Production visibility
Connect order status, work-in-progress, output and downtime records. Give supervisors a consistent view of what is running, waiting or blocked, with timestamps and clear ownership.
Factory AI & Automation · Sri Lanka
Make your factory data useful on the floor.
Turn disconnected production records into clearer decisions. We help scope and build factory dashboards, workflow automation and AI assistance around the people, equipment and systems already doing the work.
Kaduwela, Sri Lanka · Scope-led delivery
Start with a clear workflowConnect existing systemsMeasure the pilot
A clear starting point
What is factory AI enhancement?
Factory AI enhancement uses production information to help teams spot exceptions, find approved instructions and improve daily decisions. It works alongside reliable data capture, business rules and human review. The starting point is a specific process with a measurable problem.
For manufacturers, assembly operations, processing businesses and multi-stage production teams in Sri Lanka. Each implementation is scoped to the site's actual process and available data.
What we can build
Practical tools.
Connected decisions.
Choose the capabilities that address your operating problem. Integrations, data readiness and acceptance criteria shape the final scope.
Quality workflows
Digitise inspections, record defects against batches and route exceptions for review. Explore image-assisted checks only where sample quality, lighting and acceptance tests support the use case.
Maintenance coordination
Bring service logs, fault reports and maintenance windows into the operating workflow. Predictive models can be evaluated when useful equipment history exists; straightforward reminders can deliver a simpler first release.
Factory knowledge assistance
Help authorised staff locate approved procedures and maintenance references. Show source documents and their revision dates, and route uncertain or safety-related questions to a responsible person.
In everyday operation
A quality hold should reach the whole workflow.
Illustrative workflow: an inspection identifies a batch that needs review before its next production step.
Explore the application layer- 01
Capture the exception
Record the batch, operation, inspection result and supporting evidence against the production order.
- 02
Make the hold visible
Alert the responsible team and mark affected work as unavailable for the next step. A supervisor decides what may proceed.
- 03
Update the plan
Pass the approved status to planning and ERP so the team can review material availability and delivery impact.
- 04
Learn from the record
Compare repeated causes and follow-up actions using traceable records, then decide whether a model would add useful insight.
Prepare the foundations
Good decisions need
usable information.
We review your current process and information before proposing an implementation. Start with representative examples and the people who understand them.
Production context
Process steps, machine identifiers, batch or job references, shift calendars and the decisions each team needs to make.
Reliable observations
Timestamped output, downtime reasons, quality checks and source-system ownership. Manual capture can be a valid first stage.
Integration boundaries
Available ERP interfaces, approved data access and equipment connectivity. Device purchases and control-system changes are scoped separately.
From discovery to daily use
A focused pilot.
A deliberate path forward.
- 01
Observe one process
Map the actual workflow with supervisors and operators, including rework and exceptions.
- 02
Connect a useful dataset
Establish identifiers, access controls and trustworthy capture before adding complex models.
- 03
Pilot with the team
Run alongside the current process. Compare results against a baseline and review incorrect suggestions.
- 04
Expand with evidence
Agree acceptance, training, monitoring and ongoing ownership before extending to another line.
Make improvement measurable
Agree the baseline.
Review the evidence.
These are candidate pilot measures, selected with your team. Targets and expected outcomes are agreed after reviewing the current process.
- Exception visibility
- Time between a recorded problem and an assigned owner.
- Record quality
- Completeness, timeliness and consistency of production records.
- Operational follow-through
- Open issues, repeat causes and completion of agreed actions.
Useful before your first conversation
Explore the details.
Practical answers
Questions before
you begin.
Can we start without sensors or a fully digital factory?
Yes. A first phase can use existing ERP exports, spreadsheets or structured operator entries. Sensor integration is added when it solves a defined data gap and the equipment can support it.
Will AI control our machines?
The service described here focuses on information, recommendations and supervised workflows. Direct machine control, interlocks and safety functions require a separate engineering scope, validation and responsible equipment specialists.
Can this connect to our ERP or production software?
Integration depends on the available APIs, database access or approved import/export paths. We agree source ownership, update frequency, validation and recovery before connecting systems.
How much productivity improvement should we expect?
The outcome depends on the process and starting data. We agree a baseline and measurable acceptance criteria during discovery, then evaluate a pilot before making wider commitments.
What should we prepare for a first discussion?
Bring a process outline, one recurring production problem, sample records with private details removed, and the systems used by supervisors and operators. A site review can be scoped where useful.
Connect the wider workflow
Start with the work you want to improve
Bring us one workflow.
Let’s plan its next step.
Share the current process, the systems involved and what a better day would look like for your team.