The orchestrator plans, the agents act
A run starts as a goal, becomes a plan, and executes as a graph of typed steps. The active path is always visible, always logged, always reversible.
RUNNING APPROVAL SUCCEEDED
Physical AI · Glass manufacturing autonomy
The glass plant runs itself. You decide how far.
Glasent is an agent operating system for glass manufacturing. Six products control batching and furnace melting, float and IS-machine forming, lehr annealing and robotic handling, watched by seed, stone, cord and stress inspection at line speed, rehearsed on an as-formed glass twin, and bounded by a policy engine you control.
Scenario: Thickness change FL-2 · 6 mm to 4 mm clear float, residual stress inside spec, zero escaped seeds. A worked example of a run, not a customer result.
Built for
Flat & architectural
Automotive glazing
Container glass
Display & solar substrates
Specialty & fiber glass
Five glass segments, one plant-edge platform. Design partners wanted in each; none are named here until a pilot is signed.
Run timeline · scenario
One scenario run, step by step
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.
How it works
A goal becomes a plan becomes a run
Nothing about a Glasent run is implicit. The goal is declared, the plan is simulated on the twin, each step is typed, and the outcome is written to the ware record.
Autonomy is a dial, not a switch
Shadow, assist, bounded, then unattended inside an envelope. Each site sets the level per agent, per tag, per shift, and every write above the threshold waits for a named glass technologist.
Agent roster
Six products and an orchestrator
Each product owns a section of the glass plant, a bounded tool set and a measured outcome. They negotiate for shared actuators through the orchestrator, never directly.
Meltrix · Batch-and-Melt
Batching, furnace melting, pull rate, melt chemistry
Autonomous batching and furnace melting, held on chemistry every second. Real-time batch, melt-chemistry and homogeneity modelling with closed-loop furnace control.
scada.read_furnacelab.read_chemistryscada.write_setpoint
Watches: Furnace zone temperatures, pull rate, batch weights, redox and fining signals, lab chemistry, Seedscan seed and stone rate
Formeon · Form-and-Shape
Float-bath and IS-machine forming, gob weight, dimension
Autonomous float-bath and IS-machine forming, dialled to exact dimension. Real-time gob-weight, pull-rate and dimension modelling with closed-loop forming control.
is.read_gobdim.read_thicknessfloat.write_ribbon
Watches: Gob weight and temperature, ribbon speed, top-roller angle, tin-bath conditions, thickness and flatness scans, Seedscan check risk
Anneon · Anneal-and-Stress
Lehr zones, cooling curve, residual stress
Autonomous lehr annealing that relieves stress before it becomes breakage. Real-time lehr-zone and residual-stress modelling with closed-loop cooling control.
lehr.read_zonesoptic.read_birefringencelehr.write_curve
Watches: Lehr zone temperatures, belt speed, cooling rate, birefringence and polariscope readings, product thickness from Formeon
Seedscan · Defect-and-Inspect
Seeds, stones, cords, checks, stress at line speed
See every seed, stone, cord and stress before it escapes the plant. Multi-sensor fusion of vision, optical, thermal and stress or birefringence at the edge.
vision.streamoptic.stress_mapalarm.raise
Watches: Every camera, optical, thermal and stress station on the line, at 30 to 60 frames a second
Panebot · Robot-and-Handling
Pick, transfer, orient and stack hot, fragile ware
Autonomous handling of hot, fragile glass: panes, ware and stacks. Vision-guided robotic pick, transfer, orient and stack for panes, ware and substrates.
isaac.plan_pathrobot.gripwms.route
Watches: Cell cameras, ware geometry and temperature, Seedscan defect flags per piece, robot and conveyor state
Glastwin · Glass-line twin
Simulate melt, form and anneal; optimise before the run
Hit target quality, stress and takt before the run, in simulation first. GPU-accelerated furnace-CFD, glass-flow and annealing-stress simulation of the full chain.
twin.simulatecuopt.sequencepolicy.replay
Watches: The as-run state of the furnace, forming line and lehr, plus every candidate recipe the orchestrator proposes
Plant Orchestrator
Planning, arbitration, human approval
Plans the run, arbitrates between agents competing for the same actuator, enforces the autonomy level and routes anything above the risk threshold to a glass technologist.
plan.composepolicy.evaluateapproval.request
100% of writes policy-checked
Where it pays
Four loops, four numbers the plant already counts
Glasent is not a dashboard. Each agent closes a loop that a plant already measures in cullet, megawatt-hours, rejects and minutes. The tables say what is watched and what is moved; the pilot measures the result.
Seeds, stones and cords
Catch the melt fault before it becomes cullet
Seedscan fuses camera, optical and thermal streams at the edge into a rolling seed, stone and cord rate. When the rate moves, Meltrix has time to adjust batching, fining or pull instead of waiting for the lab or the cullet pile.
- 4 to 24 stations per line, sub-100 ms alerts as the design target
- Seed clusters attributed to the pull or batch transient that caused them
- Flagged ware diverted before it reaches a customer stack
agent.defect_inspect SUCCEEDED
| Signal | Watched | Moved |
|---|---|---|
| Seed and stone rate | Per station, per minute | Batch dosing, fining, pull |
| Cord and inclusion flags | Per ware | Melt temperature window |
| Attribution | Melt, forming or lehr | Which agent acts |
Energy per tonne
Stop paying for melt margin you do not need
Melting is the most energy- and carbon-intensive step in the plant and it is almost always run with margin. Meltrix holds chemistry and homogeneity against live signals instead of a fixed furnace recipe, and takes the margin back inside the composition window.
- Furnace zone and pull control against live chemistry
- Energy window respected as a hard constraint
- Every move validated in Glastwin first
agent.batch_melt SUCCEEDED
| Signal | Watched | Moved |
|---|---|---|
| Furnace zone temperatures | Per zone, continuous | Zone setpoints inside limits |
| Pull rate | Per minute | Stepped pull changes |
| Energy per tonne | Per shift | Reported against the baseline |
Grade and thickness changes
Rehearse the transition on the twin
Every grade or thickness change is simulated on the as-formed glass twin before a single setpoint moves. Candidates that save energy but push residual stress or seed risk over the site limit are discarded automatically.
- 10 to 100 candidate recipes per transition
- Ranked on seed risk, residual stress, energy and takt
- The chosen recipe executed step by step and logged
agent.twin SUCCEEDED
| Signal | Watched | Moved |
|---|---|---|
| Candidate recipes | Per transition | Ranked and filtered |
| Predicted stress and seed risk | Per candidate | Discard above limit |
| Chosen recipe | One per run | Handed to Meltrix, Formeon, Anneon |
Residual stress
Hold the stress spec, not just the cooling profile
Breakage and field escapes come from stress nobody measured. Anneon reads birefringence and lehr zones continuously and re-shapes the cooling curve for the product actually on the belt, so stress stays inside spec through speed and thickness changes.
- Birefringence and polariscope fusion at the lehr exit
- Cooling curve adapted per product, not per campaign
- Stress record written to every ware's genealogy
agent.anneal_stress SUCCEEDED
| Signal | Watched | Moved |
|---|---|---|
| Residual stress | Per ware | Cooling curve, zone setpoints |
| Lehr zone temperatures | Per zone | Within the stress envelope |
| Breakage risk | Rolling estimate | Escalate when it rises |
Multi-agent handoff
Agents negotiate, they do not collide
Two agents will want the same actuator. The orchestrator arbitrates on the run goal, not on who asked first, and the handoff is logged like any other step.
Contested move: furnace pull rate
-
01form_shape.request(pull hold) SUCCEEDED0.2 s
Formeon wants pull held while the ribbon thins, to protect thickness convergence.
-
02batch_melt.request(pull step) SUCCEEDED0.2 s
Meltrix wants the second pull step now, to settle fining before the seed rate climbs.
-
03orchestrator.arbitrate SUCCEEDED0.4 s
Seed risk outranks a short thickness excursion under the run goal "zero escaped seeds". Meltrix wins the actuator, and because the step is above threshold it goes to the technologist.
-
04form_shape.handoff(returned) SUCCEEDED90 s
Actuator returned; Formeon recovers thickness with roller angle instead. Both requests, the score and the reason are in the run record.
Arbitration rules
- Run goal first. Every request is scored against the declared goal, not the requesting agent's local objective.
- Safety and escaped defects outrank throughput. A stress fault in the field costs a recall; a thickness excursion costs minutes of ribbon.
- Time-boxed ownership. An agent holds a contested actuator for a bounded window, then must re-justify.
- Everything is logged. The losing request, the score and the reason all appear in the run record.
- Deadlocks escalate to a person rather than resolving by timeout.
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 |
Measured
Design targets, stated as targets
These are the numbers the architecture is built to hit and the pilot is built to measure. None is a customer result yet; every pilot report reproduces its figures from the plant's own ware genealogy.
90–120 days
Paid line pilot, shadow to assist to bounded writes
3–5
Design-partner plants in the first cohort
50k–250k
Synthetic rare-defect scenes per glass family · planned
1,500°C
Where the process starts, in furnaces that run for a decade
Use cases
Where plants start
Most sites pick one of these wedges, prove the number, then expand line by line.
Flat & architectural SUCCEEDED
Float ribbon thickness, flatness and stress
Float lines live on thickness, flatness and the lehr curve. Glasent starts on ribbon dimension and residual stress, where off-spec ribbon and breakage at the cutting line are measured every shift.
Container glass SUCCEEDED
Gob weight, checks and line speed
IS machines form thousands of pieces an hour. Formeon and Seedscan start on gob weight and check detection, with the lehr curve following, where cullet and rejects are counted per section.
Display & solar substrates SUCCEEDED
Seeds, inclusions and optical grade
Substrate glass is unforgiving on seeds, inclusions and cord. Seedscan and Meltrix start on melt homogeneity and defect attribution, where yield to optical grade is the number.
Specialty & fiber glass SUCCEEDED
Composition windows and energy
New compositions and short campaigns make every unproven change expensive. Glastwin and Meltrix start on simulation-first grade changes and energy per tonne.
Accelerated computing
Physical AI, at the furnace
A float ribbon never stops and an IS machine forms thousands of pieces an hour. Perception has to be local, deterministic and fast, so Glasent runs GPU inference at the plant edge and keeps training, simulation and optimisation on DGX, HGX and OVX.
Edge perception
4 to 24 synchronised camera, optical, thermal and stress stations per line. Sub-100 ms defect alerts on container lines and sub-500 ms fused stress and flatness decisions on flat glass are the design targets.
Model serving
Defect, stress, time-series drift and process-risk models served across edge and plant servers, with glass-knowledge and reasoning endpoints packaged as NIM services.
Training
Multimodal models fine-tuned on inspection imagery, optical and stress outputs, PLC time series, recipes, gob weight, furnace temperature, pull rate, lehr curves, energy, cullet and final grade. Planned cadence: monthly plant refreshes.
Digital twin
GPU-accelerated furnace CFD, glass-flow and annealing-stress simulation of the as-run line, with 10 to 100 candidate recipes evaluated per grade change.
Synthetic data
50,000 to 250,000 rare seed, stone, cord, inclusion, check and stress scenes per glass family, always validated against real inspection distributions before training use.
Optimisation
Pull-rate constraints, energy windows, cullet routing, forming and annealing sequence and line takt, with RAPIDS for high-volume telemetry ETL.
Developers
Define an agent, bound it, run it
The Glasent SDK is typed Python. Tools are declared with schemas and limits; the policy engine enforces them at call time, not in a review meeting.
What you get
- Typed tool definitions with unit-aware ranges and rate limits
- Deterministic replay of any historical run against a new model
- Local twin harness so a recipe is simulated before it is shipped
- Autonomy policy as code, versioned and reviewed like any other change
# Bound the anneal agent to twelve lehr zones on FL-2.
from glasent import Agent, Tool, Limit, Autonomy
lehr = Tool(
name="lehr.write_curve",
tags=["FL2.LEHR.Z01..Z12.TEMP_SP"],
limits=[Limit(max_step="4 C", per="60s")],
)
anneal = Agent(
id="agent.anneal_stress",
goal="residual stress inside spec, min energy",
tools=[lehr, Tool("optic.read_birefringence", read_only=True)],
# bounded writes; a technologist still gates pull steps
autonomy=Autonomy.L3,
# simulate on Glastwin before every write
verify="twin",
)
run = anneal.start(line="FL-2", product="CLR-4MM")
for step in run.stream():
print(step.name, step.status, step.duration)
CLI
Start a run from anywhere
The same run engine, the same policy checks, the same audit trail, from the terminal, the review console or the SDK.
$ glasent run "thickness change FL-2 to 4 mm" --autonomy L3
→ plan composed 10 steps · 1 approval gate
→ twin.simulate 36 candidates · best #19 · stress in spec
→ policy.evaluate 11 writes permitted · 1 held for human
→ executing melt.pull ....... ok 6m20s
→ executing form.ribbon ..... ok 4m05s
→ executing anneal.curve .... ok 3m12s
! seed cluster ribbon edge · attributed to pull transient
! approval required pull step 2 · above site threshold
→ approved glass technologist on shift · 02:29:33
→ run complete scenario · stress in spec · 0 escaped seeds
$ glasent runs show scenario_fl2_01 --format genealogy
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
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
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
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 |
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
FAQ
Straight answers
The questions plant directors and glass technologists ask in the first meeting.
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.
Get started
Bring autonomy to your line
Start with one forming or annealing line and one measurable baseline. A 90 to 120 day plant-edge pilot on seeds and stones, residual stress or energy per tonne shows the number before you commit further.