardware scalingCompetitive intensityCustomer ROI
Underwriting map

What an investor should actually diligence

Measure useful work delivered, not demonstrations. The key bridge is from a supervised pilot to repeatable deployments with improving utilization, reliability, service burden, and customer payback.

Buyer map

01

Manufacturers and warehouses

02

Logistics operators

03

Automotive and industrial firms

04

Research and development partners

Economics to request

01

Paid deployments and contracted units

02

Hours between intervention

03

Bill-of-materials trajectory

04

Deployment and support labor per unit

05

Robot-as-a-service contribution margin

Moat tests

01

Performance generalizes beyond staged tasks

02

Data flywheel improves deployment speed

03

Hardware supply chain can scale

04

Customer payback survives labor and financing assumptions

Milestone map

01

Pilot-to-production conversion

02

Manufacturing ramp

03

Autonomy reliability gain

04

New task or environment deployment

Comparable lensesIndustrial automationWarehouse roboticsAutonomous vehiclesAI software and sensors
Underwriting framework

How to diligence Apptronik

A company-specific starting point based on the operating realities of robotics. Replace assumptions with verified company materials, customer evidence, legal documents, and expert work.

Economics to model
01

Hardware gross margin

02

Service and software revenue per deployed unit

03

Manufacturing cost curve

04

Customer payback period

Evidence of proof
01

Paid deployments and renewals

02

Robot-hours without intervention

03

Task reliability across environments

04

Serviceability and fleet uptime

Comparable lenses
01

Industrial automation

02

Machine vision

03

Warehouse robotics

04

Labor and fixed-automation alternatives

Questions to answer
01

Does the demo generalize?

02

Who carries maintenance and downtime risk?

03

Which component controls cost?

04

How much new data improves performance?

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