Business interfaces
ERP / MES / OMS / TMS: define master data, order ownership, status feedback and reconciliation rules.
INDUSTRIAL SOFTWARE & AUTOMATION
Complex systems.
One continuous
flow.
From warehouse decisions to machine movements. Software, digital twins and engineering that connect your operation.
SOFTWARE. CONTROLS. SYSTEMS. IN FLOW.
THE SOFTWARE STACK
Every layer has a purpose.
Every movement is connected.
A clear path from inventory and orders to execution and equipment control. Explore where each system fits.
Execution contracts
WMS → WES → WCS: agree task identity, cancellation, retry and completion events across every handoff.
Operating architecture
Scope deployment, access roles, event retention, backup and recovery around the facility’s operating requirements.
ERP / MES↔Enterprise context. Coordinated execution. Equipment-level control.↔PLC / ROBOTICS
PHYSICAL SYSTEM × DIGITAL CONTEXTILLUSTRATIVE SCENE
THE OPERATION, IN PERSPECTIVE
One facility.
Another dimension.
3D DIGITAL TWIN
See the system.
Understand the flow.
Connect the facility model to identified equipment, task events and location data. Specify data freshness and disconnected states so operators can distinguish a live condition from an outdated view.
01
Plan and compareEvaluate rack layout, transport paths and buffer locations against material profiles and agreed throughput assumptions.
02
Monitor and investigateLink equipment states and alarm locations to task context, timestamps and exception histories.
03
Replay and improveUse recorded events to review congestion and interruptions; evaluate strategy changes with a separately defined simulation model.
SYSTEMS ARCHITECTURE
From business intent
to physical execution.
Defined responsibilities. Explicit handoffs.
A shared operational context.
ENTERPRISE CONTEXTERP / MES / OMS / TMSMaster data · production demand · customer orders · dispatch plans
↓ Orders & priorities ↑ Inventory & completion events
WMS → WES: allocated work / WES → WCS: transport missions / Feedback returns to the owning layer
PLC / DEVICE CONTROLLERSMachine sequence · interlocks · device status
AS/RS · Conveyors · AGV / AMR · RoboticsTransport, storage and physical handling
3D DIGITAL TWINShared data context across the system
Equipment identity + location + task events + time stamps → spatial state, alarm localization and event replay.
Reference architecture. Deployment boundaries and data interfaces are agreed per project. Equipment safety remains within the designated safety control system.
BUILT AROUND THE OPERATION
Built for your industry.
Designed for your operation.
Seven industry perspectives, each with a process blueprint, software responsibilities, equipment considerations and validation priorities. These are proposed approaches, configured during project discovery.
TEAM PROJECT EXPERIENCE
Cross-industry experience.
Built on project work.
Project experience of FLOWVERN’s founders and core team across production logistics, warehouse automation and order fulfillment. Explore application, equipment and software configurations by industry.
ENGINEERING & INTEGRATION
Good systems begin
before the first installation.
FLOWVERN connects system planning, software and automation integration, with the operational requirements kept in view throughout.
DESIGN THE WHOLE SYSTEM
A plan that can be built,
integrated and operated.
Material flow & capacity
Profile SKU dimensions, load carriers, inventory cover and hourly demand. Separate sustained capacity from peak bursts; size storage, buffers and workstations around the actual operating calendar.
Layout & equipment selection
Compare pallet AS/RS, Miniload, shuttles and mobile transport against load, travel distance and expansion needs. Include maintenance access, manual exception handling and facility constraints.
Software & interface engineering
Define master-data ownership, task state transitions and equipment telegrams. Specify acknowledgements, duplicate handling, timeouts and reconciliation before connecting ERP/MES to the control layer.
Brownfield integration
Survey installed equipment, PLC programs and legacy interfaces. Identify reusable assets and dependencies, then plan staged cutover, temporary workflows and rollback conditions.
FROM DESIGN TO DELIVERY
Translate the design
into coordinated operation.
Carry the approved layout, interface contracts and operating scenarios into controls implementation, installation coordination and integrated testing. Every handover needs a clear state, responsible owner and recovery path.
- Controls: I/O mapping, device handshakes, alarms and restart sequences.
- Integration: software-to-PLC task tracing and cross-equipment transfer checks.
- Handover: version baselines, operating procedures and maintenance documentation.
PROJECT DELIVERABLES
Make scope and acceptance visible.
- Requirements baseline, material-flow analysis and capacity assumptions.
- Layout drawings, equipment list and responsibility matrix.
- Software functional specification, interface contracts and test scenarios.
- Cutover plan, operating procedures and as-built documentation.
COMMISSIONING & ACCEPTANCE
FAT / interface testing
Verify agreed logic, signal sequences and fault cases before site commissioning where the project permits.
SAT / operating scenarios
Exercise end-to-end flows with representative loads: normal operation, blocked routes, lost acknowledgements, restart and manual recovery.
Performance & handover
Agree measurement windows, order mix, staffing and downtime rules. Record outstanding items, ownership and closure conditions.
From a new facility to the next stage of an existing operation.
Discuss your project ↗APPLIED ALGORITHM RESEARCH
Intelligence, grounded
in physical constraints.
Our AI algorithm research starts with concrete industrial problems: how work is scheduled, how materials move and how systems respond to change.
Research directions · evaluated against operational requirements
TASK → CONSTRAINT → DECISIONILLUSTRATIVE SCENE
FROM HYPOTHESIS TO VALIDATION
Algorithms earn their place
through measurable evidence.
Research is organized around a defined operating problem, available data and a baseline strategy. The following are research and validation directions; deployment scope is determined after feasibility assessment.
Dynamic scheduling
Balance urgent work, shared equipment and downstream buffers.
- Data requirements
- Task timestamps, station status, resource availability and priority rules.
- Validation criteria
- Queue time, starvation, task completion and peak throughput against the existing scheduling policy.
- Research approach
- Rolling-horizon optimization, constraint programming and dispatch heuristics are compared with the current rule set. Model task precedence, resource exclusivity and downstream readiness before tuning the objective.
- Research outputs
- Dispatch policy comparison, constraint catalogue and replayable scheduling scenarios.
Routing & congestion
Resolve transport conflicts while respecting one-way routes and reserved zones.
- Data requirements
- Route topology, travel times, load positions and equipment availability.
- Validation criteria
- Travel time, blocked-route recovery and conflict frequency in replayed scenarios.
- Research approach
- Evaluate graph search and time-window reservations against congestion-aware routing. Represent no-go zones, shared intersections, vehicle capacity and blocked-route recovery explicitly.
- Research outputs
- Route policy, conflict scenarios and recovery evaluation records.
Vision verification
Detect item or packaging differences and route uncertain results to human review.
- Data requirements
- Representative images across lighting, packaging, orientation and uncommon faults.
- Validation criteria
- Missed detections, false alarms and latency, separated by product and defect category.
- Research approach
- Combine barcode checks, image classification and defect detection where suitable. Separate capture quality from recognition quality and define confidence thresholds for manual review.
- Research outputs
- Annotated sample specification, category-level evaluation and exception-handling rules.
Demand & slotting
Study replenishment timing, item placement and changes in order mix.
- Data requirements
- Demand history, stock positions, item affinity, lead times and calendar effects.
- Validation criteria
- Forecast error, replenishment shortages and travel distance on held-out time periods.
- Research approach
- Compare statistical forecasts with learning-based models using chronological validation. Couple SKU velocity, item affinity, handling effort and storage constraints in slotting experiments.
- Research outputs
- Forecast baseline, slotting recommendations and operational sensitivity analysis.
Simulation & capacity evaluation
Explore how layouts, buffers and dispatch rules behave under changing demand and equipment availability.
- Data requirements
- Process routes, cycle-time distributions, arrival profiles, shift calendars and failure/recovery assumptions.
- Research approach
- Use discrete-event simulation and parameter sweeps. Calibrate the model against observed operations, run repeated scenarios and separate model assumptions from measured evidence.
- Validation criteria
- Throughput distribution, queue length, buffer occupancy and bottleneck sensitivity across scenarios.
- Research outputs
- Scenario model, capacity comparison and a traceable assumption register.
Operational anomaly diagnosis
Identify recurring interruptions across tasks, interfaces and equipment, and help operators narrow the investigation.
- Data requirements
- Time-aligned task histories, alarm records, interface queues and maintenance annotations.
- Research approach
- Compare event correlation, process-conformance checks and anomaly detection with rule-based alarms. Distinguish symptoms from root-cause hypotheses and preserve human confirmation.
- Validation criteria
- Detection lead time, false-alert workload, missed incidents and usefulness in historical investigations.
- Research outputs
- Event taxonomy, diagnostic evidence chain and recommended investigation steps.
VALIDATION PATH
Baseline → offline evaluation → shadow operation → controlled pilot → monitored release
Version datasets and models; retain rule-based fallback and human intervention. Monitor input drift and operational exceptions before extending the application.
ENGINEERING THE EXPERIMENT
A repeatable path
from data to deployment.
Define the experiment
Specify the decision interval, constraints, baseline policy and success criteria. Report trade-offs rather than a single isolated score.
Build reliable data
Align equipment and task identifiers, reconcile timestamps and audit missing or duplicated events. Split training and evaluation data by time or site where appropriate.
Stress the scenario
Evaluate peak arrivals, rush orders, blocked routes, device downtime and partial connectivity. Repeat stochastic trials and document the range of results.
Release with control
Use shadow evaluation before limited operational trials. Agree version ownership, observation windows, rollback triggers and the person authorized to intervene.
What a research engagement produces
A problem and constraint specification; a dataset and annotation specification; reproducible baseline and candidate experiments; a comparison report with limitations; and a pilot, monitoring and rollback plan. Scope is agreed for each research engagement.
Discuss a research challenge ↗ABOUT FLOWVERN
Software thinking.
Engineering discipline.
FLOWVERN brings together warehouse software, equipment control, 3D digital twins and automation engineering to address the entire material-flow system.
Our approach begins with material and information flows: establish the operating baseline, define responsibilities across software and equipment, and validate the system against agreed scenarios. Projects can focus on a software layer, a facility upgrade or integrated planning and delivery.
FLOWVERN TECHNOLOGIES, INC.Planned U.S. company · Formation in progress
CUSTOMER SERVICE
Keep the operation moving.
Keep improving the system.
Plan support around the facility’s operating hours, critical workflows and team responsibilities—from launch readiness to incident handling and lifecycle improvement.
Launch readiness & training
Prepare role-based training for operators, supervisors and maintenance teams. Rehearse critical workflows, exception handling and escalation routes before the agreed launch window.
Incident diagnosis & recovery
Collect timestamps, affected tasks, equipment states and recent changes. Prioritize by business impact, agree containment actions and coordinate software or equipment specialists.
Preventive system maintenance
Review service health, interface queues, event logs, storage growth and backup status. Schedule patch assessment and recovery exercises within approved maintenance windows.
Optimization & lifecycle planning
Review bottlenecks and recurring exceptions against an operating baseline. Assess parameter changes, capacity expansion and upgrades with regression testing and rollback plans.
A TRACEABLE SUPPORT PROCESS
Record
Describe impact, time and evidence.
Triage
Confirm severity and responsible teams.
Recover
Agree safe workarounds and recovery steps.
Resolve
Verify operations and document the cause.
Improve
Track preventive actions and follow-up changes.
Define support before go-live.
Support hours, reporting channels, severity definitions, response targets, remote access, site attendance and software/equipment responsibilities are specified in the service agreement.
YOUR NEXT PROJECT
Let's start with
your operation.
A useful brief includes your industry, facility type, SKU and load profile, average and peak order volumes, existing ERP/MES and equipment, current bottlenecks and launch window. Estimates are welcome at the planning stage.
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