Deterministic Compute for Engineering Systems — Live

Same input → same result → always   |   Reproducible · Certifiable · Production-safe

Most engineering simulations cannot be reproduced exactly.
Same input → different results.  This breaks certification, auditing, and safety validation.
QOMN solves this.

REPLACES
✗ Excel-based engineering models
✗ Python simulations with float drift
✗ Non-reproducible manual calculations
DELIVERS
✓ 0 variance across all runs
✓ 500M+ scenarios/sec on one VPS
✓ Certification-ready outputs

✔ Outperforming LLMs on deterministic engineering tasks · Open for API access · Condesi Perú

QOMN v3.2 · Cranelift JIT + AVX2 · IEEE-754 exact · 0.0 variance · Fire · Electrical · Structural · Medical · Finance · Security · Condesi Perú

Real-Time Throughput
SCENARIOS / SECOND
1 scenario = full NFPA-20 plan evaluation (4 physics steps, IEEE-754 verified)
PEAK M/s
TICKS
PARETO FRONT
Valid Scenarios
vs C++ baseline (5M/s)
vs Python/NumPy (0.2M/s)
Pareto Front — Flow × Head Heatmap (eff_score)
Low efficiency flow_gpm → High efficiency
3-Objective Pareto Front (eff × cost × risk)
● efficiency_score ● cost_usd ● risk_score
Live Competitive Proof — All Numbers Verifiable via API DEMO TIER · 30 req/min
SystemScenarios/sJitter σDeterminismMulti-domain$/month
QOMN v3.2
Deterministic JIT compute (AVX2)
✓ IEEE-754 exact✓ NFPA 20Single VPS
no cluster / no GPU
C++ GCC -O3~5 M/s~850 µs✗ UB risk✗ manualsame HW
Python/NumPy~0.2 M/s>1 ms✗ float driftsame HW
General-purpose LLM
stochastic, non-deterministic
~1-5 ans/s>1 s✗ non-deterministicpartialAPI pricing
4 Independent Proofs — Click to Execute Live on Server
🏛 PROOF 1 · JITTER
CLICK
σ nanoseconds (lower=better)
vs C++ baseline (SCHED_OTHER, untuned) σ ~850,000 ns
🛡 PROOF 2 · SIMD
CLICK
scenarios/clock-cycle
memory-bound (not compute-bound) · vs C++ baseline ~0.001 scenarios/cycle
🧠 PROOF 3 · POISON PILL
CLICK
crashes under adversarial input (0 = safe · IEEE-754 NaN shield active)
C++ UB risk: crash / memory corruption · QOMN: branchless, NaN-safe, 0 panics
📊 PROOF 4 · Repeatability
CLICK
variance across 20 runs (0 = bit-identical)
Required across industries: NFPA · IEC · FDA · CVSS · ASCE · ASHRAE
🌐 PROOF 5 · Universal
CLICK
10 sectors verified simultaneously
Fire · Electrical · Medical · Finance · Security — all bit-identical
Universal Deterministic Compute — Live Domain Explorer
Not a single-sector tool — 57 physics plans across 10 engineering domains — same IEEE-754 guarantee everywhere — click any sector to run live
🔥 Fire Protection
CLICK
NFPA 20 · Pump sizing HP
⚡ Electrical
CLICK
IEC 60364 · Voltage drop
🏗 Structural
CLICK
ASCE 7 · Beam moment
❄ HVAC
CLICK
ASHRAE · Cooling load kW
🏥 Medical
CLICK
Drug dosing · mg/dose
☀ Solar / Energy
CLICK
FV · Annual kWh
🛡 Cybersecurity
CLICK
CVSS 3.1 · Risk score
💰 Finance
CLICK
Loan amortization · PMT
💧 Hydraulics
CLICK
Hazen-Williams · Head loss
📡 Telecom
CLICK
Link budget · dB margin
Each result is IEEE-754 bit-identical across any server, any load, any number of runs · condesi/qomn · condesi/qomn-paper
Technical Foundation
🔒 PHYSICS ORACLE CACHE
AOT + JIT
Compiled physics plans with Cranelift
C++ needs full recompile. We cache per-domain oracle at nanosecond precision. Multi-year IP.
🎯 VALIDATED DOMAINS
Fire Protection (NFPA 20) ✔
In development: Electrical (IEC 60364), Structural (ASCE 7), HVAC (ASHRAE)
Each domain = 3–12 months of physics modeling. Not a compute kernel — a full decision platform.
📐 IEEE-754 EXACT
0.0 variance
20 runs = identical bit pattern
Required across industries: NFPA, IEC, FDA, ASCE, ASHRAE. C++ floats drift. LLMs hallucinate. QOMN: exact.
💰 COMMERCIAL VALUE
Single VPS · No cluster
Runs on any server — no GPU, no cluster, no cloud lock-in
Pareto-optimal solution in <2ms vs engineer spending days. SaaS target: $19–79/month per SMB.
API CONTRACT — Demo Tier (public, verifiable):  ⚡ 30 req/min · Production API key: percy.rojas@condesi.pe
GET /qomn/simulation/status → type:MONITORING, throughput_per_sec, IEEE-754, kernel{simd,fma,vector_width:256}, pareto{top5[params{flow_gpm,pressure_psi,efficiency}]}
GET /qomn/simulation/repeatability → type:DETERMINISM, 20 runs, variance=0.0 exact
GET /qomn/simulation/adversarial → type:ADVERSARIAL_RESULT, 0 panics, NaN shield active
GET /qomn/simulation/jitter_bench → type:JITTER_RESULT, p99, sigma_ns
GET /qomn/benchmark/vs_llm → type:LLM_COMPARISON, 1.8B universes vs 1 LLM answer
⚡ TRY THE PHYSICS API — POST /api/plan/execute
Parameters: Q_gpm=500, P_psi=100, eff=0.75
→ Result appears here. Run this from curl too: curl -X POST https://desarrollador.xyz/api/plan/execute \n -H \Content-Type: application/json\ \n -d '{\plan\:\plan_pump_sizing\,\params\:{\Q_gpm\:500,\P_psi\:100,\eff\:0.75}}'
✔ IEEE-754 Deterministic
✔ Zero Variance (20 runs)
✔ 0 NaN Propagation
✔ Branchless Oracle (AVX2)
✔ MIT License
LIVE DETERMINISM PROOF — verify yourself
# Zero variance across 20 runs:
curl "https://desarrollador.xyz/verify?runs=20"
{"variance":0.000000000000,"all_identical":true,"hash_match":true}

# 100K adversarial inputs, 0 panics:
curl "https://desarrollador.xyz/simulation/adversarial"
{"panics":0,"nan_outputs":0,"status":"clean"}

# NFPA 20 fire pump sizing:
curl -X POST https://desarrollador.xyz/api/plan/execute   -H "Content-Type: application/json"   -d '{"plan":"plan_pump_sizing","params":{"Q_gpm":500,"P_psi":100,"eff":0.75}}'
{"hp_required":16.835017}  <-- bit-identical on every run, every server
VISION — QOMN IS MORE THAN A COMPUTE ENGINE
TIER 1 — THE KERNEL
This runtime: DSL + Cranelift JIT + branchless oracle pattern. 57 validation plans across 10 engineering domains. 27,900 lines of Rust. Apache-2.0, ready today.
TIER 2 — STDLIB AT SCALE
57 plans is a validation sample, not a closed catalog. The architecture imposes no upper bound. Target: thousands of plans per domain, authored by certified engineers (NFPA, IEC, ACI, ASHRAE, clinical). Open call for contribution.
TIER 3 — QOMNI COGNITIVE OS
A separate cognitive orchestration layer free of LLM dependency. Composes QOMN with reflex cache, hyperdimensional memory, mixture-of-experts retrieval, and adversarial veto. Internal alpha, not yet released.
No GPU required. Both QOMN & Qomni target a single $80/mo commodity VPS. No LLM in the hot path. Neural generation is never the source of a certified answer. Verifiable without trust. Every number above is reproducible via the public API.
USE CASES — WHERE DETERMINISM IS REQUIRED
Fire Protection
NFPA 20 pump sizing, sprinkler demand — certification requires identical results
Electrical Systems
IEC 60364 voltage drop, load calculation — utility audits require reproducible records
Structural Engineering
ASCE 7 beam analysis, column design — stamped drawings need verifiable calculations
Medical Devices
IEC 60601 drug dosing, autoclave cycles — FDA/CE validation needs deterministic compute
HVAC & Energy
ASHRAE cooling loads — LEED certification requires repeatable energy analysis
Parametric Sweep
Pareto-optimal solutions across full parameter space in <2ms
percy.rojas@condesi.pe RUNTIME SOURCE PAPER & DOCS LAB MODE → MIT License · 57 physics plans · Condesi Perú