Quality caught on the line
Connect inspection images to the defect classes your quality team defines. Give operators evidence for the batch decision.
You receiveEvaluation report
Delivered through Computer Vision & ML EngineeringDecisions on the line.
Quality in every batch.
Bring MES, SAP and machine readings together so plant teams can act before the next batch leaves the line.
Quality caught in process
Inspect against the defect classes your quality team defines.
One plant view
Read production, quality and throughput on shared definitions.
AI engineers can trust
Keep release decisions with the people who own quality.
4 hrs → 1 min inspection time per batch · Grasim Industries · Aditya Birla Group
See the whole plant.
Act on the exception.
Machine readings, MES and ERP tell different parts of the same story. Bring them into one checked operating view before a batch needs a decision.
Join the plant record
Connect machine, batch and ERP records at their source.
Inspect against your standard
Score images against the quality team’s defect classes.
Route the next action
Put the exception in front of the right operator.
This is the right partner when
- Quality checks arrive after the batch has moved.
- MES and SAP disagree on production performance.
- A BusinessObjects estate needs a measured exit.
Data and AI work
for the plant floor.
Start with the decision, follow the data, and leave each engagement with something your team can use.
A live view of production
Bring machine readings into a live KPI board. Surface the condition that needs an operator’s attention.
You receiveLive KPI board
Delivered through Real-Time IntelligencePlant data on one model
Connect SAP, MES and plant feeds through reusable mappings. Validate each load before it reaches a report.
You receiveOnboarding runbook
Delivered through Data IntegrationPlant KPIs people trust
Agree the definitions behind throughput, quality and downtime. Give Power BI users one semantic model for those decisions.
You receiveSemantic model
Delivered through Analytics & BIA planned BusinessObjects exit
Inventory SAP reporting logic and the reports teams still use. Sequence the move with evidence that the numbers match.
You receiveExit wave plan
Delivered through Platform ModernizationA costed plan for plant AI
Read the workloads behind the next plant decision. Price the data gaps before committing to an AI build.
You receiveCosted modernization map
Delivered through Data Strategy Assessment
The Mandelbulb Kit Manufacturing editionBuilt and maintained in-house
Engineered in-house.
The Mandelbulb Kit.
Accelerators, agents and products we build and maintain ourselves, brought into an engagement wherever they fit.
Real-time accelerator · Inspect the batch
Pulse
Your own operational events, turned into decisions while the work is still moving.
- Takes operational events into live KPIs
- Raises anomaly alerts and automated actions
- Starts with two weeks on one stream family
On the line
Explore more about PulseAlso in this edition
- SAPBridge · BOBJ TransformerSAP in, BusinessObjects outSAP data on Microsoft Fabric or Databricks, reconciled to the source, with a planned exit from BusinessObjects built in., explore SAPBridge · BOBJ Transformer
- BlumcoreIn operator workflowsThe runtime every Kit agent runs on: it reads your data, acts in your systems and audits every step., explore Blumcore
Proof from
the plant floor.
Grasim Industries · Aditya Birla Group
Manufacturing · Textile
Grasim Checks Fibre Quality in One Minute, Not Four Hours
Grasim’s quality team defined the defect classes. We connected image inspection to live flagging so the team could make its batch decision sooner.
- Inspection time per batch
- 4 hrs → 1 min
- Defect-detection accuracy
- 98%
An Australian building-materials manufacturer
Manufacturing · Building materials
SAP to Self-Serve Power BI, With Reporting 50× Faster Than MS SQL
SAP and BI were brought into a governed model for an Australian building-materials manufacturer. Plant and business teams could work from the same checked data.
- Warehoused data unified in one governed model
- Around 3 TB
- Automated SAP-to-BI integration, no manual steps
- 100%
A Canadian food and consumer-goods company
FMCG · Food
SAP to Microsoft Fabric: 100+ Pipelines at 95%+ Validated Accuracy
A Canadian food and consumer-goods company moved legacy SAP transformations onto Microsoft Fabric. Reusable target models replaced the old mappings.
- Optimized SAP transformation pipelines
- 100+
- Validated migration accuracy
- 95%+
Questions buyers ask.
How is AI used in manufacturing?
In manufacturing, AI is most useful where a decision on the line depends on evidence: quality inspection, live production monitoring and plant reporting. We connect inspection images to the defect classes your quality team defines, bring machine readings into a live KPI board and put SAP, MES and plant feeds on one checked model. Release decisions stay with the people who own quality.
Which data platform suits a manufacturing team?
The right platform is the one your plant and business teams can run, and our work is platform independent: Microsoft Fabric, Databricks or BigQuery for data, with Azure OpenAI, OpenAI, Anthropic, Gemini or a self-hosted model for AI. Pulse, the Kit accelerator for real-time plant data, runs natively on Fabric or Databricks, and SAPBridge brings SAP onto either with every record reconciled.
How should a manufacturer start with data and AI?
Start with one batch decision that a quality check, plant report or SAP dependency is slowing down. Bring it to a 30-minute working session and we scope the next step. If the case is not yet clear, a Data Strategy Assessment reads the workloads behind the next plant decision and prices the data gaps before anyone commits to an AI build.
What governance does AI on the plant floor need?
Plant AI should support a decision, not take it away from the people accountable for it. Agree who owns each data domain, which definitions govern throughput, quality and downtime, and who signs a release. Validate each SAP or MES load before it reaches a report, and keep the evidence attached to every flagged batch so quality and audit teams can trace why it was raised.
What does a first manufacturing engagement deliver?
A first engagement delivers one artifact your team can use, scoped to one decision. Depending on the starting point, that is an evaluation report for a vision model against your defect classes, a live KPI board for machine readings, an onboarding runbook for SAP and MES feeds or a costed modernization map. Each phase is fixed price and fixed scope.
What we do.
For Manufacturing, the four practices run as one loop.
Strategy & Advisory
Judgment before the build. We map what your data can actually do for the business, price the path, and hand you a plan you keep, whichever way you decide.
Modernization
We move your data out of aging systems onto a foundation built for what comes next, without losing a single number along the way.
Data & AI
The work your teams feel every day: live operational intelligence, reporting people trust, and AI that acts inside the systems you already run.
Data Estate Optimization
A running estate quietly gathers waste. We find it, tune it, and turn the savings into the budget for whatever you build next.
Partners and recognition.
Bring the batch
that needs a decision.
Bring the quality check, plant report or SAP dependency that slows the line. We will scope the next step in a 30-minute working session.