ASURA Neural Execution Engine

Clone Any Employee.
Execute Any Task.

One neural kernel with two brains. A quantum execution engine that routes any task through 42 agents across 7 departments, and an employee cloning engine that models any role in a 1,000,000-profile corpus — then tells you whether to clone, train, promote or replace it, and what that decision is worth.

No install · Runs in the browser · 26 neurons · ReLU
@ASURA_Kernel
HL1 26 · ReLU   HL2 128
synapses 0
Live decision
0
Phases
0
Agents
0
Departments
0
Jobs Cloned1
0
Salary Pool2
What it does

Three things, measured.

Every ASURA decision is computed from inputs you supply — no random numbers, no black box. The same figure that appears on screen is the one written to your audit log.

01 / CLONE

Clone any employee

Describe a role by seven signals — skill, salary distance, experience, job fit, culture fit, performance, productivity. ASURA runs 26 phases and a 26-neuron ReLU pass, then returns one of six verdicts with a confidence score.

Clone · Fire · Replace · Train · Promote · Observe
02 / SAVE

Cut the cost of a function

30–40%

Modeled reduction in the fully loaded cost of a function, net of agent runtime, the human review the work still needs, and amortised implementation. A reference 17-role function models at 31.9%.

Modeled from your inputs3 · not observed payroll
03 / RETURN

Return on the AI spend

140–200%

Net annual benefit over what the programme costs to run, with payback typically inside two months. Every assumption behind it is published and versioned.

See methodology4 below
Live demo · no signup

Run the real engine.

This demo is not a video. It computes in your browser with the same formulas the platform runs server-side — real Shannon entropy over your text, real keyword routing, the same softmax over the same seven signals.

PLANACTVERIFY FIXREPEAT
Press execute to route this task

How these numbers are defined

1   Jobs cloned
Size of the role corpus ASURA models — 1,000,000 job profiles across 35 companies, 41 universities, 143 skills and 39 countries. It is catalogue coverage, not a count of seats replaced at customers.
2   Salary pool
$169,880,974,155 — the aggregate annual salary represented by that corpus. It is the size of the addressable pool, not revenue and not realised saving.
3   Cost reduction
Base salary × 1.30 to reach fully loaded cost, × a per-role automation coverage capped at 0.75 — then less agent runtime, less the human review the delegated work still needs, less amortised implementation. Coverage is per role and is never 100%: judgement, accountability and relationship ownership are not modeled as transferable. Every constant is published live at /api/economics/assumptions.
4   ROI
Net annual benefit divided by what the programme costs to run. A $150,000 backend engineer at 52% coverage models to $65,040 net and 179% ROI. This replaces an earlier figure of 1,775,106×, which divided a whole salary corpus by a runtime cost and was not a defensible number. These are models, not audited outcomes.
    Decisions
Fire and Replace verdicts are recorded as proposals requiring human approval. ASURA does not execute employment actions.
Architecture

Two brains, one kernel.

The same 26-neuron ReLU core serves both engines. One routes work. One values people. They share memory, governance and the audit trail.

Version 01

Quantum AI & Alien
Intelligence Engine

Routes any task through a five-stage loop and seven graph nodes, assigning named agents from 7 departments or 16 engineering roles by measured keyword signal.

Departments
7
Agents
42
Tech roles
16
Graph nodes
7
PlanActVerify FixRepeat Quantum qubitShannon entropy EinsteinNewton TeslaBohr
Version 02

Employee Cloning
Technology

Runs 26 phases over a role, performs a real ReLU forward pass, and returns a softmax verdict with the modeled economics attached to it.

Phases
26
Corpus roles
1,000,000
Companies
35
Universities
41
Skills
143
Countries
39
CloneFireReplace TrainPromoteObserve Audit trailHuman approval gate

Open the brain.

Create a workspace and run your own task through the engine. Every action is written to a per-tenant audit log you can read back.