Industry
Manufacturing
We build software for manufacturers where the gap between shop-floor reality and the ERP's version of it costs real money in scrap, downtime and expedited freight.
01Overview
What we see in this sector
Manufacturing systems tend to be well-served at the extremes and poorly served in the middle. The machines produce data and the ERP holds the plan, but the connection between them is often a supervisor with a clipboard and a spreadsheet.
That gap is where the cost sits: production reported hours late, quality issues found after a batch is complete, and maintenance scheduled by calendar rather than by condition.
We work in that middle layer — MES-adjacent systems, OEE and quality capture, and the integration that lets planning respond to what the floor is actually doing.
02Challenges
Where it usually hurts
Delayed production visibility
Output reported at shift end, so problems are found after a full shift of material has been consumed.
Quality caught too late
Defects identified at final inspection rather than at the operation that introduced them.
Unplanned downtime
Maintenance on a fixed calendar while the equipment that actually needs attention fails between intervals.
Disconnected planning
ERP schedules built on assumed cycle times that have not matched reality for years.
03Our approach
What we put in place
Real-time production capture
Machine and operator data collected as it happens, with OEE calculated continuously rather than reconstructed.
In-line quality systems
Inspection capture at the operation, with statistical process control that flags drift before it becomes scrap.
Condition-based maintenance
Sensor data and failure models that prioritise work orders by actual risk rather than elapsed time.
Planning integration
Actual cycle times and constraints fed back to the ERP so schedules reflect the plant as it runs.
05Technology
What we typically build with
- Backend02
Java
Long-lived transactional systems in regulated environments.
Python
Data pipelines, ML services and automation where library depth wins.
- Cloud & DevOps01
Azure
Natural fit alongside Microsoft identity, Dynamics and on-prem AD.
- Data02
Apache Kafka
Event backbone for systems that must decouple producers from consumers.
PostgreSQL
The default relational store. Correct, extensible, and boring in the best way.
- Enterprise02
SAP
S/4HANA, Fiori and integration work across finance, supply chain and manufacturing.
Power BI
Reporting layer where the business already lives in Microsoft tooling.
06Outcomes
What clients typically see
+8–15 pts
OEE improvement
Typical within two quarters
Real time
Production visibility, not shift-end
−30%
Unplanned downtime
Earlier
Quality detection at the operation
Want these numbers scoped against your own operation?
Book a technical assessment08Questions
What operators in this sector ask
Next step
Running manufacturing operations that need better software?
Start with a fixed-price assessment of the systems you already have. Scope, sequence and cost, before you commit to a build.