Sinew · business plan · NAVER D2SF

The force-data layer
for physical AI.

We recover contact, direction & force from ordinary video — and sell it as an API. The one source of force data that doesn't need a sensor.
Deepgram for touch software-only · no hardware picks-and-shovels for robot AI north-star: egocentric human data at scale
business plan
01 / 10
market

A huge market — with a force-shaped hole.

TAM
$38B · humanoid robots by 2035
SAM
$9.2B · robot data & labeling by 2033
SOM
force / haptic data — ~$0 sold today

Goldman Sachs (TAM) · Mordor/Grand View, ~21% CAGR (SAM). Force is the highest-value, lowest-supply slice.

  • The market prices data by the labeled hour (~$118/hr) — and nobody sells force as a product.
  • Full instrumented datasets cost $50–200K and can't be retrofit onto existing video.
  • ~$3B spent on robot data in 2 years — to manufacture what can't be scraped.
market
02 / 10
customers

Who pays — and why now.

01

Data factories

Scale AI ($29B), Mercor ($10B), Lightwheel. Now scaling egocentric human-manipulation capture (head-cams, glasses) — the cheap, massive future of training data. Force is the slice they can't capture; we add it without recapture.

02

Robot-FM labs

Physical Intelligence, Figure, NVIDIA, Skild. Force lifts their exact models (+23% over π0). They hold the budget & feel force-blindness most.

03

Research & verticals

Manufacturing, surgery, service robots. Contact-rich by definition; smaller deals, faster pilots, reference logos.

Demand is implied by the science (force improves policies) but unpriced — we validate willingness-to-pay with a paid pilot (see GTM).

customers
03 / 10
the product, as a business

Two ways to buy the same force layer.

Inference API

Stream in video → get back contact · direction · magnitude, frame by frame. Integrates into any data pipeline. Usage-metered.

real-time-friendlyno hardware to install

Recovered-haptic dataset (DaaS)

We mine un-instrumented manipulation video into a force-labeled corpus and license it. Monetizes the data flywheel directly.

bulk dealsgrows with every customer

We are a generator, not a broker — we manufacture a brand-new label class from video that already exists.

product
04 / 10
business model

Hybrid revenue — predictable floor, usage upside.

Subscription floor

Monthly platform fee per customer → recurring ARR, the metric investors underwrite.

Usage overage

Billed per frame, but metered on contact-events — value tracks recovered signal, not the ~78% dead frames.

Dataset licensing

DaaS corpus deals + enterprise on-prem licensing for the largest labs.

Mirrors the model buyers already understand (LLM token / per-minute audio APIs) while protecting gross margin against per-frame GPU COGS.

model
05 / 10
pricing & unit economics

Priced against the cost we erase.

  • Don't price vs hand-labeling — force can't be labeled from RGB by a human.
  • Anchor to instrumenting avoided: ~$118/hr teleop, $50–200K per dataset, often impossible to retrofit.
  • Target price $0.005–$0.05 / frame (≈ $5–$50 per 1,000 frames).
  • GPU COGS ≈ $0.0005 / frame (est., V-JEPA encode) → ~90% gross margin.

Comparables

companymodel
Deepgramper-minute speech API
ElevenLabs$11B val · ~$330M ARR · usage API
Specialized AI APIs$0.50–$5.00 / 1k calls

Per-frame is novel → expect buyer-education; gross margin modeled on real inference COGS.

pricing
06 / 10
competition & moat

The force-from-video lane is empty.

whoapproachvs Sinew
GelSight · Meta Digit · Sanctuarytactile hardwareneeds a sensor on the robot
NVIDIA Cosmos · Lightwheelsynthetic datasim, not real contact
"Feel the Force" (academia)tactile glove on humannot video; not a product
Sinewforce from existing videosoftware-only · no sensor

Moat

  • Harmonized multi-dataset pipeline — 16,772 rollouts, 7 embodiments.
  • Proven cross-camera / lab / robot generalization.
  • Data flywheel: every customer's video grows the corpus.
moat
07 / 10
go-to-market

Land a pilot → own the slice → flywheel.

01

Land

One paid contact-rich pilot with a data factory or FM lab → sets a reference price & proves WTP. Seed an open "force-recovered" sample for inbound.

02

Expand

Sell into the least-commoditized slice (force / contact / failure-recovery) the incumbents can't supply. Ride the privacy/regulation tailwind toward recovered data.

03

Compound

Each engagement feeds the corpus → better model → DaaS deals. North-star: the force layer for egocentric human-manipulation data at data-factory scale.

gtm
08 / 10
traction & roadmap

Feasibility done — now de-risk & sell.

contact accuracy
87%
+ generalizes across camera / lab / robot
force corpus
6.4M frames
≈14× Meta's haptic dataset

0–6 mo

De-risk the sim2real visual gap; ship the inference API; first design partners.

6–12 mo

First paid pilots → reference price; publish open sample; DaaS v1.

12–18 mo

Convert pilots to recurring; seed-round-ready revenue & margin.

Honest unknown investors will probe: the sim2real visual gap. We lead on contact + direction; magnitude & render-transfer are exactly what this stage funds.

roadmap
09 / 10
the ask · why NAVER D2SF

What we want — and why it matters.

What D2SF gives

  • ₩10M product/tech fund + ₩5M GPU/cloud
  • Investment linkage + tech-entrepreneur community
  • Office in Gangnam, Seoul

Why it moves the needle

  • Funds + GPU → de-risk sim2real, scale the model, run the first paid pilot
  • We're in Daejeon — far from the action. A Gangnam base puts us in the room to raise follow-on capital & meet big market players
  • Possibly collaborate with Naver or its partners, if there's mutual interest
ask · advised by Prof. Jee-Hwan Ryu, KAIST IRiS
10 / 10