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BRAINIALL Industrial Autopilot · Full-stack AI
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107 m · Earth Planetary scale · illustrative
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107 10⁷ m Earth 104 10⁴ m Complex ROI Results DEMO How it works PROD Products CASES Cases MOTION Machines in motion 102 10² m Operations 100 10⁰ m Machine 10−3 10⁻³ m Sensor Endpoint · Return
Safeguards Platform Research Company Contact
SCALE INDEX · BRAINIALL

CORE STAGES & SCALES

  • 107 m Planetary Orbit · Earth
  • 104 m Multi-Industry Complex
  • ROI What it’s worth, in numbers
  • Demo How it works
  • Prod Three products in staircase
  • Cases Real plants, real results
  • Motion Machines in motion
  • 102 m Operational Domains
  • 100 m Machine Cutaway Atlas
  • 10−3 m Field Sensor Node
  • Network One industrial endpoint

SAFEGUARDS & ARCHITECTURE

  • 01 Autopilot safeguards
  • 02 Full-stack AI Platform
  • 03 Research & Evaluation
  • 04 Company & Ecosystem
  • 05 Plant Assessment Request
Book a plant assessment

Imagery: illustrative.

107 m / PLANETARY SCALE

Industrial AI, from insight to action.

Multi-Industry Complex · Scale 10⁴ m

Brainiall’s Autopilot reads your equipment and control systems, finds the best operating point and applies it — in energy, process industries, manufacturing and mining. Operators and safety interlocks stay in command.

+6% throughput in a 25-day AI-versus-manual test at Mineração Aurizona’s gold plant (CMOC Group) — published in Brasil Mineral #441.

Book a plant assessment See how the Autopilot works
Energy
Process industries
Manufacturing
Mining
DESCENT BY ORDERS OF MAGNITUDE
SCALE: PLANT ECONOMICS RESULTS PUBLISHED CASE

What it’s worth, in numbers.

+6%

more throughput from the same mill · 115,347 t under AI vs 109,079 t under manual, matched 265-hour windows

US$781K

incremental profit per year, net of AISC

+3%

even on hard ore

~2%

less energy per tonne · 20.7 vs 21.1 kWh/t, SoftwareOne published mining case

25 days

to prove it, in a controlled test

Mineração Aurizona gold plant in Maranhão, Brazil — a CMOC Group company since 2026 (test: Jan 10 – Feb 5, 2024). Published in Brasil Mineral #441, Jul 2024. Energy figure: SoftwareOne + AWS published case.

Example

What +6% would mean for your plant.

Projected annual value at +6% throughput
≈ US$ 1,500,000 more per year

Illustrative. Based on the published Mineração Aurizona result; the real gain depends on your process and is measured in a test at your own plant.

SCALE: DATA STREAM HOW IT WORKS CLOSED-LOOP AUTOPILOT

How we make a machine run better.

Like the autopilot of a plane: the pilot stays in command, and the autopilot makes many small corrections to keep the best course. We do the same for industrial machines.

SCALE: ARCHITECTURE STAIRCASE THREE PRODUCTS INCREMENTAL VALUE

Three products. Start with one, grow into the next.

Same mill, three levels of help — each one adds a layer.

1 Read 2 Test 3 Adjust

Animation: three sensors on the mill send readings: bearing load, temperature and acoustics. Their lines merge into a forecast; the uncertainty band widens and reaches the safe limit, which raises an early warning: liner wear rising — plan a stop. Illustrative, with simulated data.

Step 1 · Read

Predictive models

Know what’s coming.

WHAT IT IS

Forecast failures, quality and yield before they happen, from the data you already have.

WHAT IT’S FOR

Seeing trouble before it stops the machine: wear, quality drift, falling yield.

WHAT YOU GAIN

Time to act: plan maintenance and correct quality before it costs production.

EXAMPLES

Predictive maintenance for test cells · predictive models for biodiesel production

YOU RECEIVE

Early warnings and forecasts for your team.

Animation: a digital twin separates from the real mill and splits into three scenarios, A, B and C, each tested against a safe band. Scenario A leaves the safe limits and is dropped; scenario C gives the most output and is marked best inside safe limits. The recommendation goes to the real mill, and nothing is applied. Illustrative, with simulated data.

Step 2 · Test

Digital twin

Test before you touch.

WHAT IT IS

A live virtual copy of your equipment and process, updated with every reading. Run what-if scenarios safely.

WHAT IT’S FOR

Answering “what if?” — new ore, new demand, a new setting — before touching the plant.

WHAT YOU GAIN

Safe decisions: try changes on the copy, not on production.

YOU RECEIVE

The best settings for today’s conditions, recommended to your team.

Animation: a ring of light closes around the mill. A pulse runs from the sensor readings, through the digital twin, to a setting dial that moves one step inside the safe limits, and the adjustment log records it. A proof card shows +6% throughput in a 25-day pilot published in Brasil Mineral #441. An operator presses Return control; the ring turns grey and opens. Illustrative, with simulated data.

Step 3 · Adjust

Industrial Autopilot

The complete system.

WHAT IT IS

Reads, simulates and applies the best settings, continuously, through your control system — within limits your engineers approve.

WHAT IT’S FOR

Keeping the machine at its best point, all day, as conditions change.

WHAT YOU GAIN

More output with the same energy — +6% throughput in the published Mineração Aurizona case.

EXAMPLES

Gold mine (Mineração Aurizona) · Lithium & tantalum mine (AMG)

Each product is a step of the same system. The Autopilot includes the other two.

SCALE: REAL PLANTS CASES

REAL PLANTS.
REAL RESULTS.

What our AI did inside four industrial operations — the problem, what we deployed and what changed. Two are named; two are under NDA. Every number comes from a measured result or a published source.

Gold · Brazil +6% throughput Lithium & tantalum · Brazil +20–35 t of ore a day Biodiesel · South America 96.3% R² quality model Heavy equipment · Test cells In deployment predictive maintenance
+6% throughput, AI vs manual
Illustrative footage
GOLD · MARANHÃO, BRAZIL · 2024

Mineração Aurizona

A CMOC Group company

Gold mine and processing plant in Maranhão, Brazil. Since January 2026 it belongs to CMOC Group — the world’s largest cobalt producer and one of the top eight copper producers — as part of a US$1.015 billion acquisition.

The challenge

The SAG mill sets the pace of the whole plant. Under manual control, operators held the feed back whenever the circulating load spiked — while rock type, feed blend and percent solids kept changing underneath them.

What we deployed

A predictive model of the grinding circuit, built over two years with the plant’s process engineers, running the Industrial Autopilot. It reads live sensor data and sends setpoints to the plant’s automation system in real time, inside safe operating ranges, 24/7. One click hands control back to the operator.

Products used

  • Predictive models
  • Digital twin
  • Industrial Autopilot

How we measured

25 days, from January 10 to February 5, 2024. AI and manual control swapped every 48 hours, so both faced the same ores. Only hours with at least 85% operation counted — 265 hours in each mode.

Jan 10 Jan 31 Feb 5
AI · 13 days Manual · 12 days Maintenance — excluded

What changed

+6% throughput
435 t/h with AI vs 412 t/h manual: 115,347 t vs 109,079 t in matched 265-hour windows.
+3% on hard fresh rock
Ore with a Bond work index of 14 kWh/t or more — harder than the plant was designed for — still processed above design rate.
~2% less energy per tonne
With the same grind: particle size (p80) held steady.
≈ US$781K extra profit a year
≈ 2,694 more ounces of gold a year if the plant runs on hard fresh rock alone, net of all-in sustaining cost (projection from the test).

Results confirmed in writing by Mineração Aurizona in 2026: consistent results, all test criteria met.

Published in Brasil Mineral #441 (July 2024) and in SoftwareOne’s AWS case study.

Get this for your plant Read the full case
Two people in hard hats at the grinding mill of AMG Brasil’s Mibra plant, Minas Gerais
+20–35 t of ore a day
At the Mibra grinding mill, Minas Gerais
LITHIUM & TANTALUM · MINAS GERAIS, BRAZIL · 2025 AMG

AMG Brasil — Mibra mine

Part of AMG Critical Materials N.V.

The Mibra mine has operated since 1945, producing tantalum, niobium and lithium concentrates — the first link of AMG’s chain to battery-grade lithium hydroxide in Germany. AMG Critical Materials is listed on Euronext Amsterdam and had US$1.7 billion in revenue in 2025.

The challenge

In the grinding circuit, sudden rises in circulating load forced the feed conveyor to be cut back — often below 70 t/h — about seven times a day. Every cut was ore the mill could have processed.

What we deployed

A predictive model of the circulating load, driving the feed conveyor in real time. Instead of cutting the feed, the Autopilot sees the rise coming, holds a level the mill can absorb, then returns to maximum feed.

Products used

  • Predictive models
  • Digital twin
  • Industrial Autopilot

What changed May–July 2025

80%+ of feed cuts anticipated and avoided
In the first tests, with the software still in development.
+3.5 to 5 t of ore per event
More than manual control processed in the same situation.
+20 to 35 t of ore a day
At about seven events a day — with a steadier load, fewer stops and a better grind.
Get this for your plant Read the full case

“Integration was surprisingly fast — in less than two weeks we were already seeing real gains in the grinding.”

Marco Antonio, Automation & AI Engineer, AMG

Next step: controlling the mill’s water valves for even more stability.

IN PROGRESS · UNDER NDA

IN PROGRESS Illustrative
BIODIESEL · SOUTH AMERICA · 2025–2026

Global agribusiness & bioenergy producer

A global agribusiness and bioenergy producer (name under NDA), optimizing a biodiesel plant in South America.

The challenge

Maximize transesterification yield, cut the methylate catalyst — a multi-million-dollar annual cost line — and know product quality before the daily lab result.

What we deployed

Predictive models built on ten months of plant data: 56.5 million sensor readings from about 40 instruments, turned into a clean, model-ready data foundation.

Products used

  • Predictive models
  • Digital twin (not used in this case)
  • Industrial Autopilot (not used in this case)

What changed so far

96.3% R² virtual quality sensor
Predicts a key density-based quality proxy from 13 process variables, before the lab does.
Catalyst headroom found in the data
Distinct dosing patterns tied to glycerin quality: evidence that catalyst spend can come down without hurting quality.
Next yield prediction
Golden-batch analysis and models that keep learning as new data arrives.

Phase 1 delivered in February 2026 · Phase 2 in progress · figures preliminary

Read the full case
IN PROGRESS Illustrative
HEAVY EQUIPMENT · TEST CELLS · 2026

Global heavy-equipment manufacturer

A global heavy-equipment manufacturer (name under NDA), testing a critical driveline component on instrumented test cells — about 70 sensors each.

The challenge

When a test deviates, is it the bench or the component? And unplanned bench downtime stalls every test scheduled behind it.

What we’re deploying

Predictive-maintenance models and a monitoring dashboard. Instead of one global alarm threshold, the models learn a normal-behavior baseline for each component variant, so every deviation is scored against the right reference.

Products used

  • Predictive models
  • Digital twin (not used in this case)
  • Industrial Autopilot (not used in this case)

Delivered so far

  • On-site mapping of the benches and their data
  • Full telemetry history consolidated
  • Model variables selected
  • Training set ratified with the client’s maintenance leadership

Model training on schedule — May to September 2026

Read the full case
SOFTWAREONE + AWS

SoftwareOne’s published case describes Brainiall receiving plant sensor data, computing process parameters and making automatic adjustments through the plant’s supervisory system: about 2% less energy per tonne (20.7 vs 21.1 kWh/t) in the published mining case, a sales cycle cut from 8 to 3 months and cloud costs down about 50% via AWS NextGen ISV.

Your plant could be the next case.

Book a plant assessment
SCALE 100 m THREE-DIMENSIONAL SYSTEM SEVEN MACHINES

Seven machines. One system.

Watch the machines assemble. Drag to turn one, move through it, scatter it — then press Autopilot.

Machines drawn in particles — illustrative.

SCALE 102 m OPERATIONAL DOMAINS MULTI-INDUSTRY AUTOPILOT

FOUR INDUSTRIAL SECTORS.
ONE CONTINUOUS LOOP.

The same loop — read, test, adjust — in energy, process industries, manufacturing and mining.

SECTOR 01 · 102 m

ENERGY

Smarter grids, more efficient plants.

SECTOR 02 · 102 m

PROCESS INDUSTRIES

Reduce downtime, cut energy waste, increase yield.

SECTOR 03 · 102 m

MANUFACTURING

Pumps, compressors, motors and gearboxes.

SECTOR 04 · 102 m

MINING

Throughput optimization under feed variability.

01 / 04 SCALE 102 m · ENERGY

ENERGY

Smarter grids, more efficient plants.

Power generation, grid management, renewables — real-time asset monitoring and forecasting across the energy value chain.

Turbine TelemetryCONTINUOUS
Generator LoadMONITORED
Grid SyncACTIVE
Control LoopCLOSED
Simulated data for illustration
02 / 04 SCALE 102 m · PROCESS

PROCESS INDUSTRIES

Reduce downtime, cut energy waste, increase yield.

Reactors, furnaces, distillation, granulation; chemicals and biofuels.

Reflux BalanceSTABILIZED
Reboiler DutyOPTIMIZED
Purity TargetON-SPEC
Process LoopACTIVE
Simulated data for illustration
03 / 04 SCALE 102 m · MANUFACTURING

MANUFACTURING

Pumps, compressors, motors and gearboxes.

Machining lines and test benches.

Dyno SensorCONNECTED
Motor CurrentNORMAL
VibrationSTEADY
Equipment StatusONLINE
Simulated data for illustration
04 / 04 SCALE 102 m · MINING

MINING

Throughput optimization under ore hardness variation.

Grinding, flotation, leaching, crushing, tailings.

Mill LoadBALANCED
Feed RateDYNAMIC
Slurry DensitySTABLE
Target ResultOPTIMAL
Simulated data for illustration
SCALE 100 m MACHINE ATLAS IN CUTAWAY
MACHINE 1 / 7 · ENERGY

Steam turbine & generator

Steam turbine & generator cutaway render
1 2 3
1 INSTRUMENTATION

Sensors

Vibration, bearing and exhaust temperatures, steam pressure, load.

2 INFERENCE LOOP

What the AI does

Compares live behaviour with the twin, flags drift early and recommends the most efficient operating point within limits.

3 OBJECTIVE

Goal

Fewer unplanned stops, better efficiency.

MACHINE 2 / 7 · ENERGY · RENEWABLES

Wind turbine nacelle

Wind turbine nacelle cutaway render
1 2 3
1 INSTRUMENTATION

Sensors

Gearbox and main-bearing vibration, generator temperature, pitch, yaw, power curve.

2 INFERENCE LOOP

What the AI does

Spots early wear patterns and checks pitch and yaw against the power curve.

3 OBJECTIVE

Goal

Less downtime, more energy captured.

MACHINE 3 / 7 · MANUFACTURING & MACHINES

Centrifugal pump & motor

Centrifugal pump & motor cutaway render
1 2 3
1 INSTRUMENTATION

Sensors

Vibration, motor current, temperatures, suction and discharge pressure, flow.

2 INFERENCE LOOP

What the AI does

Detects anomalies such as cavitation or misalignment before they become failures and keeps the pump near its best operating point.

3 OBJECTIVE

Goal

Uptime and energy efficiency.

MACHINE 4 / 7 · PROCESS INDUSTRIES

Stirred-tank reactor

Stirred-tank reactor cutaway render
1 2 3
1 INSTRUMENTATION

Sensors

Temperature profile, pressure, feed ratio, agitator power.

2 INFERENCE LOOP

What the AI does

Holds the reaction at its best temperature and feed ratio as conditions change, within safety limits.

3 OBJECTIVE

Goal

Consistent yield and quality.

MACHINE 5 / 7 · PROCESS INDUSTRIES

Distillation column

Distillation column cutaway render
1 2 3
1 INSTRUMENTATION

Sensors

Tray temperatures, reflux, reboiler duty, pressure.

2 INFERENCE LOOP

What the AI does

Balances reflux and reboiler duty for on-spec purity with less energy.

3 OBJECTIVE

Goal

On-spec product, lower energy.

MACHINE 6 / 7 · MANUFACTURING & MACHINES

Test bench — motor · torque converter · dynamometer

Test bench — motor · torque converter · dynamometer cutaway render
1 2 3
1 INSTRUMENTATION

Sensors

Torque, speed, oil-circuit temperature and pressure, vibration.

2 INFERENCE LOOP

What the AI does

Traces each deviation to its origin — the bench or the product under test.

3 OBJECTIVE

Goal

Fewer failed tests, faster root cause.

MACHINE 7 / 7 · MINING & MINERALS

Ball mill

Ball mill cutaway render
1 2 3
1 INSTRUMENTATION

Sensors

Motor power, mill load, bearing pressure, feed rate, water addition, density.

2 INFERENCE LOOP

What the AI does

Adjusts feed and water in real time to maximize tons per hour within power and load limits.

3 OBJECTIVE

Goal

More throughput at the same energy.

SCALE ZOOM 100 m → 10−3 m MACHINE TO SENSOR TO DATA

10−3 m · FIELD SENSOR

From physical steel to continuous numerical streams.

Voltages, pulses, pressures and temperatures become live data — the input of the Autopilot’s closed loop.

SCALE RETURN: 10−3 m → 107 m FULL-STACK AI PLATFORM

FULL-STACK AI.
ONE INDUSTRIAL ENDPOINT.

Customers buy an operating result. Behind the endpoint: one or several models, our adaptations, agents and industrial software — improved together.

Energy

Process industries

Manufacturing & machines

Mining & minerals

SAFETY INTERLOCKS SITE-APPROVED BOUNDARIES IEC 61508 SIS UNTOUCHED

BOUNDED BY DESIGN.
OPERATORS IN COMMAND.

01

Bounded by design.

Min/max limits are configured per site and approved by your engineers. Outside them, anomalous readings return control to manual.

02

Auditable every step.

Existing IEC 61508 safety instrumented systems remain untouched and independent. Network segmentation follows IEC 62443. Every setpoint change is logged.

03

Operator in command.

The AI assists and never overrides. One click returns any parameter to manual.

COMPATIBLE INDUSTRIAL CONTROLS: Works with Siemens, ABB, Honeywell and Rockwell control systems.
ARCHITECTURE ENSEMBLES VISION

NOT ONE MODEL —
AN ENSEMBLE.

Classical machine learning, deep neural networks, transformers and LLMs work together with our industrial software, harnesses and scaffolding.

MULTIMODAL INGESTION
HIGH-PRECISION VISION

Images, too.

Camera and inspection images, with high-precision image ingestion, join sensor data in the same loop.

LAYER 04

Industrial software

Autopilot, digital twins and asset workflows.

SCADA · DCS · Historian
LAYER 03

Agent systems

Orchestration, tools, evaluations and harnesses.

Evaluation Loop · Agents
LAYER 02

Models & architectures

Architecture R&D, pretraining research and adaptation.

Surrogates · Time-Series
LAYER 01

Training & inference

Training methods, serving and optimization.

GPUs · Evaluation Loop

A shared evaluation loop connects every layer.

One or several models, our adaptations, agents and industrial software — improved together.

Connectors for industrial protocols.

Connectors for industrial protocols such as Modbus and automated data preparation — built so each new plant needs less engineering.

RESEARCH LAB INTERNAL EVALUATION

PRE-RELEASE PROPRIETARY SYSTEM ·
INTERNAL EVALUATION.

Pre-release proprietary AI system — on an internal software-engineering evaluation, our adapted system solved 73.0% of tasks versus 60.4% for the same base model: +12.6 percentage points (111 tasks, one seed). Internal evaluation, not a public leaderboard.

Brainiall Adapted AI System 73.0%
Same base model 60.4%
+12.6 PERCENTAGE POINTS (111 TASKS, ONE SEED)
73.0% vs 60.4% INTERNAL EVALUATION, NOT A PUBLIC LEADERBOARD

Model architecture and adaptation

Agent harnesses and evaluation

Time-series and process models

Inference optimization on GPUs

Member of NVIDIA Inception.
FOUNDATION COMPANY & ECOSYSTEM

ONE COMPLETE SYSTEM.
SINCE 2019.

Brainiall builds industrial intelligence as a complete system, served as one endpoint.

AWS
NVIDIA Inception
SoftwareOne
AMG Lithium
DEVELOPER ECOSYSTEM

Specialist AI APIs for developers → brainiall.com/en/apis

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