Ethan Mitchell
Ethan owns the mill relationships, the pilot programme and the commercial model, starting with the dyehouses and weaving sheds where shade misses and re-dyes cost the most.
About
Textiles deserve the same autonomy as semiconductors
Fabric is one of the largest manufacturing sectors on earth and one of the least instrumented. We think that is the opportunity, not an accident.
Leadership
Who is building Yarneon
Yarneon is led by its co-founders, Ethan Mitchell (CEO) and Ryan Parker (CTO).
Ryan Parker
Ryan owns the platform: mill-edge perception and control, the OT connectors, model serving on the NVIDIA stack, and the audit architecture behind every Yarneon agent.
Why now
Three things changed at once
01
Sensing got cheap
Line-scan cameras, NIR and inline spectrophotometry are now affordable at mill scale rather than lab scale.
02
Edge compute got real
A ruggedised GPU appliance can run segmentation at loom and stenter speed inside a dusty weaving shed.
03
The craft is retiring
The colourists and weaving masters who hold the tacit knowledge are leaving faster than mills can replace them.
The problem
Textiles clothe the world and still run on craft, spreadsheets and hope
Fabric is made at enormous scale and inspected at human speed. That gap is where the industry’s waste lives.
Making fabric means spinning fibre to a consistent count and twist, weaving or knitting to exact construction, dyeing and finishing to exact shade, fastness and hand, and inspecting for slubs, holes, streaks, stains, barré, skew and width faults, all to buyer spec, fully traceable.
It is unforgiving. A yarn drift, a loom fault, a dye-recipe error or a finishing defect turns into seconds, reprocessing, shade rejects and buyer chargebacks. Dyeing and finishing are craft-bound and enormously water- and energy-hungry. And the whole mill is paced by a colourist and operator workforce that is shrinking faster than it is being replaced.
Most mill managers run blind on real-time yarn quality, fabric defects, shade, chemistry, waste and roll genealogy. The work is high-volume, tolerance-bound, resource-intensive and rate-constrained, which is the exact profile autonomy is for.
Seconds
Found after the roll is made
Slubs, holes, streaks and shade misses are caught at the inspection table, not while the fabric is being made.
Re-dyes
Shade missed at drain
A standing recipe card against many greige suppliers and seasonal water. The miss is paid in water, steam and time.
Water and energy
Per kilogram, unmeasured
Most mills know the monthly bill and not the litres per kilogram per shade family.
Audits
Genealogy by hand
A roll’s history reconstructed from spreadsheets and paper lab dips every time a buyer asks.
And the constraint underneath
The craft is retiring
A senior colourist can look at a bath and know it is running hot. There are fewer of them every year, and no mill has found a way to hire the difference.
What we believe
Four opinions we build on
Belief
Autonomy must be bounded
A system that cannot be refused, reverted and audited will never be trusted with a machine, and should not be.
Belief
Evidence beats assurance
Buyers do not want a promise about your process. They want the record of what you produced.
Belief
Craft is data
Every colourist override is a labelled example. Capture it and the mill compounds instead of decaying.
Belief
Physics before language
A fabric line is a control problem. Language models help humans read it; they do not run it.
Belief
Deploy where the machine is
Autonomy that depends on a stable internet link is not autonomy in a mill.
Belief
Say the aspirational parts out loud
Anything not yet in production is labelled as such, on this site and in every deck.
Trust signals
What the runs actually produce
No customer results are published yet. These are platform facts and plan terms from our own documents. Outcomes will be measured against each mill’s own trailing twelve months, in shadow mode before anything is controlled.
9
Mill agents, one per stage, from spinning to release.
6
Products in the loop: Vireon, Spindra, Loomix, Shadeon, Doffex and Twinly.
3 to 5
Design partners we are onboarding first, one wedge workflow each.
$14k
Per line per month on the Line plan. Mill and Enterprise sit above it.
Items marked ASPIRATIONAL are design targets ahead of production validation.
Agent graph
One run, ten steps, one human decision
This is the shape of a dye lot on Yarneon, shown as an illustrative scenario rather than a customer run. Ingest and simulation fan out, converge on a colourist approval gate, then dosing, verification, finishing, inspection and release. The accent traces the active path; every node opens in the inspector.
Scroll the graph sideways · or open the text equivalent below
SUCCEEDED APPROVAL FAILED RUNNING
Text equivalent, workflow steps, dependencies and status
| Step | Stage | Depends on | Status | Duration |
|---|---|---|---|---|
| ingest | conformance | none | SUCCEEDED | 0.8s |
| twin | simulate | ingest | SUCCEEDED | 46s |
| recipe | dye + colour | ingest | SUCCEEDED | 2.4s |
| approve | human gate | twin, recipe | APPROVAL | 4m 12s |
| dose | bath control | approve | SUCCEEDED | 118m |
| shade | verify ΔE | dose | SUCCEEDED | 9.1s |
| finish | stenter | dose | SUCCEEDED | 64m |
| inspect | vision | shade, finish | FAILED | 38m |
| rework | replan | inspect | SUCCEEDED | 1.6s |
| grade | release | rework | SUCCEEDED | 3.0s |
- Parallel branches2twin simulation and recipe prediction run together
- Human gates1colourist approval, 15 minutes, attributed
- Failures recovered1weft streak on roll 09, replanned in-run
Run timeline
RUN-4471 · Indigo poplin, 3,180 kg, due Thursday
The same scenario run as an ordered timeline: step, status, duration and the tool result behind it. Inspection failed on one roll, the run recovered, replanned the cut and still held the ship date.
RUN-4471
RUN COMPLETE
Scenario mill · reactive exhaust jet dyeing · illustrative run, not a customer deployment
Goal: Dye lot DL-4471 · 3,180 kg · 40s combed cotton poplin · shade Indigo 19-4028 TCX · ΔE ≤ 0.8 · due Thu 06:00
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01 ingest.lot_state SUCCEEDED 0.8s
Pulls greige genealogy for 14 rolls: yarn lots, loom, greige GSM 138, absorbency and residual size from the prep line.
Agent, input and output
agent Quality-and-Conformance result ← 14 rolls · 3,180 kg · mean GSM 138.2 (σ 1.1) · prep OK -
02 twin.simulate_lot SUCCEEDED 46s
Omniverse fabric-line twin simulates the exhaust curve, machine loading and takt for three candidate recipes before a drop of dye is used.
Agent, input and output
agent Yield-and-Takt result ← 3 candidates scored · best predicted ΔE 0.52 · 118 min cycle -
03 dye.predict_recipe SUCCEEDED 2.4s
Spectral recipe model proposes a 3-dye combination against the buyer standard, corrected for this substrate’s absorbency and the current water hardness.
Agent, input and output
agent Dye-and-Color result ← 2.14% Navy RGB · 0.61% Blue BRF · 0.08% Black B · salt 62 g/L -
04 human.approve_recipe APPROVAL 4m 12s
Recipe deviates >5% from the standing card, so autonomy is capped: the colourist approves, rejects or edits before any dosing. Guardrail policy MILL-02/DYE-APPROVAL.
Agent, input and output
agent Dye-and-Color result → awaiting colourist · approved on shift 04:18 -
05 dye.control_bath SUCCEEDED 118m
Closed-loop dosing and ramp control against live bath spectrophotometry; two mid-cycle corrections applied when exhaustion ran 4% ahead of model.
Agent, input and output
agent Dye-and-Color result ← exhaust 96.4% · 2 corrections · bath ΔE 0.41 at drain -
06 shade.verify_lot SUCCEEDED 9.1s
Per-roll spectral read against Indigo 19-4028 TCX under D65/TL84/A; metamerism scored and rolls sorted into shade bands.
Agent, input and output
agent Shade-and-Quality result ← mean ΔE 0.58 · 13 rolls band A · 1 roll band B -
07 finish.control_stenter SUCCEEDED 64m
Stenter chemistry, overfeed and curing profile set to hold hand, 148 cm width and residual shrinkage under 3%.
Agent, input and output
agent Finish-and-Hand result ← width 148.3 cm · shrink 2.1% warp / 1.4% weft -
08 inspect.scan_rolls FAILED 38m
In-line vision flags a recurring 6 cm weft streak on roll 09 between 412 m and 445 m, traced to a stenter pin-chain slip, not a dye fault.
Agent, input and output
agent Defect-and-Inspect result ← 1 defect cluster · roll 09 · 33 m affected · class WEFT_STREAK -
09 plan.rework_route SUCCEEDED 1.6s
cuOpt re-plans the cut and rework route: 33 m diverted to seconds, remaining 14,890 m re-sequenced so the buyer’s Thursday ship date still holds.
Agent, input and output
agent Yield-and-Takt result ← 33 m to seconds (0.22%) · ship date held -
10 quality.grade_and_release SUCCEEDED 3.0s
Four-point grading, roll genealogy and the conformance packet (AATCC 61, ISO 105-C06, ZDHC MRSL declaration) are assembled and released to the buyer portal.
Agent, input and output
agent Quality-and-Conformance result ← 13 rolls Grade A · 1 roll Grade B · packet signed
RUN OUTCOME
Lot released first time, on shade, on date.
| Measure | Value | Context |
|---|---|---|
| Final mean ΔE | 0.58 | against a 0.80 tolerance |
| Re-dyes avoided | 1 | the standing card would have missed shade |
| Seconds | 0.22% | versus the scenario mill baseline of 1.9% |
| Water used | 46 L/kg | versus 59 L/kg on the scenario recipe card |
| Human decisions | 1 | the recipe approval gate |
Why this matters
A mill does not fail on the good lots. It fails on the lot where the substrate drifted, the recipe card was wrong, and nobody found out until the buyer opened the carton. Yarneon makes that lot legible while it is still in the machine.
Design-partner programme
What a design partnership involves
Three to five mills, one wedge workflow each, shadow mode first. The sequence comes from our MVP plan and does not change for anyone.
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Phase 1
Instrument
The wedge workflow is connected in shadow mode and the baseline for seconds, re-dyes and waste is measured from the mill’s own data.
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Phase 2
Assist
The agent recommends yarn, construction and recipe moves and predicts defects and shade. An engineer approves each one, and accuracy is scored in the open.
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Phase 3
Graduate
Low-risk inspection, dye and finishing loops move to bounded auto-control one setpoint at a time. Exceptions escalate to a named person.
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Phase 4
Expand
Adjacent modules are added within the same mill once the wedge holds its numbers. Publication of results is the mill’s decision.
Accelerated computing
Physical AI needs real compute, at the machine
Line-speed vision, spectral colour science and a fabric-line twin are GPU-essential workloads. Yarneon runs them at the mill edge and trains them centrally.
| Component | Role in the mill |
|---|---|
| Isaac + Jetson | Robotic roll, beam and doffing handling, plus mill-edge inference on inspection and control cells. |
| Metropolis / DeepStream + TensorRT | Line-speed fabric-defect vision pipelines targeting sub-100 ms per-frame decisions. ASPIRATIONAL |
| Holoscan | Sensor fusion across cameras, spectrophotometers, probes, PLC events and meters into one explainable state stream. |
| Omniverse + Replicator / Cosmos | The as-produced fabric twin, and synthetic generation of rare defect, barré and shade-variation cases. |
| cuOpt | Dye-lot sequencing, rework queues, machine assignment and water/energy allocation under hard constraints. |
| Triton + NIM + NeMo | Multi-model serving at the mill edge, plus grounded textile-process and colour reasoning with citations. |
| NVIDIA AI Enterprise | Private and on-prem deployment for groups protecting dye recipes and construction IP. |
Items marked ASPIRATIONAL describe target architecture ahead of full production validation.
The market
Why this is a large business
| Measure | Value | Basis |
|---|---|---|
| TAM | $17B | Global software, automation and quality spend addressable by mill autonomy. |
| SAM | $4.2B | Mills of sufficient scale and instrumentation to deploy within five years. |
| SOM | $250M | Realistic capture in target geographies over the plan horizon. |
| Typical ACV | $650k–$8.5M | Enterprise group contracts across multiple sites. |
Deployment
From first connector to bounded control, one phase at a time
No mill hands over a jet on day one. The path is deliberately slow at the start and faster once the numbers hold. Durations are quoted after the assessment, because they depend on how instrumented the mill already is.
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Phase 1
Connect
Mill survey, edge appliance install, connectors to machines, MES and meters. Telemetry starts flowing; nothing is controlled.
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Phase 2
Observe
Models run in shadow against live production. Predictions are scored against what the mill actually produced, on your data.
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Phase 3
Assist
The agent proposes; a human commits. Approval gates, setpoint envelopes and revert paths are agreed with the process owners.
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Phase 4
Control
Bounded auto-control on the setpoints you authorise, one at a time, with autonomy ceilings that you can lower at any moment.
Partners
We do not do this alone
Machine builders
Loom, knitting, jet and stenter OEMs embedding Yarneon control alongside their own HMIs.
Systems integrators
Automation integrators deploying mill-edge appliances, cameras and robotics on the floor.
Colour & lab vendors
Spectrophotometer and colour-library vendors whose data makes recipe prediction sharper.
Compute & silicon
NVIDIA Inception and accelerated-computing partners for edge, training and simulation.
Sustainability programmes
ZDHC, Higg and OEKO-TEX aligned programmes consuming Yarneon utility and chemistry data.
Buyers & brands
Apparel and home brands who want the conformance record their mills cannot produce today.
Come build the mill layer
We are hiring controls engineers, vision researchers and people who have actually run a dyehouse.
