16/17
True causal links recovered
On a standard causal-discovery benchmark, with the same answer reproduced across five runs.
Next-Gen Engineering and Research Development, LLCYou asked AI to do real work.It couldn’t.We built the one that can.
NeuroLog™
AI that checks before it answers.
NeuroLog reasons through the problem. Vectryx remembers what matters. You get an answer you can inspect instead of a guess you have to trust.
30 minutes. Your problem. No deck.
Every answer
No guessing
Reasons through the problem and shows you how it got there.
Private by design
Hardware you own
Can run entirely on hardware you own, so sensitive work can stay local when the deployment requires it.
Beyond retrieval
Finds what others miss
It reasons across relationships, time, and causality — surfacing connections similarity search misses.
Not a promise. Measured.
We built it. Then we tried to break it.
Before asking you to trust a new kind of AI system, we put it against problems with right and wrong answers.
8/8
Optimal plans found
In a scheduling challenge where the comparison frontier models produced zero perfect answers.
1 machine
Serious reasoning, locally
Benchmark workloads ran on ordinary local hardware instead of requiring datacenter infrastructure.
Want to see what those numbers actually mean?
We will show you the system, the test, and the limits of what the result proves.
Let us show you
30 minutes. Your problem. No deck.
What it fixes
Four ways ordinary AI breaks on real work.
If one of these sounds familiar, bring us the problem. We will show you what changes.
01
It makes things up
Confident answers with nothing behind them.
02
It forgets what it learned
Yesterday’s work becomes today’s prompt all over again.
03
It can’t understand the actual work
Drawings, relationships, constraints, and structured technical data break ordinary AI workflows.
04
It wastes compute
Big models get used for work that does not require them.
Why it works
NeuroLog reasons. Vectryx remembers.
NeuroLog determines what follows from the evidence and constraints. Vectryx preserves what the system knows across relationships, evidence, causality, and time.
NeuroLog · reasoning engine
Turn a problem into a checked conclusion.
It plans the work, applies constraints, chooses the right models and tools, verifies intermediate results, and keeps the reasoning path attached to the answer.
Vectryx · cognitive memory
Remember more than documents.
It retains how entities relate, when facts were true, what caused what, and which evidence supports the system’s current understanding.
Together
Together, they give AI something it normally lacks: reasoning that survives the conversation.
Let us show you
30 minutes. Your problem. No deck.
Every answer has receipts
Don’t trust the answer. Inspect it.
NeuroLog is built to preserve the path from evidence to conclusion instead of handing you a fluent sentence and asking for faith.
01 · What it used
The evidence
The source facts, retrieved evidence, and tools involved in reaching the answer.
02 · What it inferred
The reasoning
The intermediate conclusions that moved the problem from evidence toward an answer.
03 · What it rejected
The rejected paths
Conflicting, unsupported, or invalid candidates that did not survive the checks.
04 · Why it survived
The checks
The constraints and verification steps the final answer passed before it reached you.
See the receipts
Thirty minutes. Your problem on the screen.
Bring us the file, drawing, dataset, or workflow where AI failed you. We will show the work, not just the answer.
Let us show you30 minutes. Your problem. No deck.
One step
Tell us what AI got wrong. We will show you it working.
Thirty minutes. Your problem. No deck. If it does not fit, we will tell you.
sales@nextgenrd.comNo deck required
Start with the failure.
Tell us the thing you expected AI to do and where it broke. That is enough to start.
Let us show you30 minutes. Your problem. No deck.