Industry

Manufacturing intelligence from shop-floor WhatsApp

Shift handovers, downtime, quality and maintenance signals captured from WhatsApp groups, structured into OEE, MES and ERP-ready data — without slowing the line.

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The problem

Operators, supervisors and maintenance leads coordinate the line on WhatsApp because it is the only tool that works through gloves, noise and shift changes. But none of that conversation reaches the MES, ERP or KPI dashboards. Production loses hours of unrecorded downtime, quality events surface late, and shift handovers depend on memory.

What it is

AirLayer © sits on top of your existing WhatsApp groups and converts shop-floor conversation into structured production data: downtime causes, quality observations, maintenance requests, shift handover summaries and KPIs — all routed to MES, ERP and live dashboards.

Who this is built for

Built for the people who keep operations moving

Each role gets a tangible win — from removing manual reporting to faster escalation and clearer accountability.

Plant Managers

Pain: OEE built from spreadsheets days after the fact.

How Eiffels helps: Live OEE assembled from real shop-floor signals.

+9 OEE points

Production Supervisors

Pain: Shift handovers depend on memory and a paper note.

How Eiffels helps: Auto handover summaries from chats + KPIs.

−38% handover time

Maintenance Leads

Pain: Tickets arrive late, missing context and severity.

How Eiffels helps: Voice-note tickets routed straight into CMMS.

<2 min ticket latency

Safety Officers

Pain: Near-misses go unreported and unaddressed.

How Eiffels helps: One-tap near-miss reporting in WhatsApp.

+47% near-miss capture

Quality Engineers

Pain: Defects surface late with no photo or batch link.

How Eiffels helps: Structured non-conformance with traceability.

Closed-loop quality

Operations Directors

Pain: No live cross-line picture of throughput and risk.

How Eiffels helps: Live multi-line dashboards from real signals.

Real-time visibility

How it works

From WhatsApp signal to structured outcome

A predictable pipeline that runs on every message, voice note, and photo — without changing how teams work.

Capture: operators, line leads and maintenance t

Capture: operators, line leads and maintenance teams keep using WhatsApp groups already in use

Transcribe: voice notes captured in noisy enviro

Transcribe: voice notes captured in noisy environments are converted to text per-speaker

Classify: AI tags each message as downtime, qual

Classify: AI tags each message as downtime, quality, safety, maintenance, handover or coordination

Structure: events are linked to the correct line

Structure: events are linked to the correct line, machine, shift, batch and SKU

Route: structured events are pushed to MES/ERP/C

Route: structured events are pushed to MES/ERP/CMMS and dashboards in real time

Measure: OEE, MTTR, FPY and shift KPIs computed

Measure: OEE, MTTR, FPY and shift KPIs computed automatically from captured signals

Use cases

Real scenarios in this industry

Shift handover summaries

Auto-generated handover notes from chats, voice notes and KPIs at the end of every shift.

Downtime tracking

Every minute of unplanned downtime captured with cause, machine and impact — fed into OEE.

Quality observations

Defects flagged in chat become structured non-conformance records with photos and batch traceability.

Maintenance requests

Voice-note tickets routed straight into your CMMS with severity, asset and location pre-filled.

Safety near-misses

Near-miss reports captured immediately by anyone on the floor — with photo evidence and escalation.

Live OEE dashboards

Availability, performance and quality computed from real signals, not retrospective spreadsheets.

Outcomes

Numbers that matter

Benchmarks observed across pilot deployments. Your results scale with team size, channel volume, and integration depth.

+9 pts

OEE uplift in pilots

−38%

Time on shift handovers

+47%

Quality events captured

<2 min

Maintenance ticket latency

+9 OEE points in 90 days

A discrete-manufacturing site in Central Europe added AirLayer © to four existing WhatsApp groups across three shifts. Within 90 days, captured downtime reasons rose 47%, shift handovers dropped from 25 to 7 minutes on average, and OEE on the bottleneck line moved from 71% to 80%.

Integrations

Plugs into the systems you already run

AirLayer connects to the systems your teams already use.

SAP Oracle Microsoft Dynamics Siemens MES Rockwell Maximo Power BI Custom MES / SCADA

FAQ

Common questions

Will operators have to do more work?+

No. Operators continue talking on WhatsApp the same way they do today. Voice and text are accepted equally — there is no new app, no extra forms, no extra clicks.

How does it connect to our MES?+

AirLayer © has connectors for SAP, Oracle, Siemens MES, Rockwell and Maximo, plus a generic webhook + REST layer for any custom or legacy system.

Can it work in noisy environments?+

Yes. Our transcription model is tuned for industrial noise and accented speech, and we accept short voice notes (3–30 seconds) as the primary input format.

How is OEE actually calculated?+

AirLayer © combines machine signals (when available) with WhatsApp-captured downtime reasons, quality events and shift schedules to compute Availability, Performance and Quality components.

Testimonials

Real teams. Real outcomes.

Use case

“Floor incidents now reach me in minutes — with photos, location and severity already structured.”

Problem solved: Production floor escalations were getting lost in shift handovers.

+31%·Faster escalation

Priya Menon

Plant Operations Lead · FactoryOne Systems

Read use case