The Enablers Primer.

Machines are arriving.

5.08M industrial robots operate worldwide; 603,000 were installed in 2025, up 11%.

The U.S. is the number-two market.

About 38,400 installed in 2025, up 12%; North American orders reached 36,766 robots worth $2.25B.

Capital is available.

U.S. equipment finance is a $1.3T market; monthly new volume hit a record $14.3B in July 2026.

A service fee keeps the robot on the maker's books.

Agility Robotics' filings model a humanoid at about $8,500 a month plus $25,000 to deploy (ballpark, not a quote).

Financiers fund the makers.

Lessors such as DLL and Mitsubishi HC Capital finance robot leases and as-a-service contracts.

Insurance is splitting in two.

Optional generative AI exclusions are spreading: 2,369 filings across 49 states by mid-2026.

Specialist cover exists.

Lloyd's-backed AI liability policies offer limits up to $25M per insured.

Safety standards just moved.

ISO 10218 was reissued in January 2025; its U.S. version, R15.06, followed in September 2025.

Drone rules are still pending.

The FAA's final rule for flights beyond visual line of sight has been at the White House since July 2026.

The gap is operating proof.

Financiers, insurers and regulators all ask for evidence that real deployments run safely.

A physical AI deal clears six gates

8

Four groups pay, insure, run and approve

10

One pilot, six parties

9

Financiers and lessors | Pay

Insurers and brokers | Cover

Software and AI platforms | Run

Regulators and standards bodies | Approve

• Fund the purchase, lease or service contract
• Banks, lessors and vendor-finance arms
• Ask: will this machine be used enough to repay?
• Examples: DLL, Mitsubishi HC Capital, U.S. Bank

• General liability, product, cyber, workers' comp
• Specialist AI liability from Lloyd's coverholders
• Ask: what happens if it fails or injures someone?
• Examples: Armilla, Axis, HSB, Marsh

• Fleet management, simulation, data, orchestration
• Connect robots to site systems and each other
• Ask: can many machines run as one system?
• Examples: fleet managers, simulators, AI model providers

• OSHA, FAA, NIST and standards bodies such as ISO and A3
• Rules, standards and test methods for safe use
• Ask: is it safe, and can you show it?
• Examples: OSHA, FAA, NIST, ISO, A3

Each group asks one question

12

Needs change from pilot to scale

14

Nine terms to know

13

Pilot data on uptime and tasks completed; a signed multi-year commitment

A risk assessment, safety audits, geofencing and trained supervisors

Clear access to site systems and a defined fleet size and data plan

A written safety case and test evidence for the exact setting

Four blockers slow adoption

16

Interoperability is the third blocker

19

Capex is the first blocker

17

Safety proof is the fourth blocker

20

Liability is the second blocker

18

How enablers remove each blocker

21

Capex

Machines cost more than most sites can fund from operating budgets.

EASED BY

Financiers and lessors

Liability

It is unclear who pays when a machine injures someone or damages property.

EASED BY

Insurers and brokers

Interoperability

Machines from different makers must share tasks, maps and data.

EASED BY

Software and AI platforms

Safety proof

Sites need evidence a machine is safe in their setting before it runs.

EASED BY

Regulators and standards bodies

• About $200,000 upfront per humanoid
• About $20,000 to deploy
• About $36,000 a year for software and maintenance
• The client carries resale and obsolescence risk

Buy the machine

• About $8,500 a month per humanoid
• About $25,000 to deploy
• The maker keeps the robot on its own books
• The maker needs capital to build the fleet

Pay a monthly service fee

Armilla AI

Standalone AI liability from a Lloyd's coverholder, up to $25M per insured

Chaucer with Armilla

Vanguard AI program for AI-driven businesses (Feb 2026)

HSB (Munich Re)

AI liability product for small and mid-size businesses (Mar 2026)

Testudo

AI liability cover with $9.25M of capacity, backed by Atrium and QBE

Axis

Program for companies that make and deploy autonomous robots

Specialist cover is arriving

1 Jan 2026

Optional exclusion forms CG 40 47, CG 40 48 and CG 35 08 became available to U.S. liability insurers

• Each maker ships its own controller and app
• Mixed fleets need one view of tasks, maps and status
• Switching makers is costly when data is locked in
• Insurers and financiers prefer systems they can monitor end to end

Why it blocks adoption

A common language between mobile robots from different makers and one fleet controller

Open interface

What it does

OPC UA, with its robotics specification

A standard way to expose robot and machine data to other systems

Open-source robot software framework that many makers build on

Hazard analysis of the task and site

The evidence stack

ISO 10218-1 and -2

Industrial robot safety, reissued January 2025

Standard or rule

Status

Machine design to a recognized standard

U.S. adoption, published September 2025; adds cybersecurity

Test records in the real setting

Legged and dynamically stable industrial mobile robots; committee draft

Operating procedures and trained supervisors

FAA Part 108

Drone flights beyond visual line of sight; proposed August 2025, final rule under White House review since 10 July 2026

Incident logs and continuous operating data

Machines are arriving

23

Insurance is moving two ways

26

Humanoids are entering warehouses

24

Rules are being written

27

Capital is ready

25

• 36,766 robots ordered for $2.25B: units up 6.6%, revenue up 10.1%
• Collaborative robots: 7,212 units worth $241M
• Every one of these machines needs funding, cover, software and a safety sign-off

North America, 2025 orders

• Few resale markets exist for humanoids yet
• Service-fee models put the machine on the maker's books

Why financiers care

• Machines work near people in shared space
• Software updates change behavior after delivery

Why insurers care

A humanoid robot handling boxes and bins in a warehouse, with a worker in the background.

What you are seeing

8 in 10

businesses use financing to acquire equipment

• Universal Robots offers collaborative robot leasing through DLL, with options to buy, upgrade, extend or return at the end of term
• Mitsubishi HC Capital America finances makers' as-a-service contracts, and can buy the payment stream to give the maker cash up front
• Robot dealers route customers to equipment lenders such as Ascentium, Conners Financial and U.S. Bank

Robot financing is already available

• Verisk's ISO unit published optional generative AI exclusion forms, available since 1 January 2026
• 2,369 exclusion filings were on record across 49 states by mid-2026
• Underwriters worry about overlapping lines, shared responsibility and one software update or cloud outage hitting many insureds at once

Pulling back

• Armilla AI, Chaucer, HSB and Testudo have launched standalone AI liability products
• Axis has a program for companies that make and deploy autonomous robots
• Skyward and Apollo launched the ibott autonomous vehicle program, with Marsh

Moving in

Seven routes pay for a machine

30

Pay for capacity and outcomes, not fixed prices

33

Each route puts cost and risk in a different place

31

Warehouse robots are paid for by the task

34

Tax rules favor buying equipment in 2026

32

• Used equipment can qualify if it is new to the buyer
• Leases and service fees follow different rules, so the route changes the tax result
• Clients should check treatment with their own tax adviser before choosing a route

What clients should know

• Each carton or pick is a measurable unit
• Volume swings with the season

Why it fits

• A clear definition of a completed task
• A way to log it that both sides trust

What the contract needs

A robot palletizer stacking cases at the end of a line. Each case and each pallet is a unit that can be counted and billed.

What you are seeing

Makers write most as-a-service contracts

36

Three ownership models sit behind the monthly fee

38

Capital sits behind the maker

37

• Locus Robotics offers all its robots as a service and owns every robot
• Körber's as-a-service program draws on Locus robots
• AutoStore, RightHand Robotics and Skydio sell through leases, per-use fees or bundled service
• The maker, not a third party, holds the contract with the client in most cases

What the market shows

• The maker keeps the robots on its books and funds them with its own capital
• Most common today
• Examples: Locus Robotics

Maker-owned fleet

• A financier funds the robots or buys the payment stream
• The maker still runs the service
• Examples: DLL, Mitsubishi HC Capital

Financier-owned or financed fleet

• An independent company owns the robots for any maker and rents them out
• Still rare
• Example: ARO, launched in December 2022

Third-party fleet owner

Three groups of financiers fund robots

40

Collaborative robots are leased through lessors

42

Named financiers already back robotics

41

Equity funding for physical AI is accelerating

43

• Large leasing arms of banks and manufacturers
• DLL, Mitsubishi HC Capital America
• Dealer-routed lenders: Ascentium, Conners Financial, U.S. Bank
• Want proven machines and credit-worthy clients

Lessors and captive finance

• Lend to makers and fleets that banks pass over
• Trinity Capital, Triple Point Capital, ATEL, Horizon Technology Finance
• Combine venture debt with equipment loans
• Want contracts, a funded balance sheet and a path to revenue

Specialist and venture lenders

• Fund the makers and model developers
• Fund growth, not individual machines
• Their rounds set how fast new machines reach the market
• Want proof of repeat deployments

Equity investors

• Mature product with a known resale market
• Short, repeatable tasks with clear payback

Why lessors like them

• Buy, upgrade, extend or return at the end of term
• DLL offers this for Universal Robots

Lease options

A collaborative-robot palletizer on a mobile frame, which its maker also offers as robots-as-a-service: a monthly fee instead of a purchase.

What you are seeing

~$15B

Robotics startup funding in 2025, the previous record (Crunchbase)

• Skild AI raised $1.4B, bringing its total to more than $2B
• Apptronik added a $520M extension, taking its Series A to $935M, at a valuation above $5B (February 2026)
• Physical Intelligence raised $600M in a Series B at $5.6B (November 2025)
• Humanoid (UK) raised $152M in a Series A at $1.35B (July 2026)
• Skydio raised $110M at $4.4B (April 2026); Zipline raised $600M (January 2026)

Recent large rounds

Five structures fund a fleet

45

Contract terms decide who carries the risk

47

Lenders check six things before they fund

46

• Lender funds the purchase and takes a lien on the machine
• Client owns the machine from day one

Equipment loan

• Lessor owns the machine until the end of term
• Client buys it for a small amount at the end

Finance lease

• Lessor owns the machine and bears resale risk
• Client can return, renew or upgrade

Operating lease

• Maker sells its contract payments to a financier
• Maker gets cash up front, financier gets the income

Payment-stream sale

• Loan to a maker or fleet owner, often with warrants
• Funds growth between equity rounds

Venture debt

Three risks set the price of money

49

End-of-term options protect both sides

50

• What the machine is worth at the end of the term
• Few resale markets exist for newer robot types
• Lessors price this risk into the payment

Residual value

• How many hours the machine works
• Idle machines cannot repay
• Pilot data is the best predictor

Utilization

• New models can make older ones less useful
• Software updates can extend or shorten a machine's life
• Shorter terms and upgrade rights limit the exposure

Obsolescence

Drone financing mixes hardware, service and cover

52

Heavy equipment is familiar to lenders

54

Drone makers are raising large amounts

53

• Makers with strong funding can offer service contracts and absorb support costs
• Lenders favor makers with repeat customers and recurring service revenue
• Insurers and regulators will see more drones in more places, and want more data

What this means for enablers

• Construction equipment has an established used market
• Autonomy adds software that changes how the machine earns

Why lenders care

• Hours worked per week and tasks completed
• Who is liable if an autonomous machine causes damage
• How software updates are controlled

What they will ask

An autonomous excavator or pile driver of the kind built by Built Robotics, working a construction site. The machine looks like conventional heavy equipment.

What you are seeing

A pilot becomes a financeable fleet in five steps

56

How Atomgigs works with financiers

58

A financier asks for six kinds of data

57

Risk can sit with four parties

61

Failures at scale are already visible

64

Robots go where people are most exposed

62

• Fewer people enter dangerous areas
• Inspection data is captured the same way each time

The upside

• A fall or a collision can damage the machine and the asset
• Insurers ask who supervises it and what it may touch

The new exposure

A legged robot climbing steel stairs on an offshore platform.

What you are seeing

• Process units, piping, tanks and stacks spread over hundreds of acres at a U.S. refinery.
• Inspection rounds here cover heat, height, pressure and gas exposure.
• Sites like this are where legged and crawler robots take on rounds first, and where insurers look hardest at who controls them.

Aug 2026

Insurance broker WTW published a guide to insuring physical AI

• Trade press reports that insurers are not yet ready for warehouse robots
• Liability may sit with the manufacturer, the software developer, the integrator or the operator
• The main lines affected are general liability, product liability and cyber

What the market is saying

Six lines of cover touch a robot deployment

66

Drone cover is priced by the hour or by the year

67

~$10

Per hour for on-demand $1M liability cover

• Global Aerospace underwrites programs sold through SkyWatch.AI and Verifly
• Some hull programs are open only to pilots certified under FAA Part 107
• The drone insurance market is estimated at $1.57B in 2025, growing to $4.10B by 2035

Good to know

Silent AI hides in old policies

69

Three questions find the gaps

70

1 Jan 2026

Optional exclusion forms CG 40 47, CG 40 48 and CG 35 08 became available to U.S. liability insurers

• A general liability policy with a generative AI exclusion may not respond to a loss caused by AI software
• Wording varies, so the same event can be covered under one policy and excluded under another
• Insurers are moving from silence to explicit terms

Why it matters for robots

Does the general liability or umbrella policy carry a generative AI exclusion, and does it reach robotics?

Exclusion forms CG 40 47, CG 40 48 and CG 35 08 may apply to AI-driven machines

Question

Why it matters

Who bears liability for a loss caused by software?

The maker, the developer, the integrator and the operator may each point to another

Specialist AI and robotics cover is arriving

72

Affirmative cover names the risk

73

• Wording names AI-driven decisions and errors
• Removes doubt about silent AI

It says AI is covered

• Underwriters expect in-depth review of AI governance and controls
• Expect questions on testing, monitoring and human oversight

It asks for governance

• Often an addition to general liability, cyber and product cover
• Limits are separate and may be lower

It sits beside the core program

Four kinds of insurer and broker serve physical AI

75

Underwriters ask for evidence of control

77

One update can reach every machine

79

Controls limit how far a fault spreads

80

• Update a few units first and watch
• Widen only when results are clean

Staged roll-out

• Machines stop safely when a fault is found
• Humans can take over

Safe-state design

• Machine networks apart from office systems
• Limits the reach of an attack

Network separation

• Do not depend on a single supplier for every task
• Spreads software risk

Vendor mix

• Agreed liability caps and indemnities
• Notice duties when an update goes wrong

Clear contract limits

Inspection robots can lower exposure

82

Atomgigs practices map to underwriter questions

83

• Fewer people enter hot, high or confined areas
• Regular inspection data supports maintenance planning

Where risk falls

• A moving machine near workers needs clear rules
• Software faults and cyber events become new causes of loss

Where risk rises

A four-legged ANYmal inspection robot beside electrical switchgear, with workers nearby in a facility.

What you are seeing

Who is in charge?

Trained people supervise every deployment and can stop the machines

Underwriter question

Atomgigs practice

Is the task suited to the machine?

Site assessment and task scoping before the pilot

How is safety proven?

Risk assessment, procedures, training and audit records

Can the loss history be seen?

Incident and near-miss logs shared with the insurer

Who else is involved?

Contract chain mapped across maker, integrator, platform and client

Seven layers of software run a deployment

86

Platform buyers ask four questions

87

Machines run on open and vendor-specific stacks

89

Open stacks and maker stacks trade off differently

91

Autonomy can be added to existing machines

90

• Software now controls the machine
• Updates and logs matter as much as the iron

What changes

• The base machine, its owner and its lender
• Site safety rules and human supervision

What stays the same

An autonomy kit mounted on an excavator, with its sensors and compute above the cab.

What you are seeing

Fleet software keeps many machines working as one

93

VDA 5050 is an interface, not a brain

95

Fleet software manages mobile robots

94

• Assigns jobs and routes
• Shows status, faults and battery

What the platform does

• Hours and tasks completed
• Stops and incidents

What it records

An autonomous mobile robot of the type used to move goods in warehouses and factories.

What you are seeing

• A standard from the German automotive and machinery associations VDA and VDMA
• Uses MQTT messaging
• Defines how orders and states pass between a controller and a vehicle

What it is

• It does not decide how a robot navigates or avoids people
• It does not replace safety systems

What it is not

• Master control platforms such as InOrbit
• Makers of automated guided vehicles and mobile robots

Who uses it

Simulation lets teams test before the site

97

Simulation tools are mature

98

Foundation models are reaching robots

100

Models propose; people and safety systems lead

101

• Recognize objects and scenes
• Follow natural-language instructions
• Adapt to variation in a task

What models do well

• Behavior can change with new data or updates
• Failures can be hard to explain
• Performance on a demo may not hold on a busy site

Where care is needed

• How was the model tested for this task?
• What stops the machine if the model is wrong?
• Who approves a model update?

What enablers ask

Every site visit produces data

103

Data and model operations form a loop

104

• Images and 3D scans of progress
• Machine health and location logs

What gets captured

• Data improves the models over time
• Records support safety, insurance and finance

Why it matters

A legged Spot robot on a construction site with rebar, the kind of mobile inspection robot used for site capture.

What you are seeing

Machines must connect to site systems

106

Layout robots link to the building model

107

• Plans from the building model become marks on the floor
• Progress flows back into project software

The link

• A wrong model means a wrong mark
• Version control matters on a live site

The risk

A layout robot marking a concrete floor while two site workers look on, with a surveying instrument in view.

What you are seeing

Moving machines raise the cost of a cyber event

109

Four frameworks guide robot security

110

• Stolen credentials and remote access
• Tampered updates
• Ransomware that stops operations

Threats

• Network separation
• Signed updates and rollback
• Least-privilege access and logging

Controls

• Operational technology security tools
• Claroty, Dragos, Nozomi Networks are typical providers

Monitoring

Typical category leaders by layer

112

Atomgigs stays vendor-neutral

114

Several U.S. agencies each cover a piece

117

Three layers make up the rulebook

118

• Binding on everyone in scope
• Issued by agencies and legislatures
• Examples: OSHA rules, FAA Part 107

Laws and regulations

• Written by industry and standards bodies
• Voluntary, but courts, insurers and customers treat them as the benchmark
• Examples: ISO 10218, ANSI/A3 R15.06

Consensus standards

• Set by the client, the insurer and the lender
• Often stricter than the law
• Examples: site safety plan, insurance warranties

Contracts and site rules

OSHA has no robot-specific standard

120

Employers are expected to do five things

122

Workers stand close to machines

121

• Hazards assessed and guarded
• Workers trained, and energy controlled during service

What OSHA looks for

• A written risk assessment
• Records of training and checks

What helps

Two site workers standing a few meters from an excavator working at night on a beach replenishment job.

What you are seeing

A stack of standards defines robot safety

124

Different machines follow different standards

126

Industrial arms follow the R15.06 standard

125

• Design and safeguarding of the robot and its cell
• Integration, with cybersecurity added in 2025

What it covers

• Makers and integrators
• Insurers and site safety teams

Who uses it

A yellow industrial robot arm working on a production line.

What you are seeing

Mobile robots now have a full U.S. standard series

128

Legged robots work in unstructured places

130

Legged robots are still waiting for a standard

129

• General machinery and mobile robot standards
• Maker safety guidance and site rules

What exists today

• A single standard for balance, falls and stopping
• Agreed test methods

What is missing

• Use a documented risk assessment
• Keep people clear of the machine's path
• Track the draft and plan to meet it

What to do now

• Inspection rounds and site capture
• Places wheeled robots cannot reach

Where they work

• Falls and balance loss near people
• How the machine stops safely

Safety questions

A four-legged robot with a sensor payload walking on a paved path.

What you are seeing

Four paths let a drone fly commercially

132

Part 109 would unlock long-range drone flights

134

Commercial drones are regulated as aircraft

133

• Inspection and mapping
• Delivery and agriculture

Common uses

• Pilot certification and waivers
• Insurance for hull, payload and liability

What enablers ask

A professional multirotor drone with cameras and a payload, on display.

What you are seeing

Federal and state AI policy is moving quickly

136

Voluntary frameworks carry weight in practice

137

• Guides the mapping, measuring and managing of AI risk
• Gives a common language for governance

NIST AI Risk Management Framework

• Organizes security into six functions
• Used in contracts and insurance questions

NIST Cybersecurity Framework 2.0

• Security for industrial automation
• Used for machine and network design

Sector rules add to the general rules

139

Third-party testing proves a standard is met

141

Test labs measure robots on standard courses

142

Scheme or lab

What it offers

Nationally Recognized Testing Laboratories

OSHA-recognized labs such as UL, Intertek, TÜV SÜD America and CSA test and list products

• A tracked response robot working through a NIST test apparatus: a ramp, a door handle, valves and reach targets.
• Each task is repeated and timed, so different robots can be compared on the same course.
• NIST and ASTM publish test methods of this kind for robot performance.

A safety case argues that the machine is safe here

144

Evidence grows with every week of operation

145

• Risk assessment
• Simulation results
• Training plan

Before the pilot

• Daily checks and logs
• Near-miss reports
• Corrective actions

During the pilot

• Summary of hours and incidents
• Updated risk assessment
• Plan for production

After the pilot

Rules to watch in the next 24 months

147

Each enabler group gets a different offer

150

Four ways to work together

152

The path: consulting, pilot, production

154

How to start

155

• Tell us which part of the deal you serve
• We map it to the gates a pilot must clear

1 Talk

• Pick a task, a site and the evidence you need
• Agree what a good result looks like

2 Scope

• Run it with trained people in the lead
• Receive the record your process asks for

3 Pilot

Where enabler markets are heading

157

Ten things to remember

159

A deal clears six gates.

Need, machines, money, cover, clearance and operation.

Four enabler groups decide.

Financiers, insurers, platforms and regulators.

Makers write most service contracts.

Financiers fund the fleets behind them.

Tax rules can favor buying.

Section 179 and bonus depreciation matter in 2026.

Silent AI is the insurance gap.

Check exclusions and who bears software risk.

Specialist cover is arriving.

Limits up to $25M per insured, with governance asked for.

Software is a stack.

Seven layers, open interfaces and a separate safety layer.

Safety standards just moved.

R15.06-2025, R15.08-3 and the pending ISO 25785-1.

Drone rules are still pending.

Part 108 is at the White House.

Operating proof opens every door.

Pilots that log safe operation unlock money, cover and approvals.

Go deeper in other primers

161

Primer 01 Robotics

• atomgigs.com/robots
• Primer 02 OEMs, Drones and VTOL
• atomgigs.com/oems
• Primer 13 Physical AI Technology
• atomgigs.com/technology

Machines

Primer 07 Data Centers

• atomgigs.com/data-center
• Primer 08 Manufacturing and Logistics
• atomgigs.com/manufacturing
• Primer 09 Construction and Infrastructure
• atomgigs.com/construction
• Primer 10 Public Safety and Defense
• atomgigs.com/public-safety
• Primer 11 Commercial Services
• atomgigs.com/commercial
• Primer 12 Physical AI Labs
• atomgigs.com/labs

Buyers

Primer 00 Value Proposition

• atomgigs.com
• Primer 03 Talent
• atomgigs.com/talent
• Primer 04 Investors
• atomgigs.com/investors
• Primer 06 Channel Partners
• atomgigs.com/channel-partners

Partners and people

What Atomgigs is

• Robots, drones, VTOL, IoT and automation, with people, as one hybrid workforce.
• Humans in the lead, physical AI in the loop.
• Not a machine builder: it sources from makers, then staffs, integrates, deploys and operates.

The sources

• Robots: every robot type, profiled in depth.
• Everything other than robots: drones, VTOL, IoT and automation, from many makers.
• The humans: role families across the trades, engineering and safety, every person a contractor.

Who we work with

• Investors: consulting before and after the deal.
• Enablers: financiers, insurers, platforms and regulators behind every deal.
• Channel partners: engineering, design, MEP and construction firms.

Buyer groups

• Data centers
• Manufacturing and logistics
• Construction and infrastructure
• Public safety and defense (no weapons)
• Commercial services
• Physical AI labs

How it works

• Consulting engagement, then a short pilot, then production.
• One scope, one site, one metric in the pilot.
• A small senior pod runs every site, with a named human manager for every robot.

How we are paid

• Pilot, managed crew, outcome-based or advisory.
• No capital outlay for machines and no long-term headcount.
• Pay for capacity and outcomes; scale up as results are proven.

Mavengigs Consulting

• Strategic M&A, transformation and optimization
• Focused on the part of M&A that begins after everyone shakes hands
• Built for PE-backed and Fortune 500 teams

Mavengigs Capital

• An independent sponsor platform
• Family office strategy and management

Mavengigs Ventures

• Funding and mentorship for founders we back
• Panmedical: digital health in India, run as CEO
• Atomgigs, Mavens Life and Mavens Health

Career and experience

• 30+ years of management consulting: managed major M&A deals totaling $20 billion; scaled 50+ companies for $100 million of impact
• Deep expertise in M&A consulting, including integrations and divestitures
• 2018 to present: Mavengigs: Consulting, Capital and Ventures
• 2004 to present: Founded Panvisage, CEO for 22 years
• 2024 to present: Panmedical CEO (digital health)
• 1998 to 2004: Oracle, led 14 enterprise programs
• 1994 to 1996: Rolled out nationwide cell networks
• 1992 to 1994: Car and truck design (CAD, CAM, CIM)
• 1991: B.E. Electrical and Electronics Engineering
• 1989 to 1991: Designed robots, AGVs and manufacturing transfer lines
• Data centers: built one from scratch in 2000; data center migration in 2008; Equinix in 2020
• E&C companies: WSP, Jacobs, Worley, Halliburton, Fluor, Brown & Root
• Managed the buildout of a manufacturing plant and a distribution center

Monty Pandey

• Founder and CEO, Atomgigs
• Founder and CEO, Mavengigs
• Co-founder and CEO, Panmedical

Not investment advice

Atomgigs provides consulting. We are not an investment advisor, so you make your own decisions on investments.

Products belong to their owners

Company, product and robot names, logos and photos are used as examples and belong to their owners. This deck is for information only and is not a contract or an offer.

About the numbers

Numbers come from publicly available sources and are the latest available at publication. Costs, prices and returns are typical ballpark values, not quotes; they will be updated for your specific engagement.

Glossary: money

172

Glossary: technology and rules

174

Glossary: insurance

173

Key numbers

176

Key rules and standards

177

Organizations

179

VDA and VDMA

German automotive and machinery associations behind VDA 5050 and OPC UA robotics work

Verisk (ISO unit)

Publisher of insurance forms, including AI exclusion forms