Industrial AI for the machines that make everything.
Ntruss AI is an industrial AI company. We work on both halves of the problem. Digital AI carries the product from requirement to a specification the plant can build — requirement & specification, design, analysis, systems engineering, bill of materials. Physical AI carries it from there to a good part at volume — simulate, test & quality, scale — on die casting, CNC machining and injection molding. It is a platform, not a point tool: agents automate and orchestrate the design-to-scale-up workflow, then issue directed actions that link up with machines, robots and people to improve the physical process itself.
A business that no longer depends on who.
Captured
The judgement that decides the part is written down — as software, not as tribal memory held by the one engineer who knows.
Everywhere
It runs on every machine, on every shift, at the same standard — in the design office and on the shop floor alike.
Acting
It does not stop at advice. It reaches the controller, closes the loop, and leaves a record behind it.
Industrial AI has two halves.
An industrial problem does not arrive as a fragment, so it cannot be answered by a point tool. Ntruss AI is built on both halves — and on the join between them, where the requirement written in Digital AI becomes the specification the machine is held to in Physical AI.
Digital AI
Design and systems engineering — what the product must do, how it is specified, how it is analysed, and how the pieces come together into something buildable.
Design
Engineering
Physical AI
Where the part is actually made. Scope runs from simulate through to scale — the agent simulates the process, proves it, runs the machine inside a bounded envelope, and holds it at volume.
Processes
Value chain
From a customer requirement to a specification the plant can build.
This is where most of the cost of a product is committed and almost none of it is spent — and where the work is slowest, because requirements live in documents, analysis lives in silos, and the bill of materials is reconciled by hand. Ntruss AI holds all five as connected, structured objects, so a change in one is a change in all of them.
Requirement & specification
Customer intent, targets, standards and homologation clauses captured as structured requirements and decomposed into functional and technical specifications.
Design
2D to 3D, parametric modelling with GD&T recovered, and design-for-manufacturability checked against the real constraints of the process that will make the part.
Analysis
Structural, thermal and durability analysis, tolerance stack-up and DFMEA — failure modes reasoned from the design and from what has actually failed before, each linked to the control that catches it.
Systems engineering
Interfaces, allocations and change impact across mechanical, electrical and software. When a requirement moves, the agent traces what it breaks, down to the control plan on the floor.
Bill of materials
The BoM held live against design, supplier and compliance evidence — variants, make-versus-buy, part reuse and cost roll-up kept consistent as the design changes.
From simulate to scale — the agent doesn't stop at a report, it runs the machine.
Physical AI covers three processes across three stages of the value chain. When the fix belongs on the equipment rather than in a document, Ntruss AI acts there directly — inside a bounded envelope your engineering team defines, and can revoke at any time.
Pressure die casting
Mold-flow and solidification simulation, porosity and shrinkage prediction, and shot-parameter tuning — fill speed, intensification pressure, die temperature — patched on the machine controller.
CNC machining
Toolpath generation and cutting simulation, cutting-force and tool-wear prediction, and feed-and-speed adjustment written directly to the controller as conditions change.
Injection molding
Fill, pack, cool and warp simulation, gate and vent placement, and pressure-and-temperature profile tuning at the press to hold dimensions across a full run.
Simulate
The physics is run before steel is cut. The agent iterates the process design itself when a defect is predicted — porosity, warpage, short-shot, tool deflection — instead of handing back a report for someone to interpret.
Test & quality
First-article and design-of-experiment runs on a real machine, correlated back to the simulation. Then quality end to end: in-process SPC, visual inspection at the station, and CAPA traced through shot, tooling, material lot and maintenance history.
Scale
The same agent that proved the process runs it at volume — tuning parameters, watching for drift, correcting inside the envelope, and holding quality across every line and every shift.
Ntruss AI plays across the whole V model, creating value at every level.
Every engineering organisation already runs this shape, whether it is drawn on the wall or not. A requirement is decomposed down one side until it becomes something a machine can make; what the machine makes is integrated and tested back up the other until it is accepted. Digital AI works down the specification side, Physical AI turns the specification into a proven part at the bottom, and the evidence climbs back up — which is why a defect found in production does not end at a scrap ticket. It travels back to the DFMEA line that missed it.
Customer requirements & targets
Intent, targets and constraints captured as structured requirements, not a slide deck. Requirement & specification starts here.
Engineering release & acceptance
Release evidence assembled against the original targets, traceable clause by clause.
Functional & technical specification
Requirements decomposed into a specification the whole product is measured against, with the bill of materials held live beside it.
Complete product integration & test
Whole-product verification, with every result mapped back to the requirement it proves.
System functional & technical specification
Interfaces, allocations and system-level failure modes. Systems engineering and analysis live at this level.
System integration & test
Systems brought together and tested; deviations routed to the owning specification, not to an inbox.
Component specification
The part as the plant will actually see it: geometry, material, tolerance, process window, control plan. Design closes out here.
Component test & verification
First-article, dimensional and functional results correlated back to the simulation that predicted them.
Pressure die casting, CNC machining and injection molding. The agent simulates the process, proves it on a real machine, actuates on the controller inside a bounded envelope, judges the part and opens the CAPA — then pushes what it learned back up the integration and test side. This is the point almost every industrial AI product stops short of, and it is the only point at which a specification becomes a good part.
Why this matters commercially. A customer buying only the specification side gets better documents. A customer buying only the production side gets a better machine. A customer on the whole V model gets a business where the intent, the part and the evidence are one thread — and that thread is software, not a person who happens to know.
Process AI is the engine that carries the specification onto the machine.
Most plants hand a part between disconnected teams and tools. Ntruss AI orchestrates all three stages on one platform — so nothing is lost in the handoff, and the correction reaches the machine in the same cycle.
Simulate
Prove the process in physics before it costs anything in steel.
- Mold-flow, thermal, solidification and toolpath simulation
- Defect prediction — porosity, warpage, short-shot, tool deflection
- The agent iterates the design and process itself, not a report
- The process window is fixed before the die is cut
Test & quality
Prove it on a real machine, then hold quality end to end.
- First-article and DOE runs correlated back to the simulation
- In-process SPC on the parameters that actually decide the defect
- Visual and dimensional judgement at the station
- CAPA traced through shot, tooling, material lot and maintenance
Scale
Run it at volume, on every line and every shift, without the SME.
- Parameter tuning written to the controller inside the envelope
- Drift detected and corrected in the same cycle
- Consistency held across lines, shifts and plants
- Every action logged and mapped to the standard's clause structure
Bounded envelope
Every actuation stays inside limits your engineers set, per machine and per parameter.
Reversible by design
Any change the agent makes can be rolled back to the prior known-good state in one action.
Always logged
Every physical action is recorded with its trigger, evidence and outcome.
Human override, always
An operator or engineer can pause, override or lock out agent actuation on any line, at any time.
Three processes, each modelled at the level of its own physics.
Pressure die casting
Mold-flow and solidification simulation, porosity and shrinkage prediction, and shot-parameter tuning — fill speed, intensification pressure, die temperature — adjusted on the machine itself.
CNC machining
Toolpath generation and cutting simulation, cutting-force and tool-wear prediction, and feed-and-speed adjustment written directly to the controller as conditions change.
Injection molding
Fill, pack, cool and warp simulation, gate and vent placement, and pressure-and-temperature profile tuning at the press to hold dimensions across a full run.
The same platform is fungible to the four processes around the core three.
Most parts don't stop at a casting, a machined feature, or a molded shell. The simulate → test & quality → scale approach carries into the processes that typically come before or after them, without rebuilding the platform for each.
Sheet metal stamping & fabrication
Die and press-tool simulation, springback prediction, progressive-die stage tuning as material lots vary
Metal forging
Flow and grain-structure simulation, die-fill prediction, ram-speed and temperature tuning at the press
Additive manufacturing
Build simulation for distortion and supports, in-process laser power and scan-speed correction layer by layer
Surface finishing & coating
Coating-thickness and adhesion simulation, closed-loop control of plating current, spray, or cure temperature
Everything around the process, not just the process.
A die casting cell, a CNC line, or a molding press doesn't run in isolation — it runs inside a plant with its own machines to maintain, energy to manage, and a schedule to hit. Plant AI is where Ntruss AI's agents look up from a single process to the facility running it.
Predictive maintenance & asset health
Monitors vibration, thermal, and electrical signatures across machines — not just the three core processes — to flag a failing bearing or a degrading motor before it causes downtime or a quality escape.
Production scheduling & OEE
Sequences jobs, changeovers, and line balancing across die casting, CNC, injection molding, and adjacent cells to hold throughput targets, and explains why the schedule shifted when it does.
Energy optimisation
Tunes furnace and oven cycles, compressed air, and chiller loads to cut energy cost per part, without moving any parameter outside the quality envelope Test & quality is already watching.
Processes down. Simulate, test & quality and scale across.
We do not organise by industry. We organise by process — because the physics of a die, a tool, or a mold is the same whether the part goes into a car, a pump, or a laptop. Three processes are live today; four adjacent ones extend from the same platform.
Pressure die casting
LIVECNC machining
LIVEInjection molding
LIVESheet metal stamping & fabrication
FUNGIBLEMetal forging
FUNGIBLEAdditive manufacturing
FUNGIBLESurface finishing & coating
FUNGIBLEThe same thread, from the customer requirement to the finished part.
Take an automotive die-cast housing — an OEM requirement, a Tier-1 designing it, a foundry making it. Digital AI holds the first five stages; Physical AI holds the last three. Nothing is handed over as a document that someone has to re-key.
→ scroll sideways to follow the chain
Requirement & specification
Customer targets, standards and homologation clauses captured as structured requirements
Design
2D to 3D, GD&T, and manufacturability checked against the real process window
Analysis
Structural, thermal and tolerance analysis; DFMEA with each mode linked to a control
Systems engineering
Interfaces, allocations and change impact traced down to the component specification
Bill of materials
BoM held live against design, supplier and compliance evidence as the design moves
Simulate
Die, gating and cooling proven in physics; the process window fixed before steel is cut
Test & quality
First article, in-process SPC, inspection at the station, and CAPA with the evidence attached
Scale
Volume held across lines and shifts; PPAP and release assembled from records already traced
Engineering intent What the part must do and what the process can hold — set in stages 1–5, and honoured by everything downstream of them.
Quality One unbroken thread: requirement → DFMEA → control plan → in-process SPC → inspection → CAPA → PPAP. The same object all the way across, not six systems.
Predictability Die life, tool wear, machine health and schedule — from the first proving shot through to steady volume.
And it runs backwards too. A defect found at stage 7 does not end there. The finding climbs back up the chain — into the control plan, into the DFMEA, into the next design — so the same failure is not re-learned on the next programme by the next engineer.
AI doesn't scale with technology alone. It scales with deployment and adoption.
Most AI initiatives prove the technology. Few make it work inside the business. Ntruss solves that with a different model: Forward-Deployed Engineers.
We hire process SMEs who have actually worked inside the operations we solve for — people who understand the machines, workflows, decisions and realities of the plant. We train them as FDEs and deploy them alongside the Ntruss platform. The FDE brings the process expertise. The platform brings the AI.
Deploy
FDEs connect Ntruss to the real operating environment — machines, historians, QMS, ERP and existing workflows — and translate process knowledge into AI-enabled operations.
Adopt
Because FDEs speak the language of the business, they build trust with operators, engineers and leaders. AI becomes part of the way work gets done — not another technology project.
Scale
Every deployment strengthens the Ntruss platform. Process expertise becomes reusable knowledge, applications become more configurable, and subsequent deployments require less FDE intervention.
We don't scale AI by scaling implementation teams.
We scale AI by turning process expertise into software.
Built for the standards you're already audited against
Every design decision, simulation result, and physical action is logged and mapped to the applicable standard's clause structure as it happens.
Deployed wherever a mold, a die, or a toolpath decides whether the part is right.
The processes are the product. The industries are where those processes are found — which is why the same platform lands in a foundry, an aerospace machine shop, and an EV programme without being rebuilt for each.
Automotive & Tier 1
Die-cast housings, CNC machined powertrain components
11.5% CAGR
Aerospace & defence
CNC machined structural parts, safety-of-flight tolerances
11.0% CAGR
Electronics & semiconductor
Precision die-cast and molded enclosures, high mix
10.0% CAGR
Medical device & pharma
Injection molded components under Part 11 traceability
10.6% CAGR
Food & beverage
Injection molded packaging at high throughput
9.9% CAGR
Heavy equipment & fabrication
Die-cast and CNC machined components at low-volume, high-mix
~9.4% CAGR
Physical mold and tooling iterations avoided by catching defects in simulation first.
Typical time between a deviation being detected and a correction reaching the machine.
Typical audit preparation time once design, simulation, and production records are traced automatically.
Where defects get caught — in simulation or at the station, instead of at final inspection.
Where the platform is working today.
Two engagements at opposite ends of the V model — one on the specification side of a new product, one on a live new-mobility programme.
EV two-wheeler: new product development
A ground-up two-wheeler programme with a compressed concept phase, a young supply base, and homologation to hit. The constraint was not manufacturing — it was the specification side: requirements living in documents, DFMEA in spreadsheets, and a bill of materials that moved every week.
EV mobility solutions: engineering-intensive vehicle upgrade
An EV mobility solutions manufacturer building retrofit kits. Their vehicle upgrade needed SME-level system thinking, CAE and DFMEA — the kind of work that normally waits on one senior engineer. Ntruss AI was customised and deployed with a UI their technical lead could use from day one.
Three founders. Strategy, scale, and the shop floor.
Ntruss AI is not a first attempt at an industry someone read about. Between the three of us: the strategy work that gets a programme approved, the operating discipline that scales a data and AI business past $100M, and the embedded-systems and shop-floor expertise that makes a claim about a machine true.
Desegan Ponnuswamy
Strategy & transformation · 22+ yrs
Advised Fortune 500 organisations across North America, Europe, Australia, SE Asia and India in automotive, manufacturing, energy & utilities, and retail & CPG. Expert in strategy, financial modelling and digital programme execution.
des@ntrussai.comSriraj Srinivasan, PhD
Scaling data & AI businesses · 23+ yrs
Built and scaled Data, AI and Robotics businesses past $100M, driving EBITDA expansion through repeatable operating playbooks. Worked in automotive, chemicals, consumer products, oil & gas, and life sciences — PE portfolios, VC-backed startups and Fortune 500.
sri@ntrussai.comSajeeth Kumar
Applied AI/ML & embedded systems · 23+ yrs
Automotive embedded-systems SME and founder of Reynlab and the Gearmerit diagnostics-quality platform. Architect of ML-driven calibration automation and agentic engineering workflows over deep ECU, AUTOSAR and vehicle-diagnostics work.
sk@ntrussai.comThe strategy to sell it, the operating discipline to scale it, and the shop-floor expertise to make it true — behind a bench of process SMEs we train as forward-deployed engineers.