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Deploint

Industry 03

Engineering intelligence into the production line.

Deploint engineers smart-factory software for manufacturers: computer vision quality inspection, predictive maintenance, production analytics and digital twins, integrated with the MES, ERP and equipment already on the floor.

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03 / Manufacturing

System pattern

Engineered for

  • Cycle-time inference
  • MES integration
  • Traceability
  • Operator feedback

Typical system flow

05 stages

  1. 01Machines & cameras
  2. 02Edge inference
  3. 03MES integration
  4. 04Analytics
  5. 05Production

Focus areas

  • Quality inspection
  • Smart factories
  • Production optimization
  • Robotics
  • Supply-chain analytics

01Solutions

Software for the connected factory.

Each solution works with the line as it exists: its cycle times, its equipment and the systems that already own production and quality records.
  • 01

    Smart factories

    Connected equipment, consistent production events and shared data models that give every line and site a comparable operational picture.

  • 02

    Quality inspection

    Vision-based defect detection at line speed, with images, decisions and operator overrides stored for traceability and retraining.

  • 03

    Predictive maintenance

    Condition monitoring on critical equipment that ranks maintenance risk and feeds planned work into the CMMS.

  • 04

    Computer vision

    Detection, measurement, counting and verification models deployed on edge hardware beside the line.

  • 05

    Robotics

    Integration of robotic cells and mobile robots with MES and warehouse systems, including task dispatch, telemetry and exception handling.

  • 06

    Production optimization

    Scheduling, changeover and setpoint recommendations from production, quality and downtime history, reviewed by planners before adoption.

  • 07

    Digital twins

    Line and process models that combine live equipment state with engineering data for monitoring, bottleneck analysis and change simulation.

  • 08

    Supply-chain analytics

    Material, supplier and inventory signals connected to production plans to surface shortages and schedule risk earlier.

Related programs

02Use cases

Use cases on the factory floor.

Starting points with measurable operational value and data that most plants already generate.

  1. 01

    Inline quality inspection

    Surface, assembly and label verification at the station, with rejected parts routed for review and images retained as evidence.

    • Industrial cameras
    • Edge GPUs
    • MES

  2. 02

    OEE and downtime analysis

    Automated capture of downtime reasons and micro-stops, analyzed consistently across shifts, lines and plants.

    • Machine events
    • Production analytics
    • Dashboards

  3. 03

    Changeover and scheduling

    Schedule recommendations that account for changeover times, material availability and maintenance windows.

    • ERP
    • APS
    • Optimization models

  4. 04

    Traceability

    Linking materials, process parameters, inspection results and operators to each unit or batch produced.

    • Genealogy data
    • Serialization
    • Data platform

  5. 05

    Maintenance planning

    Condition-based maintenance for critical equipment, scheduled around the production plan.

    • Sensors
    • Condition models
    • CMMS

03Reference architecture

From the station to the production record.

Inference runs at the line within cycle time. Results flow into the MES so production records stay authoritative, and operator feedback closes the loop on model quality.

PLCs, machine controllers, sensors and industrial cameras at each station provide the raw signals.

  • PLCs
  • Industrial cameras
  • Sensors
  • Barcode / RFID

04Engineering considerations

Constraints that shape factory software.

A model that works in a notebook can still fail on the line. These are the constraints we design for before a camera is mounted.

  • Constraint 01

    Cycle time

    Inspection and decisions must complete within the station's cycle time without slowing the line.

    Engineering response

    • Edge hardware sized to the latency budget
    • Latency measured per station
    • Defined fallback when a model is unavailable

  • Constraint 02

    Variation and drift

    Lighting, materials, suppliers and product variants change. Models trained once degrade.

    Engineering response

    • Controlled imaging setups
    • Drift monitoring on inputs and outcomes
    • Operator feedback into retraining

  • Constraint 03

    Systems of record

    MES, ERP and quality systems own production and quality records.

    Engineering response

    • Integrate rather than duplicate
    • ISA-95-aligned data models
    • Idempotent, auditable writes

  • Constraint 04

    Traceability

    Quality decisions may need to be reproduced for customers, auditors or a recall investigation.

    Engineering response

    • Images and decisions stored with model version
    • Change control for models
    • Records aligned with the customer's quality system

  • Constraint 05

    OT security

    Factory networks connect long-lived equipment that cannot be patched like IT systems.

    Engineering response

    • Network segmentation between OT and IT
    • Outbound-only edge connectivity
    • Device identity and signed updates

  • Constraint 06

    Multi-site rollout

    What works on one line must be repeatable across plants with different equipment.

    Engineering response

    • Reference deployments per station type
    • Configuration over custom code
    • Fleet management for edge devices

05Related capabilities

Engineering disciplines behind the work.

Manufacturing programs draw on these capabilities, delivered by one team that designs, builds, secures and operates the system.
All capabilities

06Concept architectures

Reference architectures for related problems.

These are concept architectures: illustrative engineering references, including patterns from adjacent industries, not descriptions of client engagements.
All case studies

07FAQ

Common questions.

What engineering leaders ask us about manufacturing systems.
01Can computer vision inspection keep up with our line speed?

That is the first thing we test. We measure the station's cycle time, select camera, lighting and edge hardware to match, and benchmark inference latency on representative parts before committing to an architecture.

02Do we need a large labeled dataset to start?

Not always. Many inspection projects start with a modest set of labeled defects plus anomaly-detection methods, then improve as operators confirm or override decisions in production. Data needs are assessed per defect type during discovery.

03How does this integrate with our MES?

Inspection results and production events are written to the MES and quality systems through their supported interfaces, so those systems remain the system of record. We map data to ISA-95-aligned models to keep integrations consistent across lines.

04What happens when a model is wrong?

Every decision is logged with its image and model version. Operators can override results, overrides feed retraining, and each station has defined fallback behavior, such as routing parts to manual inspection, when a model is unavailable or uncertain.

Manufacturing engineering

Bringing intelligence to the production line?

Tell us about the line, the defects and the systems involved. We'll help architect the inspection, data and integration layers.

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