DEV Community: Yash Bansal

Designing an AIoT Workflow: From the Sensor Event to Verified Action

Yash Bansal — Thu, 15 Oct 2026 15:53:19 +0000

Industrial AIoT initiatives span devices, pipelines, analytics and operations: a worthy exercise to define is to walk through one event, from initial sensor through to verified action.

Here's a conceptual workflow.

1. Define the event

A connected asset is entering the defined zone, or the machine reported an abnormal condition.

First define the event. Questions to ask:

Which asset or device is responsible for the observation?
When and where did it happen?
Is the observation trustworthy?
Are there any contexts needed to interpret?

Without these basics, downstream analytics can have misleading information.

2. Normalize the data

Industrial settings often have devices that use different formats, identifiers, communications and update intervals. The pipeline would usually normalize observation data in a consistent set of representation and metadata.

A conceptual event could be:


{

"asset_id": "asset-104",

"event_type": "condition_change",

"timestamp": "2026-10-15T10:30:00Z",

"source": "sensor-12",

"data_quality": "pending_validation"

}

This is merely an illustrative schema and not actual Aperture API or production implementation.

Validation is needed to consider missing fields, duplicates, timestamps, identity and unexpected values.

3. Add contextual information

Very rare that a sensor observation captures the whole story.

You may need to associate the event with an asset, location, operating state, maintenance history or the relevant business processes.

Contextual information helps differentiate an anomalous but benign observation from one that requires immediate attention.

It also helps prevent every observation with thresholds being exceeded from triggering as important events.

4. Determine the decision or recommendation

An analytics layer can apply predefined rules, statistical analyses, or employ suitable AI models to interpret the validated information.

It could generate alerts for unusual conditions, recommend inspections based on observations, or even identify potential inventory discrepancies.

The recommendation should indicate uncertainty (if applicable) and retain the evidence needed to support its recommendation.

A model's prediction should not simply be assumed to represent an action that has definitely happened or a particular response is safe.

5. Enforce authorization before actions

If the AIoT initiative involves equipment, control systems, or other operational aspects, extra precautions must be taken to transition from the recommendation to the actual physical action.

A strong design has:

Identity and permission verification
Command validation and limits
Manual approvals (where applicable)
Timeouts, error handling and safe defaults
Audits of decisions and commands
Detection of unexpected outcomes
All designed in accordance with the impact of the initiative

6. Verify if the outcome has occurred

Issuing a command is not the same as verifying that the intended outcome has been achieved.

The system should capture feedback as appropriate and compare with the expected state to detect any discrepancies.

If an action cannot be completed, the workflow has to have a defined response to the failure, rather than simply assuming completion.

A compact architecture

The architecture has the following steps:

Identify → Sense → Validate → Interpret → Authorize → Act → Verify

Validation, authorization and feedback loops are necessary additions to turning a high-level idea for an AIoT initiative into something dependable for operational use.

Aperture Venture Studio writes about a similar Identify–Sense–Decide–Act architecture in its overview of Physical AI and AIoT applications.

Final engineering considerations

The most valuable AIoT architecture would not necessarily involve the most sophisticated model; the most reliable one connects observations and decisions in a manner that takes into account system constraints, data quality, security and authorization.

For industrial developers this means testing end-to-end (including failure scenarios) rather than only measuring the capabilities of the intelligence layer.

Design the Right Inventory Data Flow: Barcode to Stock Quantity Software Requirements

Yash Bansal — Thu, 15 Oct 2026 15:17:58 +0000

Inventory applications are only as good as the events and records that they manage. If a warehousing operation misses a scan or logs a transfer late, the application’s view of the physical environment becomes out of date.

For software architects and developers, the question of inventory accuracy begins with inventory events.

Capture inventory events

A basic inventory management cycle consists of events such as

• RECEIVED – warehouse stock arrival

• TRANSFERRED – movement between warehouses

• PICKED – removal for fulfillment

• RETURNED – returned goods acceptance

• ADJUSTED – inventory correction

Each of these inventory events holds a product identifier, quantity, transaction time, location (from and/or to), and an identifier of the associated business document. Event types and accompanying data depend on the firm’s needs.

Event records serve the primary purpose of enabling audits – responding to inquiries about why the quantity changed. If the application has to determine the point-in-time quantity, it reconstructs it from the list of processed events.

Inventory quantity and identification methods

When reading a barcode or RFID tag, always remember that the means of item identification do not ensure inventory accuracy.

The application has to take responsibility for processing and recording a change in quantity. This is where validation and database design come in for ensuring that the inventory application’s logic does not allow errors to happen. For example, using transaction identifiers and avoiding duplicates supports prevention of inventory overstatement due to an incorrectly processed RECEIVED event. The application must recognize duplicates and reject them. Even if scanning the same receiving label twice would not add quantity twice.

Location and status

The quantity that the firm records as available for sale or use is not always the actual physical inventory.

Inventory management systems have to track quantities by location and status. A separate inventory balance arises for each property or geographic location and different statuses (e.g., available, reserved, damaged, or pending repair).

This approach applies particularly to multilocational businesses.

Transactions and concurrency

When two employees have picked the same last piece of inventory, the application must handle the second request as an error. Without the proper locking or transaction mechanisms at the database level, an inventory application will allow the second fulfillment to proceed despite availability issues.

Systems engineers have to evaluate the application’s requirements for concurrency and choose the necessary data isolation level or introduce application-level checks and balances.

Inventory exceptions

It is necessary to design the inventory reporting or alert system to support investigations into rejected transactions, incorrect adjustments, or significant variances between the application’s count and the actual inventory.

Therefore, the application has to support the collection and analysis of inventory exceptions, such as

• Failed inventory transactions or adjustments

• Inventory adjustments or adjustments that were repeated

• Unusual manual adjustments to inventory quantities

• Inventory system connection or synchronization failures

• Varying results from cycle counts and inventory counts

These data assist operations and technical support personnel in identifying and resolving inventory exceptions.

Hardware and systems inventory exceptions

Barcode scanners, computer programs, and mobile devices involved in automatic inventory management operate in varying physical environments and have differing communication stability. Before finalizing the applications, it is necessary to examine edge cases involving reading integrity, connection stability, and hardware interoperability.

Hardware inventory exceptions include duplicate reading of the same label, incorrect reading, or application freezes or failures. On the systems side, exceptions arise when incorrect data are captured or when communication between peripheral devices and the application is hampered.

The Right Inventory Software Architecture Begins With Physical Inventory Activities

Inventory applications are not simple warehouses with goods and quantity values. These applications have to appropriately capture event data, ensure there are no transaction processing errors, reflect the state of physical inventory locations, and provide visibility into exceptions. Inventory management software architects need to design inventory applications according to the firm’s actual events and needs. Always document the required physical inventory activities at the event level.

To learn about the general concepts and components of inventory management systems, visit The Inventory Master.

Designing an Industrial AIoT Pipeline: From Sensor Events to Alerts for the Downstream Product

Yash Bansal — Tue, 13 Oct 2026 14:49:01 +0000

While attaching a sensor to the thing being sensed and plotting it on a dashboard is straightforward, actually engineering a pipeline of data that translates the event into meaningful operational decisions is rather non-trivial.

An industrial AIoT architecture will have to grapple with the following:

Data collection, validation, processing, inference, and downstream action given an alert.

First: come up with a sensible event model

Let us think of a normalized example of sensing some industrial equipment. The event schema we need could be something along the lines of

asset_id, timestamp, temperature, vibration, operating_state, and sensor_status.

The kind of information collected, of course, depends on the equipment and the particular use case. What is rather important is defining common fields, timestamps, units of measure, identifiers (the way assets and even sensors are identified), and event format.

Data validation, checking for required fields, outliers, staleness, and other vector for errors.

Some very simple validation code in Python would be something like:

def validate_reading(event):

"""Check if event has all expected fields and they are not NaN."""

required_fields = ["asset_id", "timestamp", "temperature"]

for field in required_fields:

if field not in event:

return False

if event["temperature"] is None:

return False

return True

Again, this is something very simple: actual production-grade code will have to do more comprehensive type checking, timestamp validation, unit validation, possible error reports at the sensor level, and other checks that make sense in context.

Consider decoupling event ingestion from processing and rule evaluation for the purposes of reliability, observability, and maintainability.

There are many ways that such a pipeline can be structured:

gateways can receive the events,

the system can ingest them into a storage or processing layer (rules engine or a model),

a separate service can consume processed or raw events and generate alerts,

and there should also be some infrastructure-level monitoring for such a pipeline.

The more generic the infrastructure for processing these events is, the easier it is to use it across different use cases. Depending on the scale and the SLAs required, data may be processed on the edge, on the cloud, or a combination of both.

Choosing the right tools for the job at hand.

Sometimes a simple rule can be better than a machine learning model, if the required performance, interpretability, and maintenance budget are taken into account. Statistic process control-like approaches can be much better at detecting outliers.

At the same time, more complex rules or even black-box ML models may be necessary for more nuanced use cases when operating conditions are barely predictable.

It all boils down to identifying the false-positive budget and business-driven priorities.

Make sure alerts actually have actionable information and can get to the right people or processes.

An alert, if necessary, should have clear recommendations on what to do next, identify all relevant measurements and assets involved, and have all the relevant contextual information such as timestamps and causes. Again, what constitutes an actionable piece of information depends on the use case and the downstream workflow.

Finally, make sure the system is monitored and tweaked in production.

Such a system will benefit tremendously from having dashboards that monitor ingestions and alert rates, data quality, latencies in processing, model performance (if applicable), and business-related metrics when possible. The operating conditions of any industrial setting, equipment, and sensors will constantly change, so the models and pipelines will need to adapt.

Conclusion

Industrial AIoT systems are seldom about the models themselves; building an actual production system is a data and operations-heavy endeavor just as much as it is a ML challenge. Data validation, observability, rules, and even downstream actions must be designed with care. Aperture Venture Studio works with companies across different areas of industrial applications and digital operations; get in touch if you are building or scaling an industrial AIoT solution of your own!

Designing an Event-Driven Visibility Layer for Factory Logistics

Yash Bansal — Tue, 13 Oct 2026 14:38:08 +0000

A persistent problem in manufacturing systems is the misalignment between enterprise records and physical events.

An ERP system may indicate material issue but an MES may show a production progress, or RFID/RTLS infrastructure may register a movement event - what business logic can be derived from these signals?

One viable approach is to design the logistics visibility system as an event processing problem.

Define a small set of events that you want to make actionable:

AssetDetected

ContainerArrived

MaterialIssued

ReplenishmentRequested

WIPZoneEntered

WIPZoneExited

These are just example names - your actual classes will depend on the problem domain, but there should always be an event ID/time, asset/container ID, location/zone, and source device where applicable.

It is crucial to avoid "noise" from the physical world. A single RFID reader or BLE gateway may provide raw observations that require deduplication, validation, or contextualization before producing an actual business event.

Normalize across devices

Modern smart factories often combine RFID readers, BLE gateways, UWB anchors, barcode scanners, vehicle telemetry, and legacy production systems.

Each of these data sources produces messages in different formats and with different levels of fidelity and confidence.

Normalization layer would parse these events into a common set of internal event objects, retaining as much source metadata as needed for debugging and auditing.

Deduplicate and decouple

Wireless devices are prone to produce duplicate messages or get disconnected unexpectedly. The same physical object may appear multiple times in proximity to the same device, or appear in several different locations at the same time due to wireless interference.

The pipeline would need to handle event deduplication, temporal decoupling, and idempotency.

For example, your code would not want to raise a replenishment request for a container that has been several times detected near the same RFID reader.

Enrich events with contextual information

Location data is frequently insufficient for downstream consumption.

Use the domain knowledge to enrich the events with additional data: what was the destination zone, what was the issued material, did it match the expected quantity, or what is the current status of the associated WIP order.

This is where the raw telemetry data turns into actual information.

Decouple detection from decisions

Another pattern that helps production-grade systems deal with the noisy data is to split the detection (what happened) and the decision logic (what should we do).

For example, when a container is seen near a production cell, the first part of the pipeline would determine if it has entered a certain zone - a lower-level detection event.

Then, a higher-level rule engine could analyze this event and a set of other criteria (did the container contain the right materials, at the correct quantity, etc.) to make a business decision: whether to update the item status, confirm a WIP order, or raise a quality control alert.

Monitor the operational performance

Beyond simply processing the events, it can be valuable to measure both the technical and the operational business metrics.

The technical layer should track the health of the pipeline: what percentage of events needed deduplication, contained invalid data, or came from unregistered devices.

The business layer should track how well the visibility layer supports the factory operations: how quickly materials were found, what percentage of expected movements were actually observed, or how frequently inventory discrepancies had to be resolved.

Both technical and business metrics help to evaluate if the pipeline is working correctly, and if it provides sufficient value to the operations team.

The broader industrial logistics visibility problem

PlantLog AI is a deep-tech company that focuses on industrial AIoT applications, including in-plant logistics, asset tracking, inventory management, and material flow visibility. The PlantLog AI website has more information about the company's approach to the logistics visibility problem in manufacturing.

The key considerations from this post - producing validated events and enriching them with business context - are applicable to a wide range of industrial applications, and can be used in conjunction with many different technologies, including RFID, Bluetooth, Ultra-Wide Band, computer vision and others.

Designing AIoT Pipeline: Events to Actions

Yash Bansal — Mon, 12 Oct 2026 15:46:03 +0000

A working AIoT application is rarely limited to an AI or ML model that consumes the output of a sensor. At least some contextually relevant data preparation and operational relevance validation must take place.

Below is a technology-agnostic description of such a pipeline.

We will consider the example of a warehouse with tracking devices.

First, there is the source event

A warehouse tracking application would see the events generated by a reader or a sensor, identifying

Device or asset ID

Event time

Position or zone identifier

Sensor value (if any)

Sensor or event source ID

The list could be expanded from there, but these are the most common fields, with the caveat that some information may not be applicable to a given application.

Next, the data must be validated and normalized

Events can have duplicates, erroneous, out-of-bound values, or be missing some data, especially time and identifier fields. This layer ensures that the event meets the criteria specified for this application, validates and, if necessary, corrects anomalous values, and handles duplicates.

For example, consecutive events from the same source identify movement, but these movements may not actually occur, indicating erroneous data.

Events must be given operational context

What does a position or zone mean to an operator? Depending on the type of asset and the stage of the process, a similar event may need to be treated differently.

The location or status change must be related to a specific asset type, its life cycle, the relevant procedure, and other factors.

Then, a model or a decision logic must extract actionable information

A rule-based system could be used, for example, to find that an asset has been found in a prohibited area or has been delayed for too long in a particular zone.

A trained model could find patterns of behavior similar to previously observed ones and propose a solution or assess the risk of a delay in the process. These are two sides of the same coin and one does not exclude the other. The choice of the method should be dictated only by the specifics of the application.

An action must be taken to involve the operator in the decision-making process.

What does the operator need to do to resolve this situation? Depending on the application, it may require drawing up a report, inspecting the asset, or changing the procedure. The action taken must be specific to the case, and the operator must have the ability to influence the outcome.

Actions involving changes in the state of the equipment or physical processes are usually automated through appropriate techniques and tools. In this case, the model should not specify the action directly but select it from a list of options presented, with an explicit confirmation step. There are many dangers in automatically executing model-specified actions without checking their relevance to the current context.

In addition, for such applications, the question of safety, security, and physical protection of devices from interference and disruption arises.

It is necessary to collect statistics on the results of decisions made to continuously improve the system

Did the action produce the desired result? Did the operator take any action? Was the model correct in its choice, or was it wrong?

It is important to analyze not only the results of the system but also possible changes in the external environment.

There are several pitfalls that often occur in the development and deployment of such systems.

The quality of input data is often not taken into account. Even the best neural networks cannot correct incorrect input data. Contextual conditions are often not considered, which leads to unjustified conclusions. The model’s accuracy often works as the only metric, while other values, such as latency, may be much more important. It often goes unnoticed that the system works in the presence of other processes. This interaction must be considered in the design of the application or system as a whole. The system’s context and assumptions are static and rarely take into account the changes that are happening in the application context. All of this leads to failures and the loss of trust in the entire concept of AIoT.

Finally, a generic system such as this one is usually applied to specific sets of data and requires the implementation of data preparation and operational quality control for each new use case.

This note relates to the work of venture studios Aperture Venture Studio in developing applications involving artificial intelligence and the Internet of Things in industrial environments. They specifically focus on the problems of industrial applications for venture studios, not the AI itself.

In summary, the main takeaway is that the application as a whole, from sensors to final decisions, is much more important than the model itself.

Questions: Which layer tends to cause the most problems for your AIoT systems – data, context, models, or operational applications?

Architecture for Connecting AIoT in Pharmaceutical Industry

Yash Bansal — Mon, 12 Oct 2026 14:16:33 +0000

Pharmaceutical AIoT is not an exercise in connectivity between sensors. It is an information architecture problem that involves devices, event processing, enterprise applications, access control, and operational procedures.

A given facility could have RFID device reads for material movements, BLE observations for tracked assets, environment and process sensors, and production events from an MES. The engineering task is to derive value from that information haystack by transforming different signals into useful information.

The Process of Building Out a Pharmaceutical AIoT Integration

Inventory the sources of information

A typical architecture for pharmaceutical AIoT could feature:

RFID readers for recognition events

BLE infrastructure for observation

Environmental sensors for temperature, humidity, or differential pressure

Industrial gateway devices

MES, ERP, LIMS, or QMS applications

Each of these information sources produces varied signals that reflect different timings, accuracy, and operational contexts. An RFID read is not an undisputable fact about a material transaction.

Normalize the events

One way to approach this challenge is to select a common event structure and populate it with contextual and operational details. Here is an example of a generic event:


{

"event_type": "asset_observation",

"asset_id": "ASSET-1042",

"source": "ble_gateway_03",

"timestamp": "2026-10-12T10:15:00Z",

"location_id": "AREA-B",

"quality_status": "pending_validation"

}

This example is not an official schema of PharmaFlux AI event format. In a production environment, additional fields would be needed to address quality control and versioning concerns and implement appropriate access controls.

More importantly, events represent observations, and observations are not the same as verified facts on the ground. Finding a BLE tag near a specific location does not automatically confirm that an asset has been transferred from one area to another. The associated production or logistics process may have specific rules about the required documentation or controls.

Leverage edge processing

Industrial gateways and edge devices can be instrumental in collecting, aggregating, and transforming event data. The benefits of implementing such a middle layer include:

Resilience to intermittent connectivity issues

De-duplication logic for eliminating irrelevant or repeating events

Protocol conversion for heterogeneous device set

Optimized bandwidth utilization by reducing raw data volume

Such a design would still require appropriate data retention and resynchronization policies for periods of disruption.

Connect to enterprise applications

MES, ERP, LIMS, and QMS applications are typically used to manage production processes in a facility. One may attempt to consolidate information from these systems but should recognize that each application views its set of objects as unique and has a specific set of rules and logic for modification and update.

A generic integration layer would need to recognize and formalize dependencies and conflicts. An example of such a scenario could be a production asset observation from a tracking system and a conflicting location indicator from a maintenance application. In such cases, the combined view should not erase the possibility of manual inspection and updates from either side.

Apply analytics selectively

Event data can be analyzed with AI and rules-based logic to produce alerts about production issues, material misplacement, or irregularities in process flow. Such rules are most useful when implemented in production only after addressing a few critical questions:

What is the rate of false positives and false negatives in different environments?

How do event timing and data lineage impact overall accuracy?

What validation rules and approval workflows are required for different alert types?

What operational review and escalation rules need to be implemented?

Can such AI analytics replace existing manual controls in a given environment?

Analytics tools produce the best results when implemented in narrow contexts with well-defined quality control rules. For life sciences applications, regulations often require special attention to controls and validation procedures.

Design security and observability in

The system’s security model should address device-level permissions, user access controls, network segmentation, data encryption, auditing, and other operational concerns. The same applies to operational monitoring and observability: a system should be able to alert when it stops functioning correctly instead of hiding issues.

Pharmaceutical AIoT Architecture Summary

A properly designed pharmaceutical AIoT system will be able to transform sensor data into verifiable events and tie them to relevant operational contexts. This process inevitably involves normalization, reconciliation, validation, and selective enrichment of data from multiple sources.

Pharmaceutical manufacturing intelligence is the focus of PharmaFlux AI. We invite you to see how our solutions can support tracking, inventory management, and operations across your facility. Learn more about the pharmaceutical AIoT platform we offer at this link.

As you begin to design such architectures for your facilities, we recommend starting with a single use case, a clearly defined set of requirements, and a working event model that reflects ownership and operational realities on the ground.

From sensor data to decisions: A practical AIoT stack

Yash Bansal — Fri, 09 Oct 2026 17:24:59 +0000

Industrial IoT initiatives can capture huge amounts of telemetry data, but the process involves more than getting the information. What most of us who design solutions for a living forget to address are the decisions we must make to get actionable information.

We can think of a practical AIoT system as encompassing five different layers.

1. Sensors and connected devices

An AIoT system always begins with data sources such as temperature sensors, vibration readers, RFID, and machine controllers.

Each type of sensor has different requirements in terms of sampling rate, precision, connection, and power. The choice of devices should be based on the specific needs of the application rather than the coolness of the technology.

2. Edge processing

It is sometimes unnecessary to send all the raw data to a faraway platform.

Edge devices can filter data, perform calculations, keep statistics, detect events, and save results locally or send them to the cloud over a more reliable connection. It is also useful for time-critical processes running in the local environment to have some processing power available.

3. Connectivity and ingestion

The data must reach some system capable of storing or analyzing it. This part of the system depends on the physical infrastructure, which may involve using an industrial protocol, a gateway, or a message broker.

The people building the system should consider such practical details as intermittent connections, device authentication, data validation, and field consistency.

4. Storage and analytics

Time-series databases, along with other technologies, can store the data for later analysis or make it available for operational use.

AI models can analyze this information to identify patterns or anomalies and predict future trends. These results must be interpreted in the context of expected values and compared to determine whether false positives or negatives are not occurring too often.

5. Applications and human decisions

The last step in the system is to provide an interface that displays the results, notifies human operators, or connects to maintenance and business systems. Anomalies in scores rarely provide enough information to make a decision; it is often necessary to analyze the characteristics that make up that score and understand what is happening.

Design considerations that are easy to overlook

Data quality : Missing or incorrect data can cause analyzes to produce faulty results.

Security : Devices must be authenticated, data protected, and only the right people can see the results.

Observability : Data pipelines and AI models must be monitored to ensure that they are doing what they were designed to do.

Interoperability : It is important to design systems around existing industrial infrastructure.

Human oversight : Not all decisions should be fully automated, especially when it comes to safety.

Start with a measurable use case

Rather than attempt to build something big and unproven, it is better to choose a specific use case, such as finding abnormal vibrations in a certain category of machines. Then, a model can be developed, data collected, and an assessment made of how useful it is in practice.

Aperture Venture Studio thinks strategically about industrial innovation at the intersection of AI and IoT. Our perspective can give you a valuable insight into the creation of connected-technology solutions.

You can learn more about us on our website at apertureventurestudio.com .

Engineering consideration: A practical AIoT system goes beyond raw information and analyzes it as a means of making better organizational decisions, connecting the physical world to the business.

How to design an inventory event workflow to prevent stock drifts

Yash Bansal — Fri, 09 Oct 2026 17:12:03 +0000

Your inventory database can become a drifting liability if the events in it are not fully reconciled with physical operations.

A receiving operation creates receipts in one database, a transfer in another, a return processed elsewhere – and what all gets counted as inventory may have little connection to any of these numbers.

Applications which interact with physical inventory should focus on events as the primary mechanism.

Model events rather than quantity

You probably had an idea for a simplistic inventory which only has a SKU and current quantity.

The mutable value suggests that this inventory may change, however, there is not enough information recorded about how exactly the quantity changed.

A preferable way of designing the inventory is to capture the events which affect the quantity:

RECEIPT
PICK
TRANSFER_OUT
RETURN
DAMAGE
ADJUSTMENT

Make sure that the events know which product, quantity, and any applicable warehouses are involved, what time the event was done, who was responsible, and what transaction id, if any, is involved.

One particularly important case here is a transfer between warehouses, where the movement from one is effectively canceled into another in two separate operations.

Both of these actions should bear the same transaction id to allow for reconciliation.

Transaction handling is idempotent

Devices used in warehouses can sometimes be offline or experience connectivity issues.

Operators can mistakenly resubmit the same scanning results if they do not get feedback that a certain item has been successfully processed.

Integration platforms can also malfunction and replay the same requests, leading to the same transaction being submitted multiple times.

An inventory should account for these issues by being able to accept the same transaction multiple times without resulting in extra quantities of product being available.

One way to do this is to allow a unique id or an external key to be specified with every request.

If an identical transaction is attempted again, the application can find that it has already been processed and skip it.

An alternative is to specify such constraints in the database itself.

Do not allow concurrency errors

It is possible that the very last unit of a certain good will be picked by two different operators simultaneously.

The application might read the available quantity just before either operator submits their request.

The database may be instructed to allow both orders, since at the moment of creation, both picked quantities are available.

Depending on the database and the application architecture, this may be prevented with eitherlocking, row-level locking, or through a conditional atomic update.

The most important detail is ensuring that reading the quantity and making a change to it is a single atomic operation.

Available quantity is a derived value

A certain quantity of goods may effectively be unavailable for picking or allocation.

They may have been set aside, damaged, or simply in the process of being received and counted for quantity inspection.

The application needs to make a distinction and treat these goods differently from those which are immediately available for shipping or consumption.

A particularly useful practice here is to make a distinction between a general ‘on hand’ quantity, and other derived values.

Available = On hand − Reserved − Unavailable.

Always reconcile events against physical operations

No matter how good the application is, it will still have errors.

Cycle counts and reports on exceptions which the application generates can be useful for making manual adjustments.

Those adjustments, however, should make new events in the same event queue as any others.

This will keep the reconciliation process consistent.

Ensure proper scanner and application inventory integration

Barcode or RFID devices can generate identification events, but the application itself needs to manage the validation, deduplication, reconciliation process, and database mapping.

There should be some validation or de-duplication routine before the value is even considered to be entered into the database as inventory.

Testing is also essential when considering a new application in this space.

Various edge cases, such as disconnections, multiple submissions, partial transfers, stalled transactions, and concurrent reservations should ideally be tested.

For those seeking more information on different forms of inventory management solutions, including RFID or barcode scanning platforms, and general application support, The Inventory Master features a comprehensive list of solutions.

Event-based inventory software design guide

Quantity management applications tend to fail in specific ways depending on what assumptions are made.

A reliable system should make events the first differentiating factor, build transactions around them, and ensure that any concurrency problems are resolved.

Designing AI Systems for the Physical World: Identify Sense Decide Act

Yash Bansal — Thu, 08 Oct 2026 15:03:43 +0000

When developers think about an AI system, the architecture often begins with data flowing into a model.

Physical world systems need a broader awareness.

A useful industrial AI architecture must take into account what is, what happens, what it means, and what should happen.

One possible view of that flow is:

Identification

↓

Sensing

↓

AI Decision

↓

Physical Action

↓

Feedback

Aperture Venture Studio is currently using a similar four-engine architecture for their Physical AI and AIoT work.

1. Identification

Prior to analysis of an event, the system needs to know the context of what was sensed.

Which asset?

Where?

Who or what caused the event?

What processes are involved?

Identification technologies define the identity, location, motion, and traceability of any physical entity; its digital "DNA".

Sensor data without that context can be like unstructured data.

2. Sensing

The question then is:

What is happening?

Some form of sensors can provide the information about condition, environmental factors, movement, performance, or operations.

But the key engineering challenge isn't to provide more telemetry; rather, it's to deliver the telemetry that matters to a particular decision.

3. AI Decision

When you can get relevant information about the physical world, AI can begin to add value in transforming that information into something more useful.

Possible functions include:

Anomaly detection

Forecasting

Diagnosis

Root-cause analysis

Optimization

Risk prioritization

Recommendations

The AI Decision Engine described by Aperture takes physical-world data as well as enterprise information and operational constraints to make a recommendation.

That last factor is critical: an AI engine making suggestions for action in context of enterprise constraints.

Otherwise, there's little value in a prediction.

4. Physical Action

Decisions can take many forms, depending on the application. But the question the system must answer is:

What should happen?

The result might require that humans, processes, a machine-to-machine exchange, a control system, or robots perform specific bounded tasks, such as altering production lines, opening a switch, or directing a forklift.

But an important engineering principle is that AI decisions should not be converted automatically to free-reign physical actions.

The Aperture architecture includes advisory, human-approved and fully automated decision-making, guided by limits and operating rules which define how decisions get converted into action.

5. Feedback

Actions shouldn't be taken in isolation, but evaluated for effectiveness and their impact on other activities. Hence the next question must be:

Did what happen match the intended outcome?

Feedback creates a loop in an AI system, comparing what was expected against what actually occurred and how the real-world changed as a result of specific actions.

The end result is a pattern like this:

Observe → Understand → Decide → Act → Verify

↑ |

└─────────────┘

Such a closed-loop system in manufacturing, logistics, construction, mining, and other fields involving physical forces, can be extremely valuable in influencing operations and outcomes.

The engineering takeaway

Engineering Physical AI is not about finding a better model, but about defining systems that take into account:

Data quality,

identity, context, delays,

constraints,

security,

human authority and judgment,

integration,

feedback

That systems approach is what makes industrial AI challenging and rewarding.

A good place to begin further research into the architecture is at Aperture Venture Studio.

Designing an Inventory Technology Stack: From Identification to Automation

Yash Bansal — Thu, 08 Oct 2026 14:39:53 +0000

Inventory systems tend to think about inventory software as some sort of monolith.

In practice, modern inventory systems can have various layers:

Identification → Connectivity → Software → Automation → Reporting

These layers can be helpful in designing an inventory technology stack based on operational needs.

1. Identification

All systems must start somewhere, and for physical assets it usually means identification.

Barcode, QR code, RFID, NFC, smart labels – there are many ways to identify an object in order to tie some physical information to a digital representation.

However, the primary question should be: what is the best way to identify an object for a particular environment?

The value of using inventory identifiers extends to other technologies, such as connectivity and tracking, which can further affect the design of the inventory technology stack.

2. Tracking and Connectivity

Once inventory objects are identified, the next question is: what to do with this information?

Some technologies, such as BLE, GPS, Wi-Fi, cellular IoT, and LoRaWAN, can be used in a variety of ways to address inventory tracking and location-related needs.

The key is to recognize that different environments may require different approaches to connectivity.

Tracking and location are also often dictated by the physical environment, so this aspect should also be considered when designing an inventory technology stack.

3. Inventory Software

Software is often a key element in any inventory system, acting as the “glue” between other technologies.

An inventory management system typically handles information about inventory items, assets, locations, orders, and reports, among other things.

Software can be cloud-based or on-premises, depending on the needs of the business.

The most important consideration for inventory software is interoperability: the ability to work with other systems used by the company.

Once the information is captured and processed, it becomes much more useful to have it work with other business systems.

For example, ERP systems often serve as central repositories for financial and operational information, enabling better decision-making.

Thus, designing an inventory technology stack also involves considerations about software interoperability.

4. Automation

At the level of information technology, there are few reasons to automate repetitive processes.

However, at the level of inventory technology stacks, automation can be critical to minimizing repetitive human effort.

Depending on the environment, various technologies can help automate inventory management and warehousing: automated storage and retrieval systems, conveyor belts, sorters, robotic pickers, pick-to-light systems, guided vehicles, etc.

However, the main point is that, wherever possible, monotonous activities should be automated to minimize human error and operational cost.

5. Reporting and Decision-Making

Finally, any inventory technology stack should be considered in terms of reporting and decision-making.

Most often, dashboards and reports are the primary way in which inventory information is consumed by managers and executives.

However, the ability to report is directly dependent on the ability to capture and process information.

Thus, if any element of the inventory technology stack is selected incorrectly, it can negatively impact reporting and, consequently, the decision-making process.

The Inventory Master demonstrates a number of technologies related to the broader inventory technology stack by focusing on tracking and identification, inventory software systems, IoT technologies, and automation/robotics.

The Big Picture: Designing an Inventory Technology Stack

The Inventory Master provides a look at the broader inventory technology stack by focusing on technologies related to identification and tracking, software, and automation.

However, a practical implementation of an inventory technology stack rarely starts with a discussion of technology.

Rather, it is usually necessary to look at the process and ask what information is generated at what stage, how this information can be captured, how it should be stored, who needs this information, and how it gets to these people in a timely manner.

What is more, it is necessary to determine what actions, if any, should be taken based on the information obtained.

This chain of questions can help in choosing the inventory technology stack that will bring the most practical benefits.

The Inventory Master aims to help in making the right choices at each stage of the inventory management process.

By focusing on the details of inventory tracking and management, it covers some of the low-level technologies that can be useful in designing an inventory technology stack.

Designing an Industrial AI System: Identify Sense Decide Act

Yash Bansal — Wed, 07 Oct 2026 13:22:47 +0000

Industrial AI systems have a subtly different engineering problem than many software-centric AI applications.

The system has to reason about assets, equipment, processes, locations, and measurements as real-world objects with constraints and physical outcomes

One helpful way to think about the overall architecture is as a cycle:


IDENTIFY

↓

SENSE

↓

DECIDE

↓

ACT

↓

VERIFY / FEEDBACK

↺

1. Identify

Before an AI can reason about anything, it needs to have a context

What asset are we talking about? Where is it? What process does it belong to? What person, vehicle, material, work order, or production event is associated?

There are many ways to identify physical objects, including RFID, BLE, UWB, GPS/GNSS, RTLS, NFC, computer vision, and more depending on the setting.

The point of identification is to create a concrete association with real-world objects.

2. Sense

Identification creates a context; sensing adds information about the current state

Temperature, vibration, pressure, electrical characteristics, environment, position, velocity, level, energy consumption, structural integrity, or other characteristics of interest.

All of this adds up to a comprehensive state description of the physical world.

3. Decide

The AI decision layer takes information from identification, sensing, history, enterprise data, and constraints to produce an actionable outcome.

This could take the form of

an anomaly detection,

forecast,

predictive maintenance,

quality assessment,

risk scoring,

demand forecast,

inventory projection,

or recommendation for downstream actions and processes.

Crucially, the decision can be advisory or require human confirmation based on the use case.

4. Act

At the simplest level, the act layer takes a decision from the AI and applies it to the physical world.

This could mean sending a message to an operator, generating a work order, recommending an action, or even sending a command to a robot or machine.

Physical systems often require access control, authorization, constraints, validation, interlocks, cyber-security, auditing, human confirmation, and fail-safes before taking any action.

Actuation should always be bounded and controlled within the larger system.

5. Feedback

Once an action has been taken, the system may need to observe the outcome.

This might mean evaluating whether the right outcome was achieved, the equipment responded, the asset moved, the process changed state, or any other relevant action.

New measurements and status reporting from the field can be used to close the loop and make new decisions.

This is why the diagram includes a feedback step that creates a closed loop from Act back to Identify.

Why architecture matters

A modular architecture is crucial because it enables the system to grow and change.

An initial pilot might be able to use identification and location data to create value.

As the use case matures, additional data from sensors can be added, connected to AI models that generate new insights, and lead to orchestrated actions or physical processes.

This is one of the ideas behind the AIoT and Physical AI architecture developed by Aperture Venture Studio.

The fundamental engineering insight is that industrial AI systems should not be viewed as standalone models, but rather as components within a larger system that incorporates physical context, operates within constraints, and has feedback from the real world.

Why Data Integration Is the Hard Part of Industrial AI

Yash Bansal — Wed, 07 Oct 2026 12:49:58 +0000

The act of building an AI model is often depicted as the difficult part.

In practice, data integration can be just as hard, especially in the pharma context.

A typical pharma operation has multiple sources:


RFID ───────┐

BLE ────────┤

Sensors ────┤

Equipment ──┼──> Edge / Integration Layer ──> Analytics / AI

MES ────────┤

ERP ────────┤

LIMS ───────┤

QMS ────────┘

Each source has a different purpose, and potentially different data structures.

An RFID system may answer:

Where is the asset?

A manufacturing system may answer:

What production stage is active?

An ERP system may answer:

What inventory is available?

A lab system may answer:

What testing information is associated with the material?

Each individually is helpful. Combined, they can be much more powerful.

Context Changes the Value of Data

If an asset's position changes, that alone is informative.

However, if the system can associate that movement with a production activity, equipment state, inventory requirement or maintenance event, the operational value becomes much richer.

This is one reason AIoT architectures are interesting.

The IoT layer provides physical world data.

The integration layer connects these various sources.

The AI layer can then analyze the relationships and patterns in that information.

Why Edge Computing Matters

Industrial environments can also benefit from processing information closer to where it is generated.

Edge architectures can help reduce unnecessary data movement, support local processing and provide a bridge between physical devices and enterprise applications.

The exact architecture will vary depending on the operational requirements, but the principle is consistent:

AI cannot be separated from the quality and context of the data feeding into it.

For pharma manufacturers, this is particularly important because operational information may span equipment, materials, people, production, environmental variables and quality processes.

Platforms such as PharmaFlux AI approach the problem by bringing AI, IoT, RFID, BLE, edge technologies and pharma systems into a connected manufacturing environment.

The interesting engineering challenge isn't simply “How do we add AI?”

It's:

How do we build an architecture where AI can understand the physical operation?

That is where industrial AI becomes much more than a chatbot or prediction model.