Skild Brain
General-purpose foundation model for robot behavior.
SHARK MONEYYACHT CLUBSearch Shark Money
Search public and private companies, people, funds, markets, tools, and research.
Private markets
Robotics · Robotics Foundation Models
Skild AI
Develops general-purpose AI models intended to control different robotic systems.
- Research status
- Research coverage
- Headquarters
- Pittsburgh, PA
- 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.
Embodied AI stack
Cross-robot intelligence and deployment tooling.
01 / THE BUSINESS
How Skild AI creates value.
Develops general-purpose AI models intended to control different robotic systems. The research focus is whether customer adoption can support attractive economics after development, delivery and ongoing service costs.
SectorRobotics
Founder coverage2 profiles
Research checkedSep 18, 2026
02 / PEOPLE BEHIND THE COMPANY
Meet the builders.
Co-founder
Deepak Pathak
Deepak Pathak is a co-founder of Skild AI. The company’s founding and leadership material provides context for its work in robotics foundation models.
Explore founderCo-founder
Abhinav Gupta
Abhinav Gupta is a co-founder of Skild AI. The company’s founding and leadership material provides context for its work in robotics foundation models.
Explore founderSelected 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.
A financing figure is not established here.
Use the original company sources below to check new announcements. We have not substituted an estimate for a disclosed round.
04 / IN THEIR OWN WORDS
Watch. Listen. Form your view.
A direct interview has not yet been verified for this profile. The company’s official materials are linked below.
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.
The case to investigate
- Whether a shared robotics brain can transfer across machines and environments.
- A stronger product position becomes economically meaningful when customers renew, expand and pay enough to cover delivery costs.
Pressure-test the thesis
- Technical uncertainty.
- Compute requirements.
- Long commercialization horizon.
What to watch next
01
Cross-platform performance — what changed in the latest original disclosure?
02
Commercial integrations — what changed in the latest original disclosure?
03
Training-data advantage — 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.
Whether a shared robotics brain can transfer across machines and environments.
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 currentSignals to monitor
Cross-platform performanceCommercial integrationsTraining-data advantageRisks to underwrite
Technical uncertaintyCompute requirementsLong commercialization horizon 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 Skild AI
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.
Hardware gross margin
Service and software revenue per deployed unit
Manufacturing cost curve
Customer payback period
Paid deployments and renewals
Robot-hours without intervention
Task reliability across environments
Serviceability and fleet uptime
Industrial automation
Machine vision
Warehouse robotics
Labor and fixed-automation alternatives
Does the demo generalize?
Who carries maintenance and downtime risk?
Which component controls cost?
How much new data improves performance?
No securities are offered here.
SMYC does not represent that shares are available, transferable, appropriately priced, or suitable for any person. Private securities are illiquid, speculative, subject to transfer restrictions, and may result in total loss.