Next-Gen Engineering and Research Development, LLCThat was the architecture, not the ceiling.
So we rebuilt the architecture.
Two years building the answer to the bottleneck the frontier labs just published. NeuroLog reasons, remembers, and shows its work — on a fraction of the compute.
Up to 50%+ Lower cost in token use, depending on the question | More than 100× Less compute | Target Zero Hallucinated answers |
Explore Five sections. Collapse any you don’t need. | The Gap · NeuroLog · Problems · Cost · About |
01 The GapWhat Anthropic and Google DeepMind just published, and the two runs we can put against it. Expand +
A handful of N.E.R.D.s spent the last two years building against exactly that. Anthropic named the bottleneck: judgment — which results to trust, when to stop, who reviews work produced faster than humans can read it. DeepMind mapped four routes past human-level AI and called the hard parts open.
80%+ of code merged into Anthropic’s own codebase is authored by Claude, as of May 2026. Before Claude Code, it was low single digits. | 8× as much code shipped per engineer per quarter as in 2021–2025. | 3× → 52× on the same optimization benchmark, May 2025 to April 2026. A skilled human reaches 4× in four to eight hours. |
What is still missing is judgment — which results to trust, when to stop, who reviews work produced faster than humans can read it. And the curves may flatten into S-curves, past which progress may require a new architecture rather than more scale.
Human review Ours is executable. Lean proofs, replay scoring, forensic self-audits that publish their own failures. Review runs as code, not in a queue. | Research judgment Ours becomes selection pressure. A refutation is an acceptable, publishable outcome. Only verified gains survive. | Scaling hits an S-curve Ours come from architecture, not scale — 100×–1000× less compute. If scaling stalls, we do not. |
Route 1 · Scaling More compute, data, parameters — the route the hyperscalers are betting the power grid on. | Route 2 · Paradigm shift A new architecture that supersedes the transformer. Nobody has shipped the replacement. |
Route 3 · Recursive self-improvement AI that drives its own research and development. Still mostly aspiration across the field. | Route 4 · Multi-agent collectives Many specialist agents coordinating on one substrate. Coordination is the unsolved part. |
A system that discovers algorithms, verifies them against a real theorem prover, and blocks its own weak results is the loop Anthropic described and DeepMind mapped. We are on Route 3 with runs banked, not with a roadmap.
On Sachs, the standard published benchmark of 11 protein-signalling nodes, our system produces a Structural Hamming Distance of 5 — below every comparable published in 2025 (CaPS 11, ALIAS 9, DrBO 9). Reproduced five times at the same accuracy in a sealed batch. Each run is receipt-chained.
The cooperation lift F1 = 0.20 → 0.86 Memory layer alone versus the same layer inside our engine. A 4.3× lift on identical inputs — our system raises the ceiling on whatever it wraps. | Honest framing Reproducible inside the lab today. Publication-class peer review is a funded milestone — a strict-protocol pre-registration scoped against this round. |
On 16 May 2026 our discovery pipeline produced Karatsuba’s algorithm. We gave the system a fair starting bundle and ran a scrub audit on it — zero hits for the answer’s vocabulary in the seed. It composed the three-multiplication path on its own. Lean 4 verifies certificate-level proof obligations against the real Lean binary.
Why this matters Every algorithm a customer’s engineers cannot write themselves is a problem they otherwise pay a hyperscaler to brute-force. A system that discovers algorithms — with machine-checkable receipts — collapses that cost structure. | Honest framing One re-discovery banked, four staged. The next runs may refute their target, which the framework treats as an acceptable, publishable outcome. The pipeline even blocked its own paper from arXiv: novelty scored 0.090 against a 0.30 bar. |
02 NeuroLogThe architecture — reasoning, memory, and models called on demand. Expand +
Every other stack points one expensive model at everything and hopes. NeuroLog plans first, remembers what it learned, and spends frontier money only where frontier intelligence is actually required.
| 01 | Reasoning The engine It understands the problem before it touches a model. It builds a plan, works the plan, checks its own path, and corrects the path when the path is wrong. No human sits in the loop to catch it. |
| 02 | Memory Long term Not a context window. A persistent fabric holding how things connect, what they mean, why they follow, and when they changed — so a single thought crosses all four at once, and what it learned in March is still true in August. |
| 03 | Models Called on demand Language, vision, machine vision, vision-action — NeuroLog hosts them and routes each step to the cheapest one that can do the job. That routing is where the compute savings come from. |
01 · You ask A question, a drawing, a document, a stream. | 02 · It plans NeuroLog works out what is actually being asked and what it will take. | 03 · Memory answers Context, relationships, history — supplied, not re-derived. |
04 · Cheapest model runs Each step goes to the smallest thing that can do it. | 05 · You get the trail The answer, and the reasoning that produced it. |
Model-agnostic by design NeuroLog hosts any model and routes each step to the cheapest one that can do the job. Wrap the model you already own — the engine raises the ceiling on whatever it wraps. | Your instance only Learning stays inside your deployment. Nothing crosses between clients, and there is no central model collecting what your data taught it. | Where it runs On-premises, co-located, or cloud — down to phone-class hardware. The compute budget is a design input, not an afterthought. |
Relational The real links between people, accounts, and systems. | Semantic Reads intent, not just keywords. | Causal Reaches an answer it can defend. | Temporal Facts move. It tracks the movement. |
03 ProblemsThe four failures that stop AI being trusted with consequential work. Expand +
These are the bottlenecks that stop AI from being trusted with consequential work. Each one is a place we have built something that works.
A model can read a drawing and still not see it. It traces a line straight through another line. It cannot tell you what connects to what.
Why it fails Language models were built to predict text, not to hold a two-dimensional relationship in mind. Give one a schematic and it will describe the drawing convincingly while getting the topology wrong. | What we built An engineering single-line diagram tool that follows what actually connects to what, checks it against load and standard, and flags what does not add up. In development for permit review at volume. | Also applies to Construction inspection from a walking robot — is the stud spacing right, is that detail built to drawing. Same failure, different surface. |
The window closes and the work is gone. Load a long document and the middle of it quietly stops counting.
Why it fails A context window is a scratchpad, not a memory. Facts in the middle of a long input carry less weight than facts at either end, and nothing survives the session. | What we built Persistent memory holding relationships, meaning, causation, and time in one fabric. What the system learned in March is still true, still connected, and still auditable in August. | Also applies to Any workflow that rebuilds the same context every session — underwriting, diligence, revenue analysis across divisions. |
A fluent answer with nothing behind it. The failure is not that it is wrong. It is that it is wrong, confident, and unverifiable.
Why it fails A model generates the most plausible continuation. Plausibility is not provenance. Nothing in the architecture forces the answer back to a source. | What we built Reasoning that runs before the answer, not after. NeuroLog checks its own path, corrects it when it is wrong, and returns the trail it followed to get there. | Also applies to Anywhere a wrong answer has consequences — financial models, permit review, compliance, safety-critical inspection. |
A frontier model does work a far smaller model could have done, and you pay frontier prices for every token of it.
Why it fails Most stacks route everything to one large model because that is the only component they have. Nothing decides which steps actually need that much intelligence. | What we built NeuroLog plans the work first, then sends each step to the cheapest model that can do it — often a small vision or classification model rather than an LLM at all. | Also applies to Continuous workloads where cost compounds — monitoring, inspection, always-on classification across many sites. |
Ask for the side-by-side: the same task run on a standard model and on NeuroLog, with the token counts visible on both.
04 CostWhat it costs to keep re-sending your data, priced against published rates. Expand +
Every time you ask an AI a question about your data, the whole file goes into the prompt and you pay for it again. NeuroLog works the answer out first and hands the model the finished figure — same question, same model, same answer.
TokenMeter by NeuroLog | 1,000 questions per day |
| Model · input per 1M | LLM alone / yr | NeuroLog + LLM | You keep / yr |
|---|---|---|---|
| Opus 5 — $5.00 | $49,029 | $122.28 | $48,907 |
| Sonnet 5 — $2.00 | $19,612 | $48.91 | $19,563 |
| Haiku 4.5 — $1.00 | $9,806 | $24.46 | $9,781 |
Anthropic list prices for input tokens, read 2026-08-26. Published prices, not something we measured. Sonnet 5 is at its introductory rate; confirm the current card before you budget on it. Figures scale linearly — at 10,000 questions a day, multiply by ten.
Here is what each side sends to answer one question about the same file — a 266,257-token export, asked a single aggregate question over its rows.
| LLM alone | 266,257 tokens |
The entire file, re-sent with every question.
| NeuroLog + LLM | 67 tokens |
Still an AI answer — NeuroLog just hands the model the finished figure instead of the file. That green sliver is drawn to the same scale as the bar above: 0.03% as wide. Being almost invisible is the entire point.
A question NeuroLog can resolve against your data before the model is involved — an aggregate, a lookup, a comparison across rows — collapses to almost nothing. A question that genuinely requires the model to read the raw text will not. Most real workloads sit between the two, and the mix is yours, not ours.
Which is why we do not quote you a percentage. Use your own volume, and measure your own file.
The sample data is made up Real PJM column layout, invented rows, at realistic scale. The token counts measured over it are genuine; the rows are not. | We did not test the big vendors The measurement runs on gemma4:e4b locally. Their tokenizers and context limits differ, which changes the size of the gap. | We are not quoting a percentage The gap depends on your data size, your question mix, and your model. Your own numbers are the only honest ones. |
Send us a real export — a CSV, a JSON dump, a log — and we will measure what it actually costs you to keep re-sending it, on your questions and your volume.
05 AboutWhere we came from, the wall we hit, and who is on the work. Expand +
We set out to finish engineering projects. The tools could not hold up their end, so we built our own. Two years later that engine is the product.
01 — Where we came from An engineering shop, not a lab. Next-Gen was founded in 2024 by Steve Moore and Brock Pellerin to deliver customized engineered solutions — design, project management, and manufacturer representation across the energy and distribution markets. Real projects, real constraints, work that has to survive commissioning. |
02 — The wall We brought AI to that work. It could not keep up. It moved data fine. It could not read a drawing and tell us what connected to what. It forgot everything between sessions, so every engagement rebuilt the same context from scratch. And it could never explain why it answered the way it did — which is the one thing an engineer cannot sign off without. |
03 — What we did about it We stopped waiting for someone else to fix it. NeuroLog came out of client work, not a whiteboard. A reasoning engine with its own long-term memory that plans before it answers, routes each step to the cheapest model that can do it, and returns the trail it followed. We own it outright. The engineering practice still runs, and it is what keeps the technology honest. |
NeuroLog was not built by a research group in isolation. It was built by the disciplines that had to live with the failures — and every one of them is still on the work.
01 · Reasoning & architecture The engine itself — planning, self-correction, and the routing layer that decides which model does what. | 02 · Data science Graph and knowledge systems, causal discovery, and the evaluation work that decides what actually shipped. | 03 · Firmware & embedded Getting the engine onto constrained hardware at the edge, where the compute budget is fixed. |
04 · Hardware Sensors, prototypes, and build — the physical side of deployments that have to survive a site. | 05 · Engineering delivery Design, project management, and the client work that keeps surfacing the next hard problem. | 06 · Commercial & capital Licensing, pricing, partnerships, and how the company is capitalized and structured. |
Advisors drawn from industrial and semiconductor manufacturing, academic research, and go-to-market leadership in distribution.
Engineering design Electrical, controls, and systems, concept through IFC. | Project management Scope, schedule, and vendors on projects that cross trades. | Hardware & embedded Sensors, firmware, and prototypes taken to production. | Mobile infrastructure · patent pending Deployable power and edge systems. |
One short call tells us whether this is a reasoning problem, a memory problem, or an engineering one. We will say which.
Reasoning, memory, and engineering. Delaware LLC.