 Next-Gen | What it fixes Show me |
| Next-Gen | What it fixes Show me |
Next-Gen Engineering and Research Development, LLC
NeuroLog™
AI that doesn’t hallucinate.
NeuroLog reads the problem before it answers, remembers what it learned, and shows you how it got there.
Let us show you →What it fixes
What it costs
Far less
You stop paying to re-send the same file.
Every answer
No guessing
It reasons instead of predicting. You see how it got there.
No data center
Your laptop
Runs on hardware you own. Your data never leaves.
What it fixes
We hit all four on our own engineering projects. We built NeuroLog to stop them.
It describes the schematic and gets the connections wrong.
Close the window and last week’s work is gone.
Confident answers with nothing behind them.
Top-tier prices for work a small model could handle.
Major frontier labs
The major frontier labs recently published the same admission: AI still cannot judge which of its own answers to trust. We had already spent two years building for exactly that.
Other AI needs a data center.
NeuroLog runs on the hardware you already own.
The system
It reads the problem and makes a plan before it answers. Then it checks its own work and fixes it when it is wrong.
Not a scratchpad that empties when you close the tab. A memory that holds what things are, how they connect, and when they changed.
It sends each step to the cheapest model that can handle it, and only calls the expensive one when it has to.
Proof, receipt-chained
Two results we can put on the screen. On a standard published benchmark it beat every score from 2025, using a general engine that was not tuned for the test. And it independently re-derived a known algorithm from scratch, with a proof checker confirming the work.
Built by two engineers in 2024 because the AI tools on their own engineering projects could not do the job. The projects are still running. So is the engine.
See it run
We show NeuroLog working on something like yours, live. If cost is the question, bring a real export — on our own file one question dropped from 266,257 tokens to 67, same model, same answer — and we measure yours on the call.
One step
Thirty minutes. We show it working on your problem. If it does not fit, we will tell you.