The mill readiness assessment
A 24-point checklist covering sensing, connectivity, data and change management before you attempt autonomy.
Resources
Useful whether or not you buy anything
Readiness checklists, benchmarks and an ROI model you can run yourself. If the answer is that your mill is not ready, we would rather you found out here.
Library
What is available
Right-first-time shade: an engineering account
How substrate state, water chemistry and exhaust control combine, and why static recipe cards cannot hold shade.
Mill-edge reference architecture
Edge appliance, camera placement, network segmentation and OT security for a 400-machine mill.
Autonomy policy template
A starting policy set for approval gates, setpoint envelopes and revert conditions per workflow.
Textile autonomy benchmark 2026
How we propose to baseline right-first-time, seconds, water and energy across mill archetypes, each mill against its own trailing year.
Re-dye cost model
Estimate the annual cost of your re-dye rate in dye, water, steam, machine hours and late shipments.
Readiness
Is your mill ready for autonomy?
| Gate | What good looks like | Common failure |
|---|---|---|
| Sensing | Inline measurement at the stage you want to control | Lab-only measurement, sampled hours later |
| Connectivity | Machine data reachable over OPC UA or vendor tags | Controllers isolated with no export path |
| Data history | 12 months of lot outcomes with shade and defect records | Paper records or a spreadsheet per shift |
| Process ownership | A named owner per setpoint who can grant autonomy | No one willing to authorise a change |
| Change management | Operators told what is happening and why | Autonomy introduced as a surveillance tool |
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.
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.
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.
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.
Use cases
What mills point Yarneon at first
Every deployment starts with one wedge workflow that pays for itself, then expands along the line.
Hold shade on the first dyeing
Substrate-aware recipe prediction plus closed-loop exhaust control, with the colourist approving only what deviates.
Catch defects at the machine
Line-scan and NIR vision classify slubs, holes, streaks, stains, floats, barré and skew at full line speed, mapped to roll position.
Stop end-breaks before they cascade
Evenness and draft telemetry predict end-break clusters on ring frames and adjust before the frame sheds packages.
Keep construction on spec
Warp and weft tension control across air-jet, rapier and circular-knit machines holds pick and course density inside tolerance.
Hit hand, width and shrinkage
Stenter chemistry, overfeed and curing profiles tuned per construction so finished goods pass dimensional-stability tests.
Band rolls by shade before cutting
Spectral scoring sorts rolls into shade bands so no garment is cut from two shades.
Sequence dye lots for water and takt
Shade-family aware sequencing under delivery and machine constraints cuts changeovers, water and steam.
Automate doffing and roll transport
Vision-guided manipulators and AMRs take the lifting out of the job that mills cannot staff.
Produce the buyer packet automatically
Roll genealogy, test methods and chemistry declarations assembled and signed at release.
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.
Guardrails
An agent earns the right to touch a machine
Nothing here is a global "AI on" switch. Autonomy is granted per setpoint, bounded by policy, reversible by any operator and logged permanently.
- Graduated autonomy
- Every workflow moves observe → assist → bounded auto-control. Autonomy ceilings are per-setpoint policy, not a global switch.
- Human-in-the-loop gates
- Approval gates fire on deviation thresholds, new substrates, or any action outside the authorised envelope. Approvals are attributed and time-stamped.
- Reversibility
- Every control action ships with a revert path and a last-known-good setpoint. Operators can take manual control at any moment, from the HMI or the panel.
- Immutable audit log
- Assurance-grade, append-only record of every observation, decision, tool call, approval and actuation, exportable for buyer and regulatory audit.
- Tenant isolation
- Recipes, construction libraries and roll data are isolated per tenant. No cross-tenant training. Permission-aware retrieval with enforced citations.
- Sandboxed tools
- Agents call machines through a typed, scoped tool layer with rate and range limits. There is no path from a model output to an unbounded PLC write.
Pricing
Land on one line. Expand across the mill.
Priced per line, per mill or per group, with outcome-based components on the workflows where the value is measurable.
Line
Mills validating ROI on a single wedge workflow.
- One agent workflow on one line, yarn/fabric control, dyeing, or inspection
- Mill-edge inference appliance and connector for that line
- Run timeline, tool-call log and immutable audit trail
- Assist mode with human approval on every control action
- Standard email and shared-channel support, 1 business-day response
Mill
Full loopScaling mills adopting the full perceive–decide–act loop.
- All nine agents: spin, weave/knit, dye, finish, defect, shade, yield/takt, robotics, conformance
- As-produced fabric digital twin with pre-run simulation
- Graduated autonomy with per-workflow approval policies and spend limits
- Mill-edge fleet management and offline fallback control
- Roll genealogy, buyer conformance packets and sustainability reporting
- Named mill engineer and quarterly outcome review
Enterprise
Integrated textile groups standardising on Yarneon.
- Multi-site deployment with group-level benchmarking
- Custom fibre, construction and shade models trained on your archive
- On-prem or private-VPC deployment via NVIDIA AI Enterprise
- SSO, SCIM, RBAC, data residency and signed SLAs
- Outcome-based commercial components on yield, seconds, re-dyes and utilities
- Dedicated solutions team and 24×7 support
Annual prepay saves 15 to 20% depending on term. Full detail, including outcome-based terms, on the pricing page.
Questions
What mills ask before they let an agent near a jet
Both, in that order. Every workflow starts in observe mode, graduates to assist (the agent proposes, a human commits), and only then to bounded auto-control on the specific setpoints you authorise. Autonomy ceilings, approval gates and spend limits are policy objects you own, and every action is reversible and logged.
The control loop lives at the mill edge. Inference, guardrails and the audit log run locally on the edge appliance, so a lost uplink degrades reporting and retraining, not machine control. Buffered telemetry syncs when the link returns.
Design partners start with one wedge line in shadow mode: connectors and telemetry first, then models scored against what the mill actually produced, then assist mode where the agent proposes and an engineer approves. Whole-mill rollout follows once the wedge holds its numbers. Durations depend on how instrumented the mill already is, so we quote them after the assessment, not before.
To predict recipes well, yes, and that IP never leaves your tenant. Retrieval is multi-tenant isolated, models fine-tuned on your data are yours, and on-prem deployment is available for groups that will not put recipes in any cloud.
A Jetson-class edge device per inspection or control cell, one GPU server per mill for multi-model serving, line-scan cameras where in-line inspection is in scope, and a spectrophotometer feed for colour work. We survey the existing fleet first, most mills already have more sensing than they use.
Run the readiness assessment with us
Twenty-four questions, one hour, and an honest answer about whether autonomy is the next thing you should buy.
