Control returns to the plant
The furnace, forming and lehr systems hold their last known-good state. Nothing Glasent was doing is left half-done; writes are atomic and logged.
FAQ
The questions that actually come up in the first meeting.
Platform, deployment, safety, data and commercial, answered the way we would answer them on a call.
FAQ
Platform and deployment
Writes, failure, data and pilots.
Yes, but only within an explicit tag allow-list with per-tag rate and magnitude limits, and only at the autonomy level your site has set. Every pilot starts in shadow mode, where Glasent predicts and recommends and a person enters everything. Writes come later, after the recommendations have earned it.
Control returns to your existing furnace, forming and lehr systems at their last known-good state. Glasent is a supervisory layer on top of the control system you already run, never a replacement for it, so an outage degrades the plant to its current way of running, not to a stop.
Shadow mode starts on the first day from existing SCADA, forming, lehr and inspection data. Defect and stress prediction improve as site history and labelled outcomes accumulate; the pilot plan sets a baseline period before any recommendation is scored.
Only if you choose cloud training. Compositions, forming recipes and defect libraries are tenant-isolated and never used to train another customer's models. On-prem training and an air-gapped plant edge are available for IP-sensitive producers.
You are, the same as with any control strategy, which is why every write is policy-checked, bounded, logged and reversible, and why anything above your risk threshold waits for a named approver. The audit log records the request, the reasoning, the limits applied and the human decision.
A 90 to 120 day line pilot in three stages: shadow mode to measure the baseline, assist mode where a technologist approves each recommendation, then bounded write-back on low-risk forming, annealing or inspection loops if the plant is satisfied with the results.
FAQ
Commercial and fit
Tiers, discounts, outcome components and who is not a fit.
One agent module on one forming or annealing line at $16,000 a month: melting and forming control, annealing control or inspection AI, with the plant-edge node, the connectors for that line, the run log and ware genealogy.
When the first line has proved its number and you want the full loop: all six products, the additional coat-and-temper, quality, yield and takt scopes, the orchestrator and the twin across the site, at $95,000 a month.
For the highest-value workflows, yes. Outcome fees are tied to measured yield, cullet, defect, energy and first-pass-quality improvement against the baseline agreed before the pilot.
Annual prepay carries a discount in the 15 to 20 percent range, and multi-year enterprise agreements can ramp usage over time.
Studio and art glass shops with no continuous furnace or forming scope, sites unwilling to integrate furnace, forming or inspection systems under IP and security controls, and buyers who want a dashboard rather than a system that acts.
A historian or SCADA extract, the inspection data you already keep, the grade spec and tolerances, and one KPI you want moved: cullet, seeds and stones, energy per tonne, breakage or escaped-defect risk.
Run timeline · scenario
The run behind most answers
Every Glasent run is an ordered, inspectable sequence. This is scenario_fl2_01 on FL-2 · float line · clear soda-lime: Thickness change FL-2 · 6 mm to 4 mm clear float, residual stress inside spec, zero escaped seeds. It is a worked scenario that shows the shape of a run, not a measured customer result.
scenario_fl2_01
FL-2 · float line · clear soda-lime
scenario
SUCCEEDED
-
01
ingest.order
SUCCEEDED
0.9 s
Plant Orchestrator · Pulled the 4 mm clear float spec, optical-grade tolerances and the standing energy window from plant MES; locked the target envelope for the run.
-
02
twin.simulate
SUCCEEDED
41 s
Glastwin · Simulated 36 candidate transition recipes across furnace pull, tin-bath ribbon speed and lehr curve; ranked them on seed risk, residual stress and energy per tonne.
-
03
melt.pull
SUCCEEDED
6 m 20 s
Meltrix · Stepped furnace pull toward the new ribbon mass flow while holding melt temperature and fining; chemistry stayed inside the composition window.
-
04
form.ribbon
SUCCEEDED
4 m 05 s
Formeon · Raised ribbon speed and re-angled the top rollers to thin the ribbon toward 4 mm; thickness converged inside the design tolerance.
-
05
anneal.curve
SUCCEEDED
3 m 12 s
Anneon · Re-shaped the lehr cooling curve for the thinner, faster ribbon so residual stress stays inside spec at the higher speed.
-
06
inspect.ribbon
SUCCEEDED
continuous
Seedscan · 8 camera, optical and stress stations streaming; a seed cluster flagged at the ribbon edge and attributed to the pull transient, routed to cullet.
-
07
yield.balance
SUCCEEDED
2.4 s
Plant Orchestrator · Re-sequenced cut sizes so transition ribbon routes to cullet recovery and good ribbon to the highest-value open order.
-
08
approve.human
APPROVAL
48 s
Plant Orchestrator · The second pull step exceeded the site autonomy threshold. Held for the glass technologist on shift; approved and written to the audit log.
-
09
handle.stack
SUCCEEDED
1 m 10 s
Panebot · Re-planned pick and stack paths for the thinner panes; plates flagged by Seedscan diverted to cullet, good plates stacked to rack A3.
-
10
ware.qualify
SUCCEEDED
1 m 02 s
Plant Orchestrator · Lot released with full genealogy: batch, melt, forming, lehr curve, defect map, stress map and the technologist's approval.
Tool calls · scenario
The agent shows its work
Every tool invocation, argument and result is written to an immutable, human-readable log, and every reasoning step is expandable. Nothing about a run is hidden from the plant.
- 02:14:03mes.read_orders({ line: "FL-2", next: "CLR-4MM" })
- 02:14:03ok spec locked · 4.0 mm · optical grade · stress limit set
- 02:14:04twin.simulate({ candidates: 36, horizon: "22 min" })
- 02:14:45ok best recipe #19 · seed risk low · stress inside spec
- 02:14:46policy.evaluate({ recipe: 19, autonomy: "L3" })
- 02:14:46ok 11 writes permitted · 1 write requires human approval
- 02:14:47scada.write_setpoint({ tag: "F1.PULL_SP", step: 1 })
- 02:21:07ok pull step 1 settled · fining stable · chemistry in window
- 02:21:08float.write_ribbon({ speed: "+", rollers: "re-angle" })
- 02:25:13ok thickness converging to 4.0 mm · flatness in tolerance
- 02:25:14lehr.write_curve({ zones: 12, profile: "4mm-fast" })
- 02:28:26ok residual stress inside spec at new belt speed
- 02:28:27vision.stream({ stations: 8, model: "seedscan-v3" })
- 02:28:41seed cluster · ribbon edge · attributed to pull transient
- 02:28:42cuopt.sequence({ objective: "cullet+energy" })
- 02:28:44ok transition ribbon routed to cullet · good ribbon to order
- 02:28:45approval.request({ action: "pull_step_2" })
- 02:29:33ok approved by the technologist on shift · logged
- 02:31:02isaac.plan_path({ stack: "A3", thickness: "4.0 mm" })
- 02:32:12ok paths validated in Isaac Sim · flagged plates diverted
- 02:33:15mes.log_lot({ lot: "FL2-4MM", genealogy: "full" })
- 02:34:17ok scenario run complete · stress in spec · 0 escaped seeds
Reasoning trace
Plan, thought, action, observation, exactly as the orchestrator would record it.
GoalMove FL-2 from 6 mm to 4 mm clear float with residual stress i…
Move FL-2 from 6 mm to 4 mm clear float with residual stress inside spec and zero escaped seeds.
PlanSimulate the transition on the twin first. Lead with furnace p…
Simulate the transition on the twin first. Lead with furnace pull so the ribbon mass flow is right before the forming change, then thin the ribbon, then re-shape the lehr curve. Watch the ribbon continuously; hold the second pull step for a human.
ThoughtThe standing recipe changes pull and ribbon speed together. On…
The standing recipe changes pull and ribbon speed together. On the twin that produces a fining transient that seeds the ribbon edge. Sequencing the pull step ahead of the forming change and holding fining removes most of it.
Actiontwin.simulate(candidates=36) returned recipe #19: pull in two …
twin.simulate(candidates=36) returned recipe #19: pull in two steps, forming change after fining settles, lehr curve re-shaped before belt speed rises.
ObservationRecipe #19 scored lowest on seed risk with stress inside spec.…
Recipe #19 scored lowest on seed risk with stress inside spec. Two candidates scored lower on energy but pushed residual stress over the site limit and were discarded.
ActionExecute recipe #19 under autonomy level L3: eleven setpoint wr…
Execute recipe #19 under autonomy level L3: eleven setpoint writes permitted, the second pull step routed to the glass technologist.
ObservationSeed cluster at the ribbon edge at 02:28:41, attributed to the…
Seed cluster at the ribbon edge at 02:28:41, attributed to the pull transient. cuOpt routed that ribbon to cullet recovery; no flagged plate reached a customer stack.
OutcomeScenario run complete. Thickness at 4.0 mm, residual stress in…
Scenario run complete. Thickness at 4.0 mm, residual stress inside spec, one approval gate, full genealogy written to the lot record.
Autonomy
Four levels, set per agent and per tag
A plant does not go from manual to unattended in one step. Glasent makes the level explicit, auditable and reversible at any time, and the first release plan is shadow, then assist, then graduated autonomy.
| Level | What the agent does | What the person does | When |
|---|---|---|---|
| L1 · Shadow and advisory | Observes, predicts and recommends setpoints with its reasoning | Enters every change manually; a baseline is measured | Pilot weeks 1 to 3 |
| L2 · Assist | Proposes a write; it executes on approval | Approves each write in the review console | Pilot weeks 4 to 8 |
| L3 · Bounded | Writes inside tag, rate and magnitude limits on low-risk loops | Approves pull steps, grade releases and anything above threshold | Pilot week 9 onward |
| L4 · Unattended | Runs the approved envelope without prompting | Sets the envelope; reviews the shift record | Planned, after graduated autonomy proves out |
Guardrails
An agent that can move a furnace needs a leash
Glasent writes to production equipment. Every capability is scoped, every write is policy-checked, and every action is written to an append-only audit log the plant owns.
- Bounded action space. Each agent can only write to an explicit tag allow-list, inside per-tag rate and magnitude limits.
- Policy engine before every write. Autonomy level, shift, product, interlock state and operator presence are all evaluated before a setpoint moves.
- Human-in-the-loop gates. Anything above the site threshold, pull steps, grade releases, safety-adjacent moves, waits for a named approver.
- Immutable audit log. Append-only, hash-chained, exportable, and retained on the plant's own storage.
- Hard fallback. Loss of the edge node, the network or the model returns control to the furnace, forming and lehr systems' last known-good state.
- Tenant and IP isolation. Compositions, forming recipes and defect libraries never cross a customer boundary. On-prem deployment available.
Compliance posture
| Standard | Scope | Status |
|---|---|---|
| SOC 2 Type I | Cloud control plane | RUNNING Planned in the first six months |
| SOC 2 Type II | Cloud control plane | QUEUED Planned in months six to twelve |
| IEC 62443 | Plant-edge OT security | RUNNING Design-aligned |
| ISO 9001 / IATF 16949 | Quality and genealogy records | SUCCEEDED Record formats supported |
| Container and safety-glass standards | Stress and defect conformance records | SUCCEEDED Record formats supported |
Failure modes
What happens when things go wrong
The honest answers to the questions OT teams ask first.
The edge keeps running
Local inference and the policy engine run on the node. The control plane is not in the write path.
Limits still hold
A bad recommendation cannot exceed a tag limit, because the limit lives in the tool definition. Above threshold it waits for a person; below, it is bounded and reversible.
The run waits
There is no timeout that resolves an approval. The step holds until a named person decides, or the run is cancelled.
Pricing
Priced per line, per plant, per group
Land on one workflow with a measurable baseline. Expand by line, module and autonomy level. Outcome components available on yield, cullet, defects and energy.
Melting and forming control, annealing control or inspection AI for one line. For glass makers validating ROI on the wedge.
- One line, one agent module
- Plant-edge inference node included
- Furnace, forming, lehr or inspection connectors
- Run log, audit trail, ware genealogy
- Business-hours support
The whole loop: batch and melt, form and shape, anneal and stress, coat and temper, defect, quality and dimension, yield and takt, robotic handling and the twin.
- Meltrix, Formeon, Anneon, Seedscan, Panebot
- Glastwin twin and grade-change sequencing
- Coat-and-temper, quality, yield and takt scopes
- Orchestrator with site autonomy policy
- 24/7 support, quarterly model review
Multi-site glass groups standardising on Glasent, with custom composition, forming and tolerance models.
- Multi-site plant-edge fleet
- Custom composition, forming and tolerance models
- Group autonomy policy and approval chains
- On-prem and air-gapped options
- SLAs, SSO and a dedicated engineer
Integrations
It speaks plant, not cloud
Glasent reads and writes through the furnace, forming, lehr, inspection and MES systems already on the floor. No rip-and-replace, no parallel historian, no new HMI to learn.
Furnace SCADA and PLC, batch-plant weighing, redox and fining instruments
Setpoint reads and guarded writes over OPC UA and Modbus
Float-bath and IS-machine controls, gob-weight and timing systems
Gob, pull, ribbon speed and roller reads; guarded writes
Lehr zone controllers and belt drives
Zone temperature and curve reads; guarded writes
Camera, optical, thermal, polariscope and birefringence stations
Frames, stress maps, defect records, line-speed streams
Orders, grades, lots and ware genealogy
Spec and tolerance reads; genealogy and release writes
Time-series stores and lab information systems
Backfill, replay and lab chemistry
Robot cells, conveyors and stackers via NVIDIA Isaac
Pick, path and stack commands inside the safety envelope
SSO and RBAC via SAML or OIDC; NVIDIA Jetson Orin edge nodes
Named approvers, tag-level roles, sub-100 ms inference
Enterprise
Standardise autonomy across the group
One policy model, one audit trail, one benchmark across every line in every plant, with the composition and forming models kept private to each site.
- SSO and role-based access down to the tag level
- Group-wide autonomy policy with per-site override and approval chains
- Cross-plant benchmarking on cullet, seeds, energy per tonne and first-pass quality
- VPC, on-prem and air-gapped plant-edge deployment options
- Custom composition, forming and tolerance models per site
- Dedicated deployment engineer and quarterly model review per plant
From the plant floor
The problems we hear
No customer quotes yet; we are pre-launch. These are the three buyer personas the product is built for and the pain each one describes, in their own terms.
"The furnace has run for years on the same setpoints. The process never has. We find out a melt drifted when the cullet pile grows."
Plant / operations director · ICP persona
"I can tell you why a check appeared from the lehr curve and the gob weight. I cannot be at every line, and the people who could are retiring."
Glass technologist · ICP persona
"A missed seed is a reject. A missed stress fault is a pane that shatters in the field. I need genealogy on every piece, not a spot check."
Quality / reliability engineer · ICP persona