Industries · Manufacturing
Connected data, intelligent operations and faster decisions: computer vision, optimisation, GenAI and data engineering from one team, running inside your plants.
What AI delivers
01
Plans that rebuild themselves and bottlenecks found as they happen.
02
Inline vision catches defects before they reach customers.
03
Predict failures and remove manual effort in plant and back office.
04
One source of truth, with AI answers grounded in your own data.
The use-case spectrum
Mapped for a large manufacturing corporation, all on one data, vision and GenAI foundation.
| Function | Steel processing | Auto components | Corporate & shared services | Recycling & logistics |
|---|---|---|---|---|
| Production planning | 01 Automated production planning02 Smart master coil planning | 10 Line throughput (JPH) analytics | ||
| Quality & inspection | 03 Sheet defect detection04 Sheet-cut measurement | 08 AI weld inspection cell | ||
| Maintenance | 05 Predictive maintenance | |||
| Safety (EHS) | 06 PPE & safety compliance | |||
| Engineering & costing | 07 CAD to BOM & pricing11 3D to 2D drawing agent12 Program management platform | |||
| Finance & accounts | 09 Invoice & freight bill automation | |||
| Procurement & commercial | 13 Contract intelligence | |||
| Human resources | 14 HR policy assistant15 Workforce sentiment analytics16 Competency-to-career path | |||
| Stores & logistics | 18 Parts & inventory counting19 Route optimisation | |||
| Customer service | 17 Voice agent call attender |
Key solutions
01 · Production planning
Thousands of SKUs scheduled by hand, reshuffled every morning by spillover.
A planning engine, rule engine and optimiser allocate every SKU to stations, then re-plan automatically on new orders and spillover — with “why this plan” explanations.
03 · Quality & inspection
Sheet runs at up to 180 m/min — far too fast for visual inspection.
Line-scan cameras with purpose-built lighting and an AI model that classifies dents, scratches and holes in real time and sends a halt signal to the line.
08 · Quality & inspection
Every welded assembly checked by hand, 2.5–3 minutes a part, with fatigue-driven misses.
A turntable imaging station photographs each part from several angles; deep-learning models verify every checklist weld and flag defects for the operator.
05 · Maintenance
An ageing bottleneck machine is a single point of failure; maintenance is reactive.
IoT condition monitoring and ML failure prediction, with an alarm engine and a maintenance chatbot built on the plant's breakdown history.
07 · Engineering & costing
BOMs built by hand from CAD — hours per assembly — and costed on fragmented masters.
A CAD-kernel pipeline reads assembly structure exactly, a self-hosted LLM cleans names and metadata, and processes are mapped to roll up should-cost per variant.
How we have solved it
Large manufacturing corporation
19 use cases mapped from coil planning and weld inspection to finance, procurement and HR — sequenced from quick GenAI wins to shop-floor platforms on one foundation.
19
use cases
10
functions
4
divisions
Global automotive seating Tier-1
One CAD → VBOM → cost platform: AI reads geometry, classifies parts by commodity, maps processes and rolls up should-cost against benchmarks for every seat variant.
70%
faster costing
30–40%
less manual effort
90%+
process classification
Global automotive seating Tier-1
An AI agent reads the 3D model, plans views, applies the company template, adds dimensions and GD&T, and validates against standards before export.
70%
faster drawings
90%
standard compliance
30–50%
less drafting
Global top-3 tyre manufacturer
Multiple data automations on Azure Data Factory and Azure Databricks, plus computer vision and GenAI contributing to advanced R&D.
Azure
Databricks platform
CV + GenAI
for R&D
Multi
function analytics
Seating outcomes are the targets set for each engagement; every engagement fixes a baseline and a target up front.
On a trusted data foundation
Plant systems — ERP, MES, SCADA, IoT, cameras and PLM — connected into one governed lakehouse that every use case shares.
Pick two or three use cases, share sample data or camera clips, and start a four-week discovery with a measured baseline.