P-204: a developing mechanical problem?
Inspect rising temperature, vibration, and current. Separate the trip event from an unconfirmed root-cause hypothesis.
Explore scenario 1 ↗ PRIVATE INDUSTRIAL INTELLIGENCE AT THE EDGE
Diagnose faster.
Restore sooner.
Inside your plant.
QAVEAI is developing a private industrial AI copilot that brings equipment signals and plant knowledge together—helping your team troubleshoot faster and reduce unplanned downtime.
Plant-specific knowledge · Local intelligence · Engineer-led decisions
QAVEAI / SYSTEM ARCHITECTURECONCEPT 001
AI
Your equipment. Your knowledge. Your copilot.Proposed on-premise architecture.
BUILT FOR ENGINEERING TEAMSMANUFACTURINGINDUSTRIAL AUTOMATIONMAINTENANCE & RELIABILITY
01 / THE FLAGSHIP CONCEPT
QAVEAI Industrial
Intelligence Copilot
A proposed on-premise platform combining machine intelligence and plant knowledge to support technicians and engineers. Scoped pilots begin with one workflow.
ILLUSTRATIVE WORKFLOW · NOT A LIVE PRODUCT DEMOIt’s 2:00 AM.
It’s 2:00 AM.
The line has stopped.
An alarm tells you what tripped. Finding out why may mean searching manuals, checking trends, reviewing maintenance history, and calling another engineer.
The copilot is designed to bring that evidence into one source-grounded troubleshooting workflow.
The design scales from one asset to fleet-wide health — so the same workflow that troubleshoots a pump at 2 AM can prioritize which machines across the plant need attention this week.
TECHNICIAN QUESTION
“Why did Pump P-204 trip,
and what should I check?”
01 Correlate alarms, sensor trends, and ML findings
02 Retrieve relevant manuals, SOPs, and maintenance records
03 Present evidence, uncertainty, and checks for engineer review
04 Route findings to the responsible technician by text or email
Actual guidance depends on connected data and approved plant procedures. The initial pilot supports decisions; it does not issue machine-control commands.
Faster diagnosisMeasure mean time to repair
Less downtimeTrack equipment availability
Better maintenanceMeasure first-time-fix rate
More productive teamsTrack time spent finding answers
Target outcomes to validate against a baseline during a pilot—not guaranteed results.
SYNTHETIC INDUSTRIAL DEMONSTRATIONS
Explore two troubleshooting scenarios.
Two scripted examples show how industrial evidence can be presented and compared. Fictional data; no live model, retrieval, or plant connection.
P-206: a different signal pattern.
Compare pressure changes and stable bearing temperature, then examine what the evidence can—and cannot—establish.
Explore scenario 2 ↗Includes separately scaled charts, downloadable sample data, and provenance and review notes.
02 / THE TECHNOLOGY UNDERNEATH
Machine signals.
Plant knowledge.
One useful interface.
Hardware and models follow the workload. The proposed architecture keeps core inference and retrieval within the facility, with external dependencies identified during assessment.
The proposed design uses classical analytics and specialized vision models to detect anomalies, then a local language model to explain the evidence alongside approved manuals and procedures. Latency, grounding, and usefulness must be measured for each pilot.
INPUTS
Machines & systems
PLCs · sensors · cameras
Alarms · historian data
SIGNAL INTELLIGENCE
Local ML & vision
Anomalies · condition monitoring
Inspection · process predictions
REASONING & RETRIEVAL
Local LLM ↔ Plant RAG
Manuals · P&IDs · SOPs
PLC documentation · work orders
HUMAN DECISION
Industrial copilot
Source-grounded answers
Technician & engineer review
Designed for the plant.
Size the edge system for latency, concurrency, model memory, camera streams, and operational constraints. Define and test which workflows must keep working without an external connection.
Knowledge with sources.
Retrieve approved plant documentation at answer time. Track document versions, enforce access permissions, and show the evidence behind a response.
Adapt when justified.
Consider fine-tuning for terminology, response structure, or specialized workflows after evaluating a retrieval-first baseline. Measure whether it adds value before increasing complexity.
THE STARTING POINT
Downtime Diagnostic
Assessment
Focus on one asset and one recurring failure mode. Review existing records, identify documented troubleshooting delays and evidence gaps, and prioritize practical improvements—even when the answer is not AI.
Fixed scope, fixed fee: $3,500 for one asset and one recurring failure mode, delivered within 10 business days of kickoff and receipt of the agreed records. $1,750 due on signing and received before kickoff; $1,750 due on readout delivery. Larger scope and implementation are quoted separately.
Discuss an assessment ↗WHAT YOU WALK AWAY WITH
01 A baseline of the current troubleshooting workflow
02 An evidence and data-completeness review
03 Gaps in data, instrumentation, documentation, and workflow
04 Prioritized recommendations with owners and next steps
05 A scoped pilot proposal only if justified by the evidence
06 A written report and 60–90 minute readout
Uses existing records and scheduled interviews. No planned shutdown, control-system changes, or AI implementation is included.
03 / HOW WE WORK
A clear path.
Evidence at every step.
Each phase ends with a decision: what we’ve learned, what needs to change, and whether to proceed.
Define the outcome.
Understand the workflow, the cost of the problem, and what success would mean.
Check the foundations.
Review data availability, system constraints, and the practical fit for AI.
Test the hypothesis.
Build a read-only pilot for one workflow using approved documentation and scoped operational data.
Decide with evidence.
Compare troubleshooting time, answer quality, and operational usefulness with the baseline before expanding.
04 / THE EXPERIENCE BEHIND QAVEAI
Industrial roots.
An AI mindset.
QAVEAI is an independent consultancy founded by Amr Attia, an AI/ML architect with experience spanning industrial automation, applied machine learning, edge systems, and AI evaluation.
His background includes manufacturing and industrial analytics at Admix, packaging-system troubleshooting at Momentum Manufacturing Group, automation at LafargeHolcim, and published research on network intrusion detection.
He has worked on AI for predictive maintenance since 2007 — applying neural networks to cement plant process data at LafargeHolcim years before the industry caught up.
Our vision is to become a global leader in industrial AI, helping industry in the United States and worldwide achieve greater productivity, reliability, and resilience.
Our mission is to turn plant data and engineering knowledge into actionable intelligence through private, locally deployed AI systems. Security is requirement zero: core inference and plant data stay on your edge devices and plant infrastructure.
These are the founder’s prior roles and research, not a list of QAVEAI clients.
Meet the founder ↗05 / BEFORE WE BEGIN
What does “private and local” mean?
The target design runs core AI inference and document retrieval on customer-controlled infrastructure. Data flows, access permissions, updates, telemetry, and any external services are agreed in the deployment scope. Local deployment alone does not establish security or guarantee offline operation; those requirements must be tested.
Do we need to fine-tune a model on all our documents?
The proposed starting point is retrieval over approved plant knowledge. Fine-tuning is optional and considered when a measured gap in behavior, formatting, or a specialized workflow justifies it.
Will the copilot control machines?
The initial offering focuses on read-only decision support. Technicians and engineers review evidence and follow approved procedures. Any future control-system integration would require a separate scope and validation process.
How will business value be measured?
Agree on a baseline, a defined equipment population, and an observation period. Track mean time to repair (MTTR), unplanned downtime, availability, first-time-fix rate, maintenance cost, and technician time as relevant. Separate changes attributable to the pilot from other operational changes.
Is the copilot available as a finished product?
This is QAVEAI’s proposed flagship offering. The next step is a downtime diagnostic assessment, followed by a separately scoped pilot when justified—not a claim of an already deployed, off-the-shelf platform.
LET’S START WITH YOUR CHALLENGE
Start with one asset.
Prove the value.
Tell us about a recurring fault, a difficult diagnosis,
or the plant knowledge your team struggles to find.