Industrial AI for Manufacturing
Industrial AI that runs on the plant you already have.
ByteArmor deploys and governs Dataviss Industrial AI across manufacturing plants, facilities and asset networks. Your ERP, MES, SCADA, QMS and BMS stay exactly where they are. We work out which operational problem to solve first, prove it on one line or one plant, and scale it under governance.
Implementation partner for Dataviss Analytics · Production deployments across automotive, electronics and process manufacturing · Governance engineered in from day one
Find Your Starting Point
An AI strategy alone won't fix this. You need one expensive problem solved.
Most industrial AI programmes stall because they start with a platform instead of a problem. We start with the recurring work that is already costing you time, margin or sleep — and prove the outcome on that one thing before anything scales.
01
Daily operating reviews take hours to prepare
Production, downtime, maintenance, quality and cost evidence sits in five systems. Someone spends half a shift assembling it.
First measure: review-preparation time, recurring losses, unresolved actions.
02
Cash is trapped in inventory, or shortages are stopping production
Excess, ageing and blocked stock on one side; expedites and line stoppages on the other. Both are working capital.
First measure: working capital exposed, shortage risk, avoidable expedites.
03
Quality engineers spend more time assembling evidence than solving problems
SPC variation, supplier evidence, PPAP packages, complaints and warranty cases — all evidence-heavy, all manual.
First measure: cases processed, SLA performance, cost of poor quality.
04
Energy and utility losses only show up on the bill
Abnormal consumption, HVAC drift and equipment running outside baseline are invisible until the month closes.
First measure: energy cost per unit, abnormal-consumption events.
05
Visual inspection can't hold consistency across variants
Manual end-of-line inspection doesn't scale across product variants, shifts and plants.
First measure: escape rate, rework, inspection throughput.
One problem at a time.
One accountable owner. One measurable outcome agreed before we start. Nothing scales until the first result holds.
What Gets Deployed
Six building blocks. You start with one.
Dataviss is built as three layers — connect what's trapped, run the operational workflow, then put specialised AI Analysts on the recurring analytical work. You adopt only the layers your problem actually requires.
01 · Connect
EdgeVISS
Industrial Data Enablement
Connects and contextualises data trapped in PLCs, SCADA, HMI, BMS, machines and sensors across 250+ industrial protocols. Deployed only where data isn't already reachable through ERP, QMS, databases or APIs — if your systems can already hand over the evidence, you don't need this layer.
02 · Operate
Qualitiviss
QMS & SPC Platform
End-to-end quality management, SPC, PPAP, audit readiness and supplier quality.
FaciliVISS
Facility & Utility Platform
Energy, HVAC, water, utilities and equipment performance monitoring against operating baselines.
DatVision AI
Visual Inspection
Defect detection, assembly verification, label and marking inspection, foreign-object detection, and PPE and safety compliance.
03 · Analyse
IQVISS
Industrial Business Analyst
Production, downtime, cost, inventory, energy and profitability in one view — with evidence-backed findings and a recommended next investigation, rather than another dashboard to interpret.
QAI
Quality Analyst
A workforce of specialised analysts across the quality lifecycle:
- · SPC intelligence — process variation, drift and emerging risk
- · Shipment quality & compliance — supplier documents and certificates
- · PPAP preparation and review
- · 8D customer complaints — root cause and corrective action
- · Warranty claims — eligibility, failure analysis, recovery
- · PFMEA governance — discipline, revision control, action closure
Dataviss®, QAI® and DatVisionAI® are registered trademarks of Dataviss Analytics LLP. ByteArmor AI is an implementation partner for Dataviss Analytics.
Our Methodology, Applied
The same five phases. Scoped to one plant, one line, one decision.
Industrial AI follows the same ByteArmor journey as every other engagement — with a deliberately narrow first scope, so the outcome is proven before the spend scales.
01 · 2–3 weeks
Discover
Map the recurring work, the systems that hold the evidence, and whether the data can actually support the decision. Output: a go/no-go with a named first use case.
02 · 2–4 weeks
Design
Define the work unit, the minimum evidence set, the accountable review owner and the success gate. Select the Dataviss components required — no more.
03 · 6–12 weeks
Build
Deploy, integrate and validate against the agreed baseline. Operators use it in daily work, not in a demo environment.
04 · Ongoing
Govern
Role-based access, audit trails and evidence retention. Humans keep decision authority — the system recommends, your team decides.
05 · Ongoing
Scale
Replicate across lines, plants and regions once the first outcome holds.
What the first deployment must prove
One work unit
A single recurring review, case or inspection decision.
Minimum evidence
Only the systems and signals that decision actually needs.
One review owner
An accountable person who validates the output.
One success gate
A baseline agreed in writing before we begin.
No portfolio-wide commitment. We expand only after your team validates the result.
Proof, Not Promises
Production deployments. Running for years.
These are deployments running in daily operations.
Facility & Utility Operations
5 years
A global network-equipment manufacturer runs daily facility operations on this platform across two global data centres, one network centre and an electronics manufacturing plant.
Quality Operations
3 years · 10+ plants
A Tier-1 automotive wheel manufacturer runs daily quality workflows across more than ten plants, now extended pan-India, reaching an ecosystem of 100+ Tier-1 and Tier-2 suppliers.
Visual Inspection
3 years · 4 plants
A commercial-vehicle OEM runs vision-based end-of-line inspection across four plants — part of 20+ rollouts across multiple customers.
Warranty Analysis
78 → ≤45 days
A global commercial-vehicle manufacturer extended its warranty from two years to five, quadrupling the analysis queue. Ticket closure came back within the 45-day SLA at 4× the volume, with human approval retained at closure.
Read the published case study →Asset Network Intelligence
Pan-India ATM surveillance availability
Tens of thousands of surveillance sensor points across a nationwide distributed installed base, assessed daily. The existing monitoring platform stays in place; the Analyst ranks which sites need human attention first, and field teams decide the intervention.
Read the published case study →Deployments and outcomes described above are technology partner customer engagements, reproduced with permission. Figures are as published by the partner; no additional performance claim is made by ByteArmor AI.
Built for Plant Reality
Written for the people who own the problem.
Industrial AI succeeds or fails on the shop floor, not in the boardroom. These are the roles we work with directly.
| Role | What we solve |
|---|---|
| Plant Head | Throughput, cost per piece, OEE, downtime |
| Quality Head | Cost of poor quality, rejection, warranty, audit readiness |
| Operations Head | Line performance, yield, delivery reliability |
| Facility / EHS Head | Energy cost, utilities, sustainability reporting |
| Maintenance Head | Asset availability, predictive maintenance, reliability |
| CFO / Finance Head | Working capital, cost per unit, EBITDA protection |
| CIO / IT Head | Integration, data governance, security, scale |
Industries we serve
Bring us one recurring decision.
Show us the operational work that keeps consuming time — a review, an investigation, an evidence pack, a recurring loss. We'll map the minimum evidence required, the right Dataviss component and the outcome your first deployment should prove.