Trust before autonomy
Grounding, audit, human-in-the-loop and graduated control. The level is a dial the forge turns, and the press is the last thing it moves.
About
Forgings carry the highest loads we trust metal with. The forge should not run on memory.
Forgiron exists to give every forging plant a system that runs heating, forging, die management and inspection itself, on spec, at rate, with less scrap and energy, and to capture the craft of a retiring forge workforce before it disappears. The vision is to become the operations layer for the world's forging manufacturing.
Mission
Give every forging plant the ability to run heating, forging, die management and inspection autonomously
On spec, at rate, with less scrap and energy, and capture the craft of a retiring forge workforce before it disappears.
VisionBecome the operations layer for the world's forging manufacturing, where every forging is heated, forged, die-managed, inspected and qualified on Forgiron.
Why
Why we are building this
The fact on the forge floor that started it.
Forgings are the strongest, most safety-critical metal parts made: crankshafts, connecting rods, gears, turbine and jet-engine discs, landing gear, valves and flanges, shaped by enormous force at high temperature so their grain flow carries fatigue load. Making them is still craft-bound, energy-hungry work. A billet heated a little wrong, a stroke that under-fills the die, a worn die left in one cycle too long, a lap folded into the surface or an internal crack no one saw each becomes scrap, a forced die change, a downgrade or a safety-critical field escape.
Most forges run this on fixed heating and press setpoints, spot inspection and the craft of a retiring forge-operator and die-engineer workforce. Induction controllers heat. Press controls hold a schedule. Inspection stations alarm after the part is made. Nobody closes the loop.
Why now
- The aerospace and energy forging boom, the EV driveline and heavy-vehicle shift, defense and reshoring
- Enormously energy-intensive billet heating and high scrap eroding margin
- A severe, retiring forge-operator and die-engineer workforce in a brutal, hot environment
- Machine vision, ultrasonic and MPI inspection, edge AI, robotic forging handling and forging FEM twins have matured for high-force forging
Principles
How we build
The principles we work by.
Compounding data
Every supervised correction trains the system. The closed-loop chain from heat to stroke to die to inspection to disposition is the asset.
Land narrow, expand
One cell, one metric, one baseline agreed in writing. Then the forge. Then the group.
Cite everything
Every answer from the reasoning layer carries its source. Every action carries its evidence and its envelope.
Isolate tenants
Die designs, recipes, models and memory never cross a customer boundary. On-prem for those who need it.
Say what is planned
Figures are design targets until they are measured. Scenarios are labelled scenarios. Standards are cited, not claimed.
Focus
What kind of company this is
For anyone checking eligibility or category.
- An independent startup building original physical-AI software for forging and press-forming manufacturers
- A product company, not a consultancy, systems integrator, reseller or outsourced development shop; services are limited to deployment, integration and qualification support for the platform
- Not a cloud provider and not associated with cryptocurrency
- Pre-launch and pre-revenue. Incorporation, headquarters and headcount are not published yet and nothing on this site should be read as claiming them
Run timeline · scenario
What we are actually building
Every Forgiron run is an ordered, inspectable sequence. This is run_7c2e41 on Cell F-2 · 2,500 t closed-die press · 42CrMo4 connecting rod. Scenario completes in 10 steps with one approval gate. Surface temperature held at 1,205 °C with a 14 °C core-to-surface spread, fill predicted and confirmed complete, one lap precursor at the flash land cleared by a blow-energy trim, lot L-80412 released with full genealogy.
run_7c2e41
Cell F-2 · 2,500 t closed-die press · 42CrMo4 connecting rod
illustrative scenario
SUCCEEDED
-
01
ingest.order
SUCCEEDED
0.6 s
Plant Orchestrator · Pulled the part spec for the 42CrMo4 connecting rod from MES: forging window 1,180 to 1,230 °C, two-blow closed-die schedule, flash land 2.4 mm, temper to 28 to 32 HRC, 1,200 pieces. Locked the qualified process envelope.
-
02
twin.simulate
SUCCEEDED
52 s
Forge Twin · Simulated 24 candidate heat-and-stroke recipes on the as-forged twin for soak time, blow energy and spray timing, ranked on predicted fill, lap risk at the flash land, die load and kWh per piece.
-
03
heat.soak
SUCCEEDED
2 m 10 s
Heat-and-Billet · Induction power on IH-2 stepped down 4% in the final soak stage as the pyrometer read 1,205 °C at the surface. Core-to-surface spread estimated at 14 °C against a 20 °C limit, no overheat, scale growth inside the window.
-
04
die.spray
SUCCEEDED
48 s
Die-and-Lube · Lower-die spray dose raised 6% and cooling extended 0.3 s as die surface temperature climbed through 240 °C. Heat-check growth on cavity 2 flagged at 0.31 of the die-change threshold.
-
05
approve.human
APPROVAL
46 s
Plant Orchestrator · Recipe #9 moves second-blow energy 3% below the standing schedule. Press-schedule changes stay supervised on this part family, so the step was held for the shift forging engineer and approved at 05:47:31.
-
06
forge.stroke
SUCCEEDED
1 m 36 s
Forge-and-Press · Two-blow schedule executed with the approved energy. High-speed vision and force curves confirmed complete fill on the first ten pieces; predicted lap risk at the flash land fell from 0.27 to 0.08.
-
07
inspect.fuse
SUCCEEDED
continuous
Defect-and-Inspect · Eight cameras, two thermal cameras and the ultrasonic station fused at the edge. One lap precursor at the flash land on piece 212 flagged and cleared by a 1.5% blow-energy trim inside the approved envelope; the piece was routed to MPI and passed.
-
08
handle.transfer
SUCCEEDED
continuous
Robot-and-Handling · Press-to-trim transfer planned and executed by the handling robot at 11 s per piece, temperature loss in transfer 38 °C against a 60 °C limit, interlock zones clear throughout.
-
09
heat.treat
SUCCEEDED
continuous
Trim-and-Heat · Temper furnace HT-1 zone 2 trimmed 5 °C against the predicted hardness from the recorded heat and stroke history. First hardness checks read 30 HRC, inside 28 to 32.
-
10
lot.release
SUCCEEDED
1 m 20 s
Quality-and-Conformance · Lot L-80412 released: genealogy written (billet lot, heat curve, stroke record, die cycles, inspection map, approvals, temper record) and dispositioned against IATF 16949 criteria with cited evidence for the quality engineer's sign-off.
Market
The market
Figures are the company's own estimates.
$15BTotal addressable spend: forging automation, heating and press control, die management, inspection, forging MES and factory software
$3.8BServiceable with Forgiron's product and go-to-market
$230MRealistically obtainable within three years, an estimate
~11%Estimated annual growth of the addressable spend
Leadership
Who is building Forgiron
Forgiron is led by Nathaniel Brooks (Founder & CEO) and Ethan Mitchell (CTO): one owns the forge floor and the customer, the other owns the platform that runs on it.
Nathaniel Brooks
Nathaniel spent fifteen years in forging, starting as a process metallurgist and moving through forge operations and plant leadership at aerospace, energy and automotive forging operations. He founded Forgiron after watching die wear, mis-heated billets and late-found laps scrap safety-critical parts that experienced operators could have saved.
Owns: company vision and strategy, forging-plant design partnerships and pilots, qualification and customer-assurance programmes, fundraising, and hiring.
Expertise: open- and closed-die forging, billet heating and heat treatment, die life and lubrication, grain flow and mechanical properties, aerospace and energy qualification, lean forge operations.
Ethan Mitchell
Ethan has spent twelve years building perception and control software for heavy industry, from machine-vision and ultrasonic inspection to edge AI running next to presses and furnaces. He leads engineering at Forgiron, with a bias for supervisory control that is bounded, auditable and safe to run in a hot, high-force environment.
Owns: the plant-edge platform, agent and control architecture, vision and NDT perception, the as-forged digital twin, the NVIDIA stack, OT integration and security, and the engineering team.
Expertise: computer vision, edge inference (Jetson, TensorRT), PLC/OT integration, forging FEM and digital twins, time-series ML, safety-bounded autonomy and audit design.
Hiring
The founding roles we expect to open are on the careers page: edge perception, controls and OT integration, simulation, and a forging process or die engineer.
Writing
Recent from the blog
The stroke is the moment of truth
A forging is the strongest metal part we make, decided in a fraction of a second by force, heat and a die. Most forges still judge it after it cools.
Autonomy is a dial, not a switch
A forge does not go from manual to unattended in one step, and a press should not be the first thing an agent moves.
Hit soundness before the stroke
Forges commit a heated billet, a die and a press to a part before they know it will fill. The twin changes the order.
Accelerated computing
Physical AI, at the press
A stroke lasts a fraction of a second and a billet cools while it waits. Perception has to be local, deterministic and fast, so Forgiron runs GPU inference at the plant edge and keeps training, simulation and optimisation in the cloud or on-prem.
Edge perception
8 to 24 cameras per line plus thermal, ultrasonic, MPI, press and ram streams fused at the cell. Sub-100 ms visual alerts and sub-500 ms recommendations are the design targets. Jetson Thor is planned for higher sensor density.
Model serving
Defect, ultrasonic and MPI fusion, die-wear, heating, press-fill, property and disposition models behind one orchestrator at the plant edge, routed by latency, tenant, sensitivity and cost, with canary rollout by cell.
Training
Multimodal defect, die-wear, heating, press-fill, property and quality-disposition models trained on thermal images, high-speed vision, ultrasonic and MPI signals, press curves and engineer corrections. Dedicated training capacity is planned.
Forge-line twin
Billet heating, die thermal state, press stroke, fill, stress, die load and robot handling simulated before the stroke. GPU forging FEM is the workload; physics-informed surrogates are planned so recipes rank at production cadence.
Synthetic data
Rare lap, fold, underfill, crack and die-wear scenarios generated and validated against real inspection distributions before promotion. Planned.
Optimisation
Die allocation, press sequencing, heating batches, heat-treat queues, robot and takt coordination and inspection routing.
Robot handling
Hot billet, forging, die and trim handling skills trained and validated in simulation before they run next to a press.
GPU ETL
Daily features from thermal, video, ultrasonic, MPI, press and genealogy data for retraining, drift detection and yield, scrap, die-life and energy benchmarks.
Guardrails
An agent that can move a press schedule needs a leash
Forgiron writes to production equipment. Every capability is scoped, every write is policy-checked against the qualified envelope, and every action is written to an append-only audit log the forge owns.
- Bounded action space. Each agent writes only to an explicit tag allow-list, inside per-tag rate and magnitude limits and the qualified process envelope for the part family.
- Policy engine before every write. Autonomy level, shift, part family, interlock state and operator presence are evaluated before a setpoint moves.
- Human-in-the-loop gates. Press-schedule changes, die changes and lot release wait for a named approver. Safety-critical press control stays guarded by PLC interlocks and engineer approval until qualification evidence supports more.
- Immutable audit log. Append-only and exportable. Every action records source evidence, the approved process envelope, confidence, estimated yield, scrap, die-life and energy impact, rollback path, approver and lot scope.
- Hard fallback. Loss of the edge node, the network or a model returns control to the furnace, press or die controller's last known-good state. Interlocks are never in Forgiron's path.
- Tenant and die-IP isolation. Die designs, part drawings, alloy recipes and quality records never cross a customer boundary. On-prem deployment is available for sensitive producers.
Compliance posture
| Item | Scope | Status |
|---|---|---|
| SOC 2 Type I | Cloud control plane | QUEUED Planned, first 6 months |
| SOC 2 Type II | Cloud control plane | QUEUED Planned, 6 to 12 months |
| SSO and RBAC | Console and approvals | QUEUED Planned, 6 to 12 months |
| AS9100 and NADCAP assurance packages | Disposition and genealogy evidence | QUEUED Planned, 6 to 12 months; not a certification claim |
| Standards-cited reasoning | AS9100, IATF 16949, NADCAP, ASTM documents | RUNNING In the design, every answer cites its source |
| Per-tenant and die-IP isolation | Recipes, die designs, models, memory | RUNNING In the design from day one |
| On-prem and plant-edge deployment | Sensitive producers | RUNNING Design option |
FAQ
Straight answers
The questions plant directors, forging engineers and quality engineers ask in the first meeting.
Yes, but only within an explicit tag allow-list with per-tag rate and magnitude limits, inside the qualified process envelope, and only at the autonomy level your plant has set. Design partners start in shadow mode, where Forgiron writes nothing. Press-schedule changes stay supervised by a named forging engineer until the part family is qualified.
Control returns to the furnace, press or die controller's last known-good state. Forgiron is a supervisory layer on top of the PLCs and interlocks you already run, never a replacement for them, so an outage degrades the cell to its current way of running, not to a stop.
Advisory recommendations start from the physics-based twin and the part spec in the first weeks. Defect, die-wear and heating prediction improve as cell history and labelled outcomes accumulate per part family. The first training sets are sized at 2 million labelled frames and signals, 20,000 heat and press cycles and 10,000 die-life events per design-partner cohort, all design targets.
Only if you choose cloud training. Die designs, part drawings, recipes, process models and quality records are isolated per tenant and never used to train another customer's models. A fully on-prem deployment at the plant edge is available for sensitive producers, and federated learning across sites is planned and waits on data-rights agreements.
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 evidence, the envelope applied and the human decision.
A paid pilot of 90 to 180 days on one cell, scoped to a single wedge with a success metric written into the order form: defect-detection accuracy, scrap reduction, die-change reduction, energy reduction or first-pass-quality improvement. The first 30 to 60 days baseline scrap, die life, energy, inspection escapes and takt in shadow mode.