The answer lands after the decision
By the time the study is signed off, the concept is frozen and the supplier is chosen. The work was correct, thorough — and commercially irrelevant.
Agentic simulation for engineering teams
A design question is asked on Monday. The answer arrives three weeks later — after the review, after the supplier was chosen. The analysis was right. It was also too late to matter.
Your engineering decisions stop waiting on simulation.
Describe the problem in plain language. Vyntra's AI agents run the study end to end and hand back a verified result — with the evidence behind it — while the design is still being decided.
Everything you see on this page is real — either a solve this platform ran, or a measurement from the rig we validate against, and each is labelled as such. Nothing here is an artist's impression.
Separating sand from water — solved on Vyntra, then traced to show where each phase ends up Real run
- Compute included — nothing to procure
- Onboard and start — no setup project
- No fixed commitment — no seats to buy
- Isolated per organisation — your data stays yours
- No simulation software licence fees
Where the time actually goes
Compute got cheap. Your design cycle didn't get faster.
Teams rarely lose weeks to computing time. They lose them preparing the problem, redoing work that was set up wrong, waiting behind the one person qualified to do it, and paying for the right to ask a second question.
Your specialists are the queue
Routine studies stack up behind the handful of people qualified to set them up. Your most expensive judgement is spent on preparation instead of on decisions.
Being wrong is discovered late
A flaw found in test, or in the field, costs a redesign, a schedule and a customer conversation. The same flaw found in design costs an afternoon.
None of that is a computing problem, which is why more computing has not fixed it.
What it actually does
Ask in plain language. Get an answer you can act on.
No scripting, no setup ritual, no week of preparation before the first useful number. Three steps, and your engineer is present for two of them.
Describe the problem
Your engineer states the question the way they would explain it to a colleague: this part, these conditions, this is what I need to know.
It runs the study end to end
The work is carried all the way through to a result — and checked as it goes, not hopefully at the end. Nobody has to babysit it.
You get the answer and its evidence
Findings, the visuals to present them, and the record of how the answer was reached — produced together, not assembled afterwards by hand.
The same three steps whether it is one question or the whole design space. Asking a second time is not a second project.
Real runs on this platform
This is what your team would be looking at.
Process equipment, a drivetrain component, an occupied space, a separation duty and an advanced material. Every frame below is this platform's own output, reproduced unedited.
The dead zones costing you batch time
Red marks the volume that barely moves: the cause of long batches, inconsistent product and expensive cleaning. Found on screen, not on the shop floor.
yield & batch-time driver
found before build
Will the part survive — and will it be quiet?
A drivetrain part taken from geometry to a verdict on both durability and noise, with the platform cross-checking its own answer before reporting it. Two warranty questions settled in design, not in the field.
durability answered early
noise cleared pre-prototype
cross-checked before reporting
Is the space actually fit to occupy?
Air movement through an occupied room, showing where it stalls and where it blows too hard. The same picture answers comfort complaints, energy spend and occupant-safety questions — before anything is installed.
comfort & energy in one view
answered before install
Did the separator actually do its job?
A slurry duty run out to steady state with the solids and the liquid tracked separately the whole way. Roughly 71% of recovered solids reported to the underflow — and before the platform would quote that figure at all, it closed its own mass balance to within 0.002% of what was fed in.
separation answered on screen
mass balance self-checked
gated before quoting
Screening a new material before the lab bill starts
Advanced materials are slow and costly to develop: most candidates fail, and each one can take months of lab time to rule out. Here the platform was asked to predict a two-dimensional sheet's atomic structure from first principles — no measurements, no fitting. It landed within 0.07% of the accepted spacing, which is how you earn the right to be trusted on a material nobody has made yet.
predicted, not fitted
within 0.07% of the accepted value
no lab time consumed
Visuals like these are produced as part of the run, so nobody spends a day building slides after the analysis is finished. The full engineering detail sits inside the product, for the people who need it.
What you are actually buying
A platform, not an implementation project.
Simulation capability normally arrives as a capital purchase — hardware, licences, an installation programme, a business case, and a year before anyone sees value. Vyntra arrives as a login.
- Compute is included. No cluster to build, no hardware to buy, no capacity to forecast. It scales with the size of the question you ask.
- Onboard and start. No installation programme, no rollout, no IT project to schedule before the first study.
- No fixed commitment. No seats bought a year ahead, no shelfware to explain at renewal. A quiet quarter costs you a quiet quarter.
- Isolated per organisation. Your geometry and your results stay separated from every other customer — and can run inside your own cloud where data residency or export control requires it.
- Nothing to maintain. No version migrations, no upgrade programme. Improvements simply arrive.
Airflow through an occupied space — solved on Vyntra Real run
Ground truth
And here is the hardware we are judged against.
Simulation is only worth trusting if someone checked it against reality. We run our own combustion rig and hold our results to what the instruments actually recorded. Nothing below is a simulation — it is the measurement.
Combustion, filmed at 1,000 frames a second
A real flame burning behind a real obstacle, recorded on our own high-speed camera and slowed down thirty-three times so you can see what it does between one millisecond and the next. This is the behaviour that decides whether a burner runs smoothly or tears itself apart.
high-speed camera
33× slow motion
measurement, not simulation
The unsteadiness, pulled out of the same footage
Subtract the steady average and what is left is the part that moves: red where the flame is surging, blue where it is falling away, rolling downstream in an alternating pattern. This is the oscillating, recirculating flow that engineers need predicted correctly — shown here as the camera saw it.
recirculation & shedding
same recording, same frames
no detail added
The failure mode that scraps programmes, caught on instruments
Pushed across its operating range, the chamber stops being quiet and locks onto a single loud tone — the oscillation grows almost five times in amplitude. Discovered late, this is the kind of problem that costs a redesign and a schedule. Predicting it early is exactly the work this platform exists to do, and these recordings are the yardstick we hold it to.
15 operating points
×4.9 amplitude growth
every point a recorded sample
Our own rig, our own instruments, our own data. We are not publishing the hardware, the geometry or the test programme behind it — but we will walk your engineers through the comparison against it under NDA.
Why the board can rely on it
An answer you can defend.
Engineering decisions carry liability — to your customer, your insurer, your regulator. AI is only useful here if it shows its work and admits what it does not know.
- Cross-checked before it is reported. Results are held against established engineering theory before the platform is willing to present them.
- It stops rather than guesses. Work that does not meet the standard is halted and reported as such — not quietly rounded into a plausible number.
- What was reviewed is provably what ran. Each study is sealed against its approved inputs, so nobody can claim the analysis drifted after sign-off.
- The audit trail already exists. The record a reviewer, an auditor or your customer asks for is produced as the work happens — nobody spends a week reconstructing it.
- No invented figures. The platform will not present a measurement it did not take.
Hours, not weeks
The point of speed is not speed. It is that the answer arrives while you can still act on it.
Shown its working
Every result arrives with the evidence behind it, so reviews and audits move forward instead of stalling.
Honest about limits
A number that looks right and is not is the most expensive thing a simulation can produce.
Checked against reality
We hold the platform to instrument data from our own rig, not to its own opinion of itself.
The commercial case
No per-seat tax on curiosity.
The largest line item in most simulation budgets is the right to open the software at all. Remove it and the constraint on how much engineering your team does in a week stops being procurement.
No licence linesimulation software cost
Traditional tools charge per engineer and per concurrent job. That line does not appear here, and it does not reappear as the team grows.
No queuewaiting for a free seat
Licences are a hidden cap on throughput. Remove them and how much engineering gets done is set by the work, not by what was bought last year.
Ask twicefor the cost of asking once
When a second option is effectively free, teams find the better design instead of defending the first one that passed. That is where the margin is.
We will model Vyntra against your current simulation spend during the pilot, using your own numbers, before you commit to anything.
Who this is built for
Deep-tech manufacturing, where the physics has to be right.
Built for teams whose products fail expensively — launch vehicles, airframes and UAVs, rotating machinery, process and pharmaceutical plant, and the engineering firms that serve them.
Launch vehicles & space systems
UAV & defence platforms
Automotive & EV powertrain
Industrial rotating equipment
Pharmaceutical & process plant
CFD & engineering services
Deployable into your own cloud tenancy where data residency or export control requires it.
Bring us a problem you have already solved.
Hand us a case where you already know the answer. We will run it, put our result next to yours, and show you what it would have cost and how long it would have taken — before you commit to anything.
Or write to us directly: [email protected]