From industrial validation to commercial scale

Turn company knowledge into governed AI decisions.

AIMAILABS closes the last-mile gap between powerful AI models and real industrial execution. We compile procedures, standards, evidence requirements and approval rules into traceable workflows that keep experts in control.

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Working Inspecta application PETRONAS SKGAS workflow validation PETRONAS iG@P Tech Catalogue listing OTC Asia 2026 · OnePetro
AI model layer Vision · Language · Extraction

Models interpret unstructured evidence, but do not control the final operational decision.

Organisation inputs Evidence + Company Knowledge

Photos, documents, sensors, procedures, matrices, standards and expert judgement.

AIMAILABS core
Decision Intelligence Platform

Probabilistic evidence understanding before the boundary. Governed execution after it.

Operational Knowledge Compiler
Governed Decision Runtime
Control layer Human Governance

Approval authority, escalation, uncertainty handling and complete traceability.

Operational outcome Trusted Decision Packages

Assessments, priorities, approvals, recommendations and audit-ready records.

The industrial AI gap

Industries do not lack AI.
They lack a fast way to put it to work.

Operating costs rise, productivity is pressured and safety decisions must remain defensible. Yet every new use case still tends to trigger another consultancy study, hackathon or custom software build.

Higher operating costs

Fuel, labour, maintenance, logistics and downtime continue to pressure margins.

Productivity pressure

Teams lose time moving evidence across fragmented tools, spreadsheets and reports.

Safety and compliance exposure

Inconsistent checks, weak traceability and delayed approvals create operational risk.

Knowledge trapped in people

Critical know-how remains scattered across SOPs, standards, past decisions and experienced staff.

Operational problemA cost, safety, reliability or productivity issue is identified.
→
Consultant or hackathonTeams restate the problem and reconstruct domain context.
→
Custom buildMonths of development, integration and repeated clarification.
→
PilotA single use case is tested, often with limited reuse.
→
Uncertain scaleThe next workflow starts another custom project.
The AIMAILABS approach

We do not ask an LLM to make the decision.

AI models help structure evidence. AIMAILABS then validates what is allowed to influence the outcome, applies version-controlled company knowledge and controls whether the result may be finalised.

Compile time

Operational Knowledge Compiler

Transforms human-readable procedures and decision logic into versioned, testable decision packages.

01
Ingest governed knowledge

Procedures, standards, matrices, checklists and approved examples.

02
Map the machine ontology

Canonical terms, qualifiers, evidence policies and contradictions.

03
Build executable logic

Inclusion, exclusion, calculation, escalation and approval rules.

04
Review, test and version

Subject-matter approval, regression tests, source traceability and release control.

Run time

Governed Decision Runtime

Executes approved decision packages against verified evidence states.

01
Structure the evidence

Images, documents, sensor readings and user observations become typed fields.

02
Verify field by field

Accept, correct, reject, mark unresolved or require external confirmation.

03
Execute governed rules

Deterministic constraints eliminate incompatible decision records.

04
Control and trace the outcome

Human approval, escalation and provenance remain attached to the final output.

Probabilistic perception → structured evidence boundary → deterministic governed execution
Why it is different

Prompt chains generate answers.
AIMAILABS executes governed workflows.

Typical AI or SaaS workflow

Free-text inputs move between prompts or screens.
The model or user interprets the guideline at run time.
The output may vary without a hard decision boundary.
Traceability often stops at the generated answer or report.
VS

AIMAILABS Decision Intelligence

AI output is converted into typed, verifiable evidence fields.
Company rules are compiled and version-controlled before execution.
The same verified input and rule version produce the same decision result.
Evidence, rules, rejected alternatives and approvals remain traceable.
Reusable decision patterns

One platform. Many operational workflows.

The product names are validation points, not the limit of the platform. AIMAILABS can be configured wherever operational decisions depend on evidence, company rules and human authority.

PATTERN 01

Detect and classify

Identify conditions, defects, events and exceptions from operational evidence.

PATTERN 02

Assess and score

Apply technical criteria, ratings, risk logic and controlled calculations.

PATTERN 03

Validate and approve

Check completeness, compliance, permissions and approval requirements.

PATTERN 04

Prioritise and optimise

Rank work, resources, interventions, opportunities and competing actions.

PATTERN 05

Monitor and escalate

Track changes, trigger thresholds and route exceptions to authorised people.

PATTERN 06

Recommend and report

Generate traceable actions, assessments and decision records for review.

Platform validation applications

Four use cases test the same core platform.

Each application validates a different decision pattern. Their maturity is presented separately so the strongest evidence is not diluted by early-stage concepts.

Pilot-proven validation

Inspecta

Field evidence, integrity assessment, guideline matching and traceable draft reporting.

Asset integrity · First commercial beachhead
Prototype / discovery

Maintena

Maintenance prioritisation, planning logic and decision support from asset history.

Maintenance intelligence · Expansion workflow
Market validation

Permita

Permit checks, risk controls, evidence completeness and governed approvals.

HSE governance · Bid-readiness pathway
Early validation

TerraSignal

Geospatial evidence translated into prioritised plantation or operational interventions.

Agricultural intelligence · Competition validation
Validation evidence

Strong technical validation.
Early commercial conversion.

AIMAILABS started from a real enterprise problem, not a speculative product idea. We are transparent that recurring revenue and repeatable multi-customer deployment remain the next proof points.

01

Enterprise problem surfaced

PETRONAS SKGAS leadership highlighted a real inspection workflow gap.

02

Inspecta built and tested

A working application was developed around the operational requirement.

03

Industrial workflow validation

The workflow was exercised in a real industrial environment with human review.

04

Enterprise catalogue visibility

Inspecta is listed in the PETRONAS iG@P Tech Catalogue under Digital Solutions.

05

Technical recognition

The Inspecta methodology was published at OTC Asia 2026 and is available on OnePetro.

Working product

Inspecta has progressed beyond concept stage into a functioning industrial application.

PETRONAS iG@P Tech Catalogue

Catalogue listing provides internal technology visibility. It does not imply procurement or groupwide endorsement.

OTC Asia 2026 / OnePetro

External technical publication documents the original AI-powered inspection methodology.

View publication ↗
Commercial stage

Commercial conversion is underway. Recurring revenue, signed long-term contracts and repeatable sales are not yet established.

PETRONAS, SKGAS and iG@P are referenced only to describe factual engagement, workflow validation and catalogue listing. No endorsement, partnership or procurement commitment is implied.
Commercial model

Implementation lands the workflow.
Recurring platform use creates long-term value.

01

Knowledge compilation

One-time onboarding converts customer procedures and decision logic into an approved package.

02

Platform subscription

Annual or multi-year access to the AIMAILABS Decision Intelligence Platform.

03

Workflow expansion

Additional functions, departments or use cases are configured on the same core platform.

04

Support and integration

Enterprise integration, training, updates, customer success and specialist support.

Start with one decision workflow

Bring the problem, evidence and rulebook.

We will assess whether the workflow can be converted into a controlled pilot using the AIMAILABS compiler and runtime.

Discuss Your Workflow →