Make your operation more intelligent without rebuilding it from scratch.
Your machines, ERP, production systems, quality records, documents and people already generate enormous amounts of operational knowledge.
Margins builds AI around that existing environment to help manufacturers detect problems earlier, automate repetitive work and give people better information for the decisions they make every day.
Connect the operation. Find the signal. Act earlier.
The factory already knows more than it can tell you.
Your operation produces signals all day. The information exists. The problem is identifying what matters early enough to act.
- 01A machine behaves differently.
- 02A defect pattern starts emerging.
- 03Production slows.
- 04A maintenance issue repeats.
- 05A supplier input changes.
- 06A process moves outside its normal range.
- 07An operator notices something unusual.
- 08A document contains an important exception.
- 09A schedule begins slipping.
AI can help turn operational signals into operational decisions.
Most industrial systems are very good at recording what happened.
- ERP
- MES
- SCADA
- Quality systems
- Maintenance systems
- Sensors
- Spreadsheets
- Production reports
They create enormous amounts of information. But somebody still needs to interpret it.
Record
What happened?
Detect
What is unusual?
Understand
What does it mean?
Predict
What may happen next?
Recommend
What deserves attention?
Act
What should happen now?
The opportunity isn’t more factory data. It’s making better use of the data the factory already produces.
Small deviations can become expensive problems.
AI and machine learning can analyze historical and current information to identify behaviour that differs from expected patterns — across signals like temperature, pressure, cycle time, energy consumption, vibration, throughput, error rates, quality measurements, machine behaviour and production output.
- Process anomaly detection
- Production deviation detection
- Equipment behaviour monitoring
- Quality anomalies
- Energy anomalies
- Throughput changes
- Operational exceptions
The earlier you recognize that something is changing, the more options you have to respond.
Maintenance becomes more valuable when it happens before the interruption.
Where sufficient data exists, predictive approaches add another possibility to time-based and reactive maintenance.
Time-based
Service equipment according to schedule.
Reactive
Repair it when something fails.
Condition-based
Understand how equipment is actually behaving.
- 01Condition-based
- 02Risk detection
- 03Early warning
- 04Maintenance action
The objective isn’t predicting failure for its own sake. It’s creating enough time to prevent the operational consequence.
Inspect more without asking people to look at everything.
- Visual defects
- Missing components
- Incorrect assembly
- Surface anomalies
- Packaging issues
- Label problems
- Measurement anomalies
- Process deviations
- Documentation inconsistencies
Understand what can be seen.
Understand what can be measured.
Understand what can be read and processed.
Automate the predictable inspection. Put human attention on the exceptions.
Turn cameras into another source of operational data.
Computer vision can convert images and video into structured information that software can understand. Possible manufacturing applications:
If a person can repeatedly identify something visually, there may be an opportunity for computer vision to assist.
Explore Computer Vision- Camera
- Image
- Detect
- Classify
- Compare
- Pass / review
The answer shouldn’t depend on finding the one person who knows.
Manufacturing organizations accumulate enormous technical knowledge. AI can retrieve relevant information from approved company knowledge rather than relying only on generic model knowledge.
Put operational knowledge where the work happens.
- “What does error code 472 mean?”
- “What is the procedure for this fault?”
- “When was this issue last reported?”
- “Which replacement component is approved?”
- “What are the quality limits for this product?”
- “What should I check before restarting the machine?”
Some of your most important operating knowledge has never been written down.
- 01
An experienced operator hears something different in a machine.
- 02
A maintenance technician knows which fault usually follows another.
- 03
A production manager knows why a process behaves differently for a particular product.
- 04
A quality specialist recognizes an exception that isn’t described in the manual.
That knowledge may have taken twenty years to build — and often exists only in people’s heads. Capta helps capture it and convert it into structured knowledge that people and AI systems can use.
Your machines produce data. Your people produce understanding. The most valuable AI can learn from both.
Explore CaptaThousands of pages become useful when people can ask them questions.
- Q
“Show me the maintenance procedure for Line 4.”
- Q
“What torque specification applies to Component X?”
- Q
“What changed between these two SOP versions?”
- Q
“Which quality procedure applies to this defect?”
Don’t make people search the knowledge base. Let the knowledge base answer them.
- Find system
- Search document
- Open PDF
- Find page
- Interpret
- Ask
- Retrieve approved information
- Answer with context
- Source
- Action
Manufacturing still runs on enormous amounts of unstructured information.
- Purchase orders
- Specifications
- Certificates
- Inspection reports
- Supplier documents
- Quality records
- Work orders
- Maintenance reports
- Invoices
- Emails
- 01Document
- 02Understand
- 03Extract
- 04Classify
- 05Validate
- 06Update system
- 07Human exception
A document shouldn’t become manual data entry simply because a person sent it as a PDF.
Give technicians the complete context around the problem.
Then the technician reports what actually happened — and that becomes part of the future knowledge base.
- Fault472
- Recent historySame fault occurred twice in 60 days.
- DocumentationRelevant service procedure identified.
- Previous resolutionValve assembly replaced.
- PartReplacement available in inventory.
- AI suggestionInspect valve assembly before restart.
Every resolved problem can make the organization better at resolving the next one.
The best production plan has to survive reality.
AI and optimization can help planners evaluate more combinations and understand the consequences of changes across:
Let software evaluate the possibilities. Let people make the trade-offs.
- Urgent order arrivesalert
- Production scheduleaffected
- Material availabilitychecked
- Machine capacityevaluated
- Delivery commitmentscompared
- Alternative plansgenerated
- Plannerdecides
AI shouldn’t live only in management dashboards.
The people closest to production often have the least convenient access to information.
- Operators
- Technicians
- Quality teams
- Warehouse staff
- Supervisors
- Mobile applications
- Tablets
- Workstations
- Voice interfaces
- Embedded assistants
- Machine-side interfaces
Put intelligence at the point where the decision is made.
Sometimes typing is the wrong interface.
Capture operational knowledge without turning technicians into data-entry clerks.
- AssetLine 3
- EventUnplanned stop
- CauseDrive belt wear
- ActionBelt replaced
- Downtime36 minutes
- Follow-upCheck alignment
- Maintenance historyUpdated
AI needs context from across the operation.
Useful manufacturing intelligence may depend on ERP, MES, SCADA, CMMS, QMS, WMS, PLCs and industrial systems, databases, sensors, documents and custom software. Margins connects AI to them rather than requiring manufacturers to replace what already runs the operation.
The factory doesn’t need another disconnected AI platform. It needs intelligence connected to the systems it already runs on.
Explore IntegrationsYour factory doesn’t need to become greenfield before it can use AI.
That’s reality — and AI architecture has to work around it. Depending on the system, Margins uses APIs, databases, data pipelines, custom connectors, automation and software layers to make existing technology part of the solution.
- Modernize where it creates value.
- Integrate where it makes sense.
- Replace only where necessary.
Your production data doesn’t have to leave the factory to become intelligent.
AI can be designed around customer-controlled environments, including private cloud, local infrastructure and on-premise deployment where appropriate — for private LLMs, enterprise RAG, computer vision, predictive models, local automation, AI agents and edge AI.
Explore Private & On-Premise AI- Production information
- Intellectual property
- Product designs
- Processes
- Customer data
- Machine data
- Network connectivity
- Infrastructure
- Data residency
Cloud AI is an option. Not a prerequisite.
Put intelligence close to the process.
Some industrial AI workloads benefit from running close to where the data is generated — a camera, a line, a machine, a local server, an industrial computer. Useful where workloads require:
Not every industrial decision needs a round trip to the cloud.
- 01Camera / machine / sensor
- 02Edge AI
- 03Local decision
- 04Action
- 05Central system / history
Move from finding information to coordinating work.
An AI agent could potentially handle the chain below — but access and autonomy should be controlled.
- 01Detect an operational exception.
- 02Retrieve relevant production information.
- 03Check documentation.
- 04Identify affected orders.
- 05Create a maintenance task.
- 06Prepare a management summary.
- 07Notify the right person.
- 08Track whether the issue was resolved.
The more AI can do, the more precisely we need to define what it is allowed to do.
Where AI could change your operation.
Hover or tap any part of the operation to see the AI opportunities worth investigating there.
- Operational intelligence
- Exception prioritization
- Forecasting
- Supplier documents
- Exception detection
- Material information
- Exception detection
- Process anomalies
- Production intelligence
- Planning support
- Computer vision
- Anomaly detection
- Quality-document processing
- Inventory intelligence
- Visual recognition
- Operational automation
- Predictive signals
- Knowledge assistants
- Maintenance automation
- AI assistants
- Knowledge access
- Voice reporting
- Connected MES · ERP · QMS
- Planning: scheduling, capacity, demand signals
- Detect · predict · assist
- Human-in-the-loop
Don’t start with “How do we use AI in our factory?”
Start with:
- Where are we losing production time?
- Which quality issues repeat?
- Which decisions take too long?
- Where does one experienced employee hold critical knowledge?
- Which process requires constant manual coordination?
- Where is information manually transferred between systems?
- What do we repeatedly discover too late?
- Which repetitive tasks consume skilled labour?
- 01Business problem
- 02Process
- 03Available data
- 04AI opportunity
- 05Value × Feasibility
- 06First implementation
The best first AI use case is rarely the most impressive one. It’s the one that creates measurable value and a foundation to build on.
You don’t need to make the entire factory intelligent at once.
Start with one valuable problem. Each implementation creates data, integrations and organizational capability that make the next one easier.
- 01QualityDetect defects earlier.
- 02MaintenanceImprove access to troubleshooting knowledge.
- 03DocumentsAutomate repetitive processing.
- 04ProductionDetect operational anomalies.
- 05PlanningImprove decision support.
- 06KnowledgeCapture employee expertise.
- 07OrchestrationConnect intelligence across the operation.
AI shouldn’t replace the knowledge built inside your operation. It should compound it.
The competitive advantage isn’t the model. It’s what the model can learn about how your operation actually works.
Industrial AI needs more than an AI model.
Business first
Start with the operational problem and measurable outcome.
AI + software
Build the intelligence and the production software around it.
Computer vision + ML + LLMs
Use the appropriate technology for each problem rather than forcing every use case through generative AI.
Enterprise integration
Connect existing ERP, data, documents and operational systems.
Private architecture
Design for customer-controlled and on-premise environments where required.
Forward Deployed Engineering
Keep engineers close to the operation as the system encounters real-world complexity.
Managed AI
Monitor and improve production AI after deployment.
The technology is proven. The first manufacturing outcome is specific to your operation.
We won’t dress up cross-industry work as manufacturing references. What we bring is a delivery organization that has put AI and software into production across complex operations.
Related capabilities
Computer Vision
Turn visual information into structured operational intelligence.
Explore Computer Vision →Predictive AI
Detect patterns and changes before they become obvious.
Explore Predictive AI →Enterprise Knowledge & RAG
Put technical documentation and company knowledge at employees’ fingertips.
Explore Enterprise Knowledge →Private & On-Premise AI
Run AI inside customer-controlled industrial environments.
Explore Private AI →Integrations
Connect AI to the systems already running the operation.
Explore Integrations →
Where is your operation losing time, knowledge or visibility today?
We’ll work with your leadership and operational teams to understand the process, data and systems behind the problem — and determine whether AI can create a measurable advantage.
Explore AI opportunitiesStart with the operation. Find the signal. Build from there.