Industries · Robotics
Lead generation for robotics companies that make hands and grippers
Read the research ↓By Shreyan Rajesh, co-founder of Mason · Updated 9 November 2026
The short answer
We studied 22 robot makers and integrators that bought a hand or gripper and compared the year they bought with the year before. Three things rose: robot software going in (13 of 20, against 7 of 19), a new production or data team, and first people in a new country. A product launch on its own did not.
For hand and gripper makers
Who this is for
Companies that make robotic hands, grippers and end effectors, and sell them to robot makers, integrators and teams building machines that handle real objects. You know who builds robots. The hard part is knowing which programme is about to choose its next hand.
This page answers that with research we ran on real purchases, including the signals that failed. If you want the method first, here is how we research a market.
The market
What we see in this market
Many robot makers build their own hands
18 of the 179 companies we found were robot makers with hands of their own. They are not buyers, however well they fit on paper.
Few public records survive a read
519 public customer records became 228 with a date, 159 different companies, 39 that look like buyers and 22 deals whose evidence held up when every source was read.
Maker websites are a slow route
A model sorted 3,197 page addresses on hand and gripper makers' sites and flagged 32 as possible customer evidence. We never finished reading them, so this route gave us no confirmed customer.
The look-back
What came before a robot maker bought a hand
For each maker we looked at the year it bought and the year before. Not every source reached every maker, so each event is counted over the makers we could see that year. An event that is just as common in the earlier year describes the kind of maker that buys. It does not tell you when.
First people in a new country.
Robot software goes in, sometimes for learning from demonstration.
A production, field-service or data team is set up.
A new robot goes out, and the hand on it is chosen.
Bars show the share of makers a source reached. The shaded rows rose by 15 points or more in the year they bought.
These are typical lead times from small counts: 1 to 7.5 months depending on the event. Read them as a range, not a schedule.
Signals
Signals that held up, and signals that did not
A product launch on its own
A new robot or product line went out at 11 of 17 makers in the year they bought, but at 7 of 10 the year before. Active makers launch every year.
Opening the robot to outside hardware
An SDK, open hardware or a partner portal: 3 of 21 against 3 of 19. Flat.
Plant capital, a hand failing, a departing integrator
Capital for a new line was 1 of 15 in both years. The engineer who integrated the hand left at 1 of 16 in both years. A hand failed in public at 1 of 12.
Manual handling breaking at the plant
At end-user factories it was 6 of 13 in the window and 8 of 11 the year before. It is a standing condition, not a trigger.
Robot software goes in · 13 of 20, against 7 of 19
Simulation, robot-learning stacks, a new SDK. Demonstration and teleoperation data looked like the sharpest part, but we could not confirm the count behind it. A robot that learns from a person needs a hand that can do what the person did.
A new team is set up · 6 of 17, against 1 of 14
Production, field service or data collection. The clearest rise, but soft: 4 of the 6 are a single hire or a line run by a partner.
First people in a new country · 6 of 17, against 3 of 17
The earliest of the three, about 7.5 months out. 5 of the 6 rest on a first start month on LinkedIn, with no public link.
The hand changes on a model · 4 of 11 launches, against 1 of 7
Inside the launch signal, what separates the buying year is a hand or gripper changing on a model. The numbers are small, so read it as a lead to check.
Each reading is ours, not a promise. Why software or a new team matters is our inference from how these programmes work; the counts are what we measured.
In practice
What is working, to an extent
Look where hand deals are recorded
Procurement notices, contract awards and prospectuses gave us 72 finds, and 69 of them were deals with exact dates and prices. It was the densest source in the whole run.
Broad search found far more, 519 deals, but a random check of 40 found 6 wrong and 5 duplicates, and 284 of the 519 had no date at all.
Read every record before counting it
The errors only reading catches: an integrator recorded as the buyer, an early discussion recorded as a deal, a page that never mentions a hand. Two deals were dropped after every source behind them was read.
Searching in one language misses makers whose customers are announced in another.
The limits, plainly
- This is 22 deals, and each event is counted over the makers a source reached, so the counts are small.
- The comparison is each maker's own earlier year. We had no group of similar makers that did not buy. So this shows what was common before a purchase. It does not prove an event predicts one.
- We have not yet measured replies or meetings from these signals. When we do, this page gets updated.
Checklist
If you want to try this yourself
How we found the makers in this market, and the buyers behind them: the robotics case study. The same method in four other markets: all case studies.
Watch for robot software going in: simulation, robot-learning tools or a new SDK.
Watch for a new production, field-service or data team, and check whether it is one hire or a real function.
Watch for first people in a new country.
When a robot launches, check whether the hand on it changed. A launch alone happens every year.
Look for deals in procurement notices, contract awards and prospectuses, and read every record before you count it.
Your market. Your past deals.
Want this research run on your own market, with your own past deals?