Sydney, Australia

Intelligence that learns
from every interaction
and remembers everything

A proprietary neural engine that doesn't train on datasets. It learns in real time from every interaction — and gets sharper with each one. Computer vision. Field data collection. Asset intelligence. Environmental profiling. Connectivity. No third-party APIs. No external models. Your data never leaves your systems. Our intelligence never stops compounding.

Start a conversation See what's different
What Makes Us Different

Not standard ML.
Not a wrapper on someone else's model.

Standard machine learning needs massive datasets, weeks of training, and constant retraining when reality drifts. Ours doesn't. Built on associative neural memory, it learns from every interaction in real time — stores patterns, recalls them from partial or damaged input, and gets more accurate with every scan. No training datasets. No batch jobs. No third-party APIs. First interaction to last.

Proprietary Technology

Associative Neural Engine

Standard models get trained, frozen, and slowly go stale. Ours stores patterns as it encounters them — and recalls the closest match when it sees partial, damaged, or incomplete data. Not guessing from statistical averages. Recalling what it's actually seen. The engine runs entirely on our infrastructure. No OpenAI. No Google. No external providers. We built it. We own it. It improves with every interaction and never needs retraining.

01

Real-time learning, not batch training

One interaction creates a retrievable pattern. The next one strengthens it. No thousand-example minimum. No training pipelines. No GPU clusters churning overnight. The engine learns the moment data arrives and is immediately better for the next request.

02

Pattern completion from partial input

Three fields out of twelve? It recalls the other nine. Corrupted data, missing values, misread characters — the engine matches against stored patterns and fills the gaps. Not guessing. Recalling. The more it sees, the less it needs to see.

03

Environmental & condition intelligence

Every interaction captures context — location, atmosphere, condition, time. Correlate thousands of these and you get deterioration trends, risk factors, outlier detection, lifecycle patterns. Not generic models pulled from a textbook. Evidence from actual observations in actual environments.

04

Collect. Process. Deliver.

Our platforms handle the field work — scans, surveys, inspections, commissioning, condition assessments. The engine processes everything and delivers structured, validated, enriched results via API. Into whatever systems you already run. You store the data. We make it intelligent.

See It Work

Feed it data it's never seen.
Watch what it figures out.

01

Unknown data arrives

500 records. 10 fields. The engine has never seen this dataset. No training. No schema. No configuration.

02

Structure discovered

It identifies field types, value ranges, and relationships automatically. Nobody told it what anything means.

03

Patterns learned

Every record becomes a retrievable pattern. 89 distinct patterns found in 500 records. In real time. No batch processing.

04

Missing data completed

Record #501 arrives with 4 of 10 fields. The engine completes the other 6. Not guessing. Recalling.

05

Intelligence compounds

Anomalies found. Correlations discovered. Accuracy climbing. And this is just 500 records.

neural engine — live
> ingesting dataset... 500 records, 10 fields
> no schema provided. no training data. discovering structure...
> field_1 identifier  alphanumeric, unique
> field_2 category  12 distinct values
> field_3 numeric  range 14–450, continuous
> field_4 reference  manufacturer code pattern
> field_5 numeric  range 0–100, percentage
> field_6 category  6 distinct values
> field_7 numeric  range 4–24, discrete steps
> field_8 serial  8-char alphanumeric
> field_9 category  4 distinct values
> field_10 score  range 1.0–10.0
◆ learning patterns in real time...
◆ record 50 23 patterns stored
◆ record 100 41 patterns  field relationships emerging
◆ record 250 67 patterns  correlations strengthening
◆ record 500 89 patterns  structure fully mapped
> record #501 arrives — 4 of 10 fields populated
> field_1 XR-7200
> field_2 [MISSING]
> field_3 340
> field_4 [MISSING]
> field_5 [MISSING]
> field_6 Type-C
> field_7 [MISSING]
> field_8 [MISSING]
> field_9 [MISSING]
> field_10 [MISSING]
◆ neural match — pattern #47 — similarity 0.91
+ field_2 Industrial  recalled
+ field_4 MFR-KSB-2200  recalled
+ field_5 87  recalled
+ field_7 18  recalled
+ field_8 KP220034  recalled
+ field_9 Zone-2  recalled
> 10 of 10 fields — confidence 0.93
> accuracy trend over 500 records:
> records 1–100 82%
> records 100–250 91%
> records 250–500 97%
◆ anomalies detected:
! record #167 field_3 outside learned range for category
! record #302 field combination never seen in pattern space
! record #445 field_5 inconsistent with field_3 correlation
◆ correlations discovered:
◆ field_3 > 300 → field_9 = Zone-2  94% confidence
◆ field_6 = Type-C → field_7 range 16–20  91% confidence
> the engine was never told what this data is.
> it learned the structure, patterns, and relationships
> from the data itself. point it at anything.
How It Works

Five steps. Under 30 seconds.

From raw capture to structured, enriched, validated output.

01

Capture

Camera, field bus, file upload, or API. Any input source.

02

Extract

Vision pipeline reads text, identifies features, pulls structured data.

03

Match

Neural engine finds the closest stored pattern. Corrects errors. Fills gaps.

04

Enrich

Environmental profiling, condition scoring, product validation. Context layered in.

05

Deliver

Structured results via API. JSON, CSV, PDF. Into your systems.

Capabilities

What we build. What we ship.

Production systems. Field-tested. From raw data capture through to intelligence delivery.

001

Computer Vision & Data Extraction

Point a camera at a label, nameplate, sign, or document. The vision pipeline extracts structured data, identifies manufacturers from visual features, and validates against product databases. Corroded, angled, partially obscured — doesn't matter. Neural pattern recall handles the rest. 97%+ accuracy in real field conditions.

002

Field Data Collection & Datalling

Large-scale capture campaigns. Asset registers, equipment surveys, site audits, commissioning records, compliance documentation. Thousands of data points across multiple sites. GPS-tagged, photo-evidenced, validated on capture. The neural engine enriches and corrects data as it flows in — catching errors your field teams won't.

003

Protocol-Level Connectivity

Direct integration with master stations, field bus networks, and industrial protocols. Real-time data collection across multiple standards. Bulk acquisition engineered for minimal load on production networks. Live monitoring, analytics, and event-driven processing from the systems you already run.

004

Condition & Environmental Intelligence

Visual condition assessment. Deterioration tracking. Environmental profiling. Every interaction captures location, atmospheric conditions, corrosion risk, exposure levels. Scored, trended over time, compared against baselines. Environmental factors correlated with real outcomes across entire fleets and portfolios.

005

Privacy-First Processing

Identifying information separated before processing. Restored in the results. Encrypted in transit. Nothing passes through third parties. Operational data not retained after processing. Built for environments where data sensitivity isn't a feature — it's a requirement.

006

Mobile & Field Platforms

Native iOS and web applications designed for where the work actually happens. Full offline capture — stores locally, queues intelligently, syncs and processes when signal returns. GPS-tagged. Photo-evidenced. Built for remote sites, underground, offshore, and anywhere connectivity is a luxury.

The Bigger Picture

The engine isn't the product.
The data it builds is.

Associative memory that learns from every interaction, correlates environments with outcomes, and compounds over time. The technology applies anywhere you need to identify, assess, track, and predict. And the data gets more valuable with every use.

◆

Compounding Intelligence

Standard software captures data. This system gets smarter from it. After 100 scans, it corrects errors. After 1,000, it predicts what's missing before you notice. After 10,000, it surfaces patterns across sites, environments, and timeframes that no human analysis could find. Every interaction makes every future interaction more valuable. This isn't linear improvement. It compounds.

◆

Historical Profiling

Every interaction is timestamped, location-tagged, and condition-scored. Over time, you build a real history — not theoretical models, but observed changes in real environments. The system starts answering questions that didn't exist when the first data point was captured.

◆

Environment × Outcome

Every location gets an environmental profile — atmosphere, exposure, climate. Correlate that with outcomes across thousands of data points and you get evidence-based predictions for specific conditions. Not generic manufacturer tables. Real patterns from real observations.

◆

Behavioural Pattern Recognition

The neural memory stores relationships between data, not just the data itself. Certain combinations of attributes, conditions, and environments correlate with specific outcomes. Equipment reliability. Facility management. Logistics. Compliance. Safety. The patterns emerge automatically as data accumulates.

◆

Identity From Partial Information

Show it an incomplete picture. It recalls the rest. A damaged label with three readable fields out of twelve — it completes the other nine from stored patterns. Equipment, products, materials, locations, profiles. Anything with structured attributes. Partial input, complete output.

◆

Knowledge Preservation

When experienced people leave, their knowledge walks out the door. Every interaction with our system captures that knowledge as patterns in the neural memory. What works. What doesn't. What goes where. What to watch for. Institutional memory that doesn't retire, forget, or hand in a resignation letter.

◆

Cross-Domain Benchmarking

Anonymised aggregate data across deployments creates something nobody else has: context at scale. How do your outcomes compare to similar conditions elsewhere? Are you ahead or behind? What's normal? What's an outlier? Industry-level intelligence from real operational data — not surveys or manufacturer spec sheets.

◆

Predictive Lifecycle Intelligence

Model actual lifecycles from real data. Not “the manual says 15 years.” Instead: “this type, in this environment, with this usage pattern, reaches this state after N years — based on 500 real observations.” Decisions built on evidence. Not assumptions.

◆

Supply Chain & Procurement

Know exactly what's deployed, where, how old, and how it's performing. Forecast replacements before they become emergencies. Identify which products deliver the best long-term value in specific conditions. Operational data becomes procurement intelligence.

◆

Beyond Industrial

We started in industrial because that's where the first problem was. But associative memory that learns from interactions, profiles environments, and completes partial data from stored patterns? That applies to any domain where you identify, track, assess, and predict. Facility management. Logistics. Agriculture. Infrastructure. Healthcare equipment. Fleet operations. Compliance. The engine doesn't care what it's learning about. Point it at a domain and it gets better at it.

Our Platform

TORQ

Production

Field intelligence platform. Started with valve actuators. Expanding into everything the neural engine can learn from.

Scan → Structured Record

Photograph any label, nameplate, or identifier. The vision pipeline reads text, identifies manufacturers from visual features, validates against product databases — 400+ products, 20+ manufacturers and growing. Complete structured record in under 30 seconds. Corroded, angled, partially obscured. Doesn't matter.

Large-Scale Data Collection

Purpose-built for datalling campaigns. Thousands of records across multiple sites. Asset registers from scratch. Commissioning data. Compliance surveys. The neural engine validates and enriches as data is captured — catching errors and filling gaps that manual processes miss entirely.

Self-Correcting Intelligence

Misread data gets corrected against known patterns. Missing fields get completed. Inconsistencies get flagged. The more data flowing through, the sharper the corrections — across your entire operation, not just individual records.

Environmental & Condition Profiling

Every GPS-tagged interaction triggers environmental analysis — atmospheric exposure, humidity, UV, corrosion risk. Condition assessed visually alongside data capture. Scored, trended over time, correlated with environmental factors. A real picture of what's happening, where, and why.

Privacy by Design

Identifying information stripped before processing. Restored in results. Operational data not retained. Nothing touches third-party services. Built for environments where data sensitivity is a hard requirement, not a preference.

Built for the Field

Native iOS. Full offline capability. Captures and stores locally, queues for processing when signal returns. GPS-tagged. Photo capture. Remote sites, underground, offshore — anywhere the work happens and the signal doesn't.

Expanding Scope

TORQ started with valve actuators because that's where the first job was. The engine underneath doesn't care what it's learning about. Same vision pipeline, same neural memory, same environmental profiling — applied to any asset, equipment, or data category. New domains added as the platform grows.

torq — service status
> engine associative neural memory
> learning real-time per interaction
> training_datasets none required
> vision_pipeline multi-stage / proprietary
> field_accuracy 97%+
> capture_to_result < 30 seconds
> data_storage your systems
> external_apis zero
> data_privacy built in — not bolted on
> scope expanding
>
About

Sydney-based.
Engineering-led.

Australian technology company. We build intelligent systems that learn from data rather than being trained on it. Field services, data collection, computer vision, connectivity, environmental intelligence. Current deployments across oil & gas, water, power, mining, and marine — with the technology applicable well beyond.

We don't wrap third-party APIs in a dashboard and call it innovation. We built a proprietary neural engine from the ground up. It stores patterns from every interaction, recalls them to correct errors, completes missing data, and surfaces insights that compound over time. No external dependencies. Everything runs on our infrastructure.

Large-scale datalling campaigns. Asset registers from scratch. Commissioning platforms. Field service systems. Live monitoring and connectivity. The neural engine sits behind all of it — getting sharper with every piece of data that flows through.

You store your data. We make it intelligent.

Associative Memory Computer Vision Field Data Collection Connectivity iOS Zero External APIs Privacy-First Real-Time Learning
Integration
Any API
Delivered into your existing systems
Field Accuracy
97%+
And improving with every scan
Processing
< 30s
Photo to structured record
External Dependencies
0
No third-party AI. No external APIs.
Contact

Let's build something

Start a conversation

Intelligence that learns from your data. Delivered into your existing systems. No lock-in.

[email protected]

Sydney, Australia