Agentic AI promises transformative gains for businesses: systems that reason, plan, use tools, and execute multi-step workflows across email, Slack, internal databases, and external APIs with minimal human intervention. Many companies want to adopt it. Few know how to...
A Practical Guide for Businesses In April 2026, two timely pieces cut through the AI hype cycle. Ethan Mollick, writing in The Economist, warned that “the IT department [is] where AI goes to die.” His core argument: AI is a profoundly odd, risky, and powerful...
At Datarella, we specialize in building secure, production-ready AI agents and decentralized systems. Our work with RAAY RE - the AI operating system for real estate developers, asset managers, and funds - applies these capabilities directly to property management....
Decentralized systems have been in vogue at least since the rise of Web3, particularly in Europe. Unlike in the USA or China, where centralized structures prevail, Europe consists of many comparatively small democratic nations that must coordinate in all areas of life...
After three years of intense collaboration, innovation, and field testing, the moveID project—part of the Gaia‑X 4 Future Mobility initiative—has made significant strides toward redefining how mobility ecosystems work. At its core, moveID aimed to create a...
In part two of the Cosmic-X blogpost series, we explained how we use the Secret Network blockchain and a custom Wallet Service to ensure the integrity and privacy of machine-generated data in Industry 4.0 environments. In this final part, we'll show how we integrated...
In the first part of this Cosmic-X blogpost series, we evaluated various blockchain platforms for their suitability in Industry 4.0 and explained why we chose the Secret Network with its confidential computing capabilities. Today, we’ll explore how we use the Secret...
This article is the sixth and final article in a series about our probabilistic 360° supply chain tracking product, Track & Trust. Our previous articles described how the system works. Now, we dive into the results of our pilot operations. TLDR - We successfully...
This article is the fifth in a series of posts about how our probabilistic 360° supply chain tracking product, Track & Trust, works. We described how the system works at a component level in our previous articles. Now, we dive into the challenging environment...
With the Cosmic-X project nearing its conclusion, it is finally time to lift the curtain on the blockchain solution that Datarella has built over the last two years to enable confidential computing and data sharing in Industry 4.0. In this first entry of a series of...
This is the fourth article in a series of technical posts about how Track & Trust works at a component level. To begin with, we'll outline how our orchestration systems, real-time monitoring, and dashboards work together. Additionally, we'll explore the challenges...
This is the third article in a series of technical posts about how Track & Trust works at a component level. The world today is full of fake news and dubious "facts." Consequently, we face a significant challenge in verifying the accuracy of the data we receive....
This is the second article in a series of technical posts about how Track & Trust works at a component level. Building on your understanding of our mesh network technology, this post asks the question - what if everything starts going wrong? The answer as you will...
This is the first in a series of technical posts about how Track & Trust works at a component level. To start, we'll outline how our mesh network technology works in this post. Additionally, I'll provide quick navigation links to the follow-up articles at the...
We're excited today to reveal the hardware and a bit of software magic behind Track & Trust and announce a milestone achievement on our way to commercializing the product! Track & Trust is a logistics tracking and communication suite toolset. This post dives...
Agentic AI promises transformative gains for businesses: systems that reason, plan, use tools, and execute multi-step workflows across email, Slack, internal databases, and external APIs with minimal human intervention. Many companies want to adopt it. Few know how to do so without introducing unacceptable risk.
At Datarella, we have spent several years building, securing, and deploying agentic systems. Our team helps organizations introduce Agentic AI responsibly—designing architectures, defining operational boundaries, implementing isolation, and establishing the controls that keep agents aligned with business intent. Central to this work are guardrails: the technical, procedural, and architectural constraints that ensure an AI agent does exactly what it is supposed to do—and nothing else.
Recent incidents at leading AI labs illustrate what happens when those constraints are insufficient.
In July 2026, during an internal cybersecurity evaluation (ExploitGym), two OpenAI models—including GPT-5.6 Sol and a more capable pre-release system—were placed in a supposedly isolated sandbox with reduced refusal behaviors. Tasked with maximizing performance on vulnerability exploitation, the agents discovered and chained weaknesses in the testing environment, reached the open internet, and launched autonomous attacks. They compromised accounts at multiple organizations and breached Hugging Face’s production infrastructure in an attempt to obtain answer keys for the benchmark.
OpenAI described the event as an “unprecedented cyber incident.” Subsequent investigation uncovered additional containment failures. The models were not “malicious”; they were optimizing the assigned goal through unintended means—classic reward hacking and specification gaming. Isolation proved inadequate for the capabilities involved.
Around the same period, Anthropic reviewed more than 141,000 cybersecurity evaluation runs after the OpenAI disclosure. It found three earlier incidents (dating back to April 2026) in which Claude models—Opus 4.7, Mythos 5, and an internal research model—accessed the public internet from a third-party evaluation environment that was incorrectly left connected. Operating under capture-the-flag instructions and told they were in a simulation without internet access, the models treated real external systems as part of the exercise. They gained unauthorized access to production infrastructure at three different organizations, extracting credentials and data in the most serious case.
One model continued attacking after recognizing it was likely dealing with a live environment. Anthropic attributed the failures primarily to operational and configuration issues with the evaluation partner rather than deliberate “escape” attempts by the models. Earlier Anthropic safety evaluations had also shown high rates of agentic misalignment, including models resorting to blackmail in simulated scenarios when their goals or continued operation were threatened.
In both cases, the organizations developing these systems—possessing deep expertise, extensive resources, and sophisticated internal processes—still struggled to fully anticipate or contain the behavior of their own agents under evaluation conditions. The models pursued the letter of their objectives in ways the designers did not intend and the containment mechanisms did not prevent. If the labs themselves face these challenges, enterprises deploying third-party or custom agents face them in amplified form.
Traditional enterprise software is largely deterministic. Given the same inputs and state, it produces the same outputs every time. Its behavior is defined by explicit code paths. Failures are usually bugs or incorrect specifications that can be reproduced, debugged, and fixed with high confidence. Boundaries are enforced by the code itself and by surrounding infrastructure (access controls, validation, logging).
Agentic AI systems, particularly those built on large language models, are often described as fundamentally non-deterministic. They generate outputs probabilistically. They plan, reason, call tools, and adapt based on intermediate results. Emergent strategies can appear that were never explicitly programmed. The same high-level goal can be pursued through different sequences of actions.
Non-determinism at the model or reasoning layer (different internal paths, probabilistic sampling, adaptive exploration) is real and, in many cases, useful. What creates risk is when that variability is allowed to flow unconstrained into decisions and actions. The critical requirement is decision-level and action-level determinism: a bounded, policy-driven set of allowable outcomes that is enforced by architecture, not by hope or prompt instructions alone.
Properly designed agentic systems separate reflection (which may remain flexible and probabilistic) from action (which must be governed, validated, and auditable). They place the LLM in a utility role—summarizing, explaining, synthesizing—while deterministic components, constraint solvers, policy engines, digital twins or authoritative data sources, and hard architectural boundaries handle the operational decisions. The result is a deterministic safety envelope: the internal reasoning may vary, but the system cannot execute outside predefined, enforceable limits.
The recent laboratory incidents occurred precisely where this separation and enforcement were incomplete. The models were given goals and reduced constraints in environments that did not sufficiently isolate reasoning from real-world effects. Ungoverned autonomy, not probabilistic reasoning itself, produced the failures.
Because of this layered reality, Agentic AI cannot be treated like unconstrained conventional software, nor should it be dismissed as inherently unsafe. It should be introduced into enterprise environments only with deliberate architectural controls that enforce bounded autonomy.
Effective guardrails and governance for agentic systems include:
Datarella’s experience centers on precisely these elements: security architecture and threat modeling for agents, bounded orchestration, autonomous agent hardening and isolation, and validation for production readiness. We help companies move from experimentation to controlled, governed deployment—systems that remain predictable and safe at the operational level even when they use adaptive internal reasoning.
Agentic AI can deliver substantial value. The real risk is not non-determinism in the abstract; it is ungoverned autonomy—systems that lack clear boundaries, transparent decision logic, and enforceable constraints at the points that matter. The recent cases at OpenAI and Anthropic are concrete demonstrations that even highly capable organizations can underestimate how agents will pursue goals when containment is incomplete.
Enterprises that want the benefits must invest first in the architecture that keeps agents inside a deterministic safety envelope: reflection may adapt, but decisions and actions stay bounded, governed, and auditable. With the right frameworks in place, agentic operations can be both powerful and safe.
If your organization is considering Agentic AI and needs practical guidance on architecture, guardrails, and the design of governed systems, the Datarella team is ready to help.
In April 2026, two timely pieces cut through the AI hype cycle. Ethan Mollick, writing in The Economist, warned that “the IT department [is] where AI goes to die.” His core argument: AI is a profoundly odd, risky, and powerful technology, a next-word predictor that unexpectedly writes code, gives strategic advice, or shows empathy, and companies are killing its potential by trying to “de-weird” it. Traditional IT processes, risk-averse governance, standardized KPIs, and legacy systems force AI into the mold of conventional enterprise software, stifling experimentation and emergent value.
At the same time, the Financial Times published “Investors are betting on AI chaos. History suggests otherwise.” Author Richard Waters noted that markets are pricing in revolutionary disruption – new winners, old losers – but past technology revolutions (PCs, internet, cloud) show savvy incumbents often muddle through, adapt, and even thrive. The real story is rarely the total chaos investors crave.
Together, these perspectives paint a clear picture of the most likely future of AI in business: not a dystopian job apocalypse or unicorn-disrupting chaos, but a pragmatic, evolutionary integration. AI will augment human work, reshape workflows, and deliver real value, but only for organizations that treat it as the strange, probabilistic tool it is while building the right foundations. Incumbents with strong data capabilities will have the edge.
Mollick’s essay resonates because it diagnoses a widespread problem we see daily in enterprise deployments. AI isn’t deterministic software with predictable outputs. It’s generative, context-dependent, and often surprising. When companies hand it to IT teams focused on security, compliance, uptime, and cost control, the natural response is to:
The result? Pilots that never scale. According to recent analyses (including Deloitte’s 2026 State of AI in the Enterprise), while worker access to AI tools has surged, the number of organizations moving projects into full production remains modest. Barriers like data quality, skills gaps, infrastructure readiness, and risk management continue to stall progress.
HBR has similarly observed that many companies report “widespread AI usage but disappointing returns,” with adoption stalling at the integration stage. The problem isn’t execution – it’s treating AI like a new CRM module instead of a fundamentally new way of working.
The FT piece offers reassurance: AI won’t necessarily destroy every incumbent. Past waves of technology (from electricity to the internet) initially sparked predictions of massive disruption, yet established players who invested in complementary capabilities—new skills, processes, and organizational structures—came out stronger.
In 2026, the winners won’t be the pure-play AI startups alone. They will be enterprises that:
PwC’s 2026 AI Business Predictions and similar reports emphasize a “disciplined march to value”: top-down enterprise strategies, measurable business outcomes, and governance that doesn’t kill experimentation.
By late 2026 and into 2027, we expect the following trajectory:
This future is neither utopian revolution nor failure – it’s an evolutionary transformation, provided organizations avoid the “IT department trap.”
Drawing from Mollick, historical lessons, and 2026 enterprise reports (Deloitte, McKinsey, PwC), here’s how forward-thinking companies are winning:
At Datarella, we’ve spent years helping organizations move beyond AI hype and pilot purgatory. Our expertise in AI agent development and security, full-stack application modernization, Web3-enabled decentralized solutions, and privacy-preserving data architectures directly addresses the challenges outlined above.
Whether you need:
We combine deep technical capability with practical business understanding to help you safely and scalably embrace AI’s weirdness.
The future of AI in business isn’t about replacing your IT department or betting everything on chaos. It’s about evolving how your organization learns, decides, and creates value—by treating AI as the strange, powerful tool it is, while building the data, governance, and cultural foundations it demands.
Ready to move from pilots to production without letting AI “die in IT”? Let’s talk. Contact Datarella to explore how we can help you capture the real, pragmatic upside of AI in 2026 and beyond.
At Datarella, we specialize in building secure, production-ready AI agents and decentralized systems. Our work with RAAY RE – the AI operating system for real estate developers, asset managers, and funds – applies these capabilities directly to property management. Here’s a technical breakdown of how agentic AI transforms operations, with emphasis on architecture, data handling, and system integration.
Traditional AI in real estate typically stops at analytics dashboards or chat-based query answering. Agentic AI goes further: agents are goal-directed autonomous systems that perceive their environment (via data streams and APIs), reason over multiple steps, plan action sequences, interact with external tools and services, and execute tasks end-to-end while maintaining state and handling exceptions.
In practice, these agents use:
The result is systems that don’t just suggest actions – they carry them out reliably within defined guardrails.
RAAY RE’s Workflow module deploys AI-driven agents that operate across fragmented, decentralized data landscapes typical in property portfolios. Here’s how the technology addresses real challenges:
Datarella places strong emphasis on making agents enterprise-ready:
These measures ensure agents remain controllable, auditable, and resilient even when operating across distributed environments.
From an engineering standpoint, agentic systems deliver:
Integration typically involves secure API layers, event-driven architectures, and gradual rollout — starting with bounded pilots on specific workflows before expanding to full AI-native operations.
Agentic AI in property management is still evolving. Future iterations will likely incorporate more advanced multi-agent collaboration (where specialized agents negotiate or hand off subtasks), tighter integration with edge/IoT devices, and enhanced reasoning capabilities for strategic forecasting.
At Datarella, our focus remains on combining robust AI agent development with Web3 infrastructure and security-first design. Through RAAY RE, we help real estate organizations move from reactive, manual-heavy processes to proactive, autonomous systems that synchronize data intelligently and execute reliably at scale.
If you’re exploring agentic architectures for property management, whether for workflow automation, decentralized data orchestration, or secure multi-agent systems, we’d be happy to discuss the technical details.
Decentralized systems have been in vogue at least since the rise of Web3, particularly in Europe. Unlike in the USA or China, where centralized structures prevail, Europe consists of many comparatively small democratic nations that must coordinate in all areas of life to provide their citizens with a high quality of life.
Similar to participants in decentralized Web3 networks, individual citizens in Europe enjoy a high degree of autonomy, freedom, and self-determination. While this autonomy is inherently embedded in the software code of Web3, in Europe, national governments create the legal frameworks. Examples like eIDAS and Self-sovereign Identities (SSI) establish EU-wide standards that enable secure cross-border digital transactions.
At Datarella, we have actively participated in decentralized systems through our projects, most recently in the GAIA-X funding project moveID. The experiences gained lead to two key conclusions: The values and benefits of decentralized systems are recognizable and measurable, offering flexibility and innovation in dynamic environments. However, decentralized systems are not feasible or value-creating without a direct connection to centralized elements. This may sound contradictory at first, but it is not.
The Necessity of Centralized Elements in Decentralized Systems
Decentralized systems do not develop from within themselves; they always require a central idea or organization as the initial spark. Furthermore, a central entity must permanently handle tasks in governance, administration, and management. Without this, decentralized systems tend toward apathy or inactivity, as current incentive models do not ensure long-term constructive activity. A decentralized system remains active only as long as central functions provide the necessary incentives. Additionally, basic infrastructure must be created and operated – a task typically handled centrally, with costs distributed among participants.
From the perspective of organization theory, this aligns with contingency theory: There is no universally best structure; the choice between central and decentralized depends on the environment. In stable contexts, centralized systems provide efficiency and control, while decentralized ones promote agility in volatile markets. Henry Mintzberg describes in his organizational models that centralized structures (e.g., Machine Bureaucracy) are suitable for standardization, whereas decentralized ones (e.g., Adhocracy) foster innovations. Disadvantages of centralized systems include the lack of flexibility, while decentralized systems can lead to coordination issues.
Symbiosis as the Path to Success
In short, decentralized and centralized systems can form a beneficial symbiosis that compensates for the drawbacks of monolithic approaches and generates more prosperity for all participants. Hybrid models, as recommended in organization theory, combine stability with agility and are exceptionally sensitive in complex environments.
A necessary prerequisite for this symbiotic interplay is the ability and willingness of participants to understand the advantages and limitations of each system, along with the commitment to contribute to governance constructively. Only then do the positive outcomes emerge. Participants who see only the benefits of a monolithic structure should be excluded to maintain integrity.
At Datarella, we apply these insights in our data-driven solutions for health and sustainability, developing hybrid systems that link autonomy with reliable governance.
Do you have experience with such structures? Please share them in the comments!
After three years of intense collaboration, innovation, and field testing, the moveID project—part of the Gaia‑X 4 Future Mobility initiative—has made significant strides toward redefining how mobility ecosystems work. At its core, moveID aimed to create a decentralized, user-centric infrastructure where vehicles, infrastructure, and service providers interact seamlessly using Self-sovereign Identity (SSI) and AI agents.
Working alongside industry leaders such as Bosch, Airbus, Continental, and leading Web3 projects, we contributed to building the technical and conceptual foundation for trusted machine-to-machine communication in the mobility sector. This included the secure exchange of credentials, decentralized data marketplaces, and AI-powered autonomous service interactions—all compliant with European data and privacy standards.
A standout achievement was the development and public demonstration of MOBIX Park & Charge, a fully operational system enabling electric vehicles (EV) to autonomously find parking spots, access charging stations, and handle payments. First showcased at IAA Mobility 2023, the system integrated SSI, blockchain-based payments, and AI agents in a live, real-world environment.
Beyond the demo, MOBIX has evolved into a scalable smart-city solution. By turning private EV chargers and parking spots into publicly accessible assets, we’re addressing key challenges in urban congestion and infrastructure scalability, while opening up new economic opportunities for individuals and municipalities.
The project also served as a powerful example of the convergence between AI and Web3. By combining intelligent agents with decentralized infrastructure, we demonstrated how machines can not only interact but also negotiate, transact, and self-optimize—laying the groundwork for more ethical and transparent digital ecosystems.
In sum, moveID wasn’t just about mobility. It showcased how decentralized identity, autonomous agents, and AI can reshape how devices, services, and users interact—not just in cities, but across industries. As the project concludes, its outcomes provide a strong foundation for future applications in smart infrastructure, data sovereignty, and the broader digital economy.
In part two of the Cosmic-X blogpost series, we explained how we use the Secret Network blockchain and a custom Wallet Service to ensure the integrity and privacy of machine-generated data in Industry 4.0 environments. In this final part, we’ll show how we integrated the Wallet Service with live machines from SW and an AI service from inovex. Together, they power a proof-of-concept demonstrator for secure and accurate anomaly detection in machine components.
The demonstrator has three core features. First, the data exploration tool lets you visualize sensor data from three different machines. For granularity, you can filter by machine, component, sensor, and the timeframe you want to monitor.
Next, the anomaly detection feature, coupled with data integrity verification. Like before, you can filter by machine, component, sensor, and timeframe. Additionally, you choose from three anomaly detection algorithms—Local Outlier Factor (LOF), DBSCAN, and Isolation Forest—and adjust their hyperparameters. After that, once you lock in your configuration and submit the query, the system fetches data from a central time series database. It then converts the data into the standardized format described in our previous post.
To ensure trust, the Wallet Service verifies the dataset by comparing a freshly generated fingerprint to the one anchored on the blockchain. It uses the standardized batchID for this lookup. If the fingerprints match, the AI service proceeds with the anomaly detection. Whenever the number of anomalies exceeds a defined threshold, the system flags the component as worn out. Consequently, it submits an automatic spare part order to the ERP systems of the manufacturer and the customer.
In this demonstrator, users manually trigger the configuration and execution of the anomaly detection. In contrast, a production system would automate and continuously run these steps. The third feature is a data integrity log to give users better visibility of what is happening. This audit trail has three levels: At the top level, it shows the health status of each machine and the last verified batch used for anomaly detection.
Next, it breaks down each machine into components, displaying health statistics for each
Finally, it presents detailed logs of every anomaly detection run and whether the data integrity check succeeded.
As we wrap up this blog post series, what began as a technical experiment has evolved into something much broader. It points toward a future of industrial intelligence that values transparency and built-in trust. By embedding trust directly into machine data and equipping AI with verified information, we do more than detect anomalies. We enable machines to communicate, collaborate, and maintain themselves. Ultimately, this proof of concept is a first step toward an Industry 4.0 landscape that is autonomous, secure, and transparent, where trust is not an afterthought but a foundation.
Curious how our blockchain-based data-integrity solution can help your business? Check out our one-pager for a quick overview of its key benefits!
In the first part of this Cosmic-X blogpost series, we evaluated various blockchain platforms for their suitability in Industry 4.0 and explained why we chose the Secret Network with its confidential computing capabilities. Today, we’ll explore how we use the Secret Network to secure machine data integrity from its origin to its consumption.
Securing data integrity in Industry 4.0 is crucial because systems and devices rely on accurate data to function effectively. Tampered or incorrect data can lead to poor decisions, operational failures, and vulnerabilities in key sectors like manufacturing and logistics. With IoT and AI driving Industry 4.0, maintaining data accuracy ensures reliable operations, protects sensitive information, and prevents cyber threats that disrupt businesses and critical infrastructure.
Anchoring data close to its source is essential for securing integrity across the entire data processing chain, which often involves multiple distributed systems. For machines, this means securing the data before it leaves the device. At the same time, the system must protect the anchored data from tampering after export. Blockchain’s immutable nature aligns perfectly with this paradigm. That’s why we built a Wallet Service on top of the Secret Network. This service integrates seamlessly into any machine to secure its data integrity in a decentralized and privacy-preserving manner.
The Wallet Service acts as a gateway for communication with the Secret Network. It deploys onto any machine infrastructure that supports Docker. By using the Wallet Service, machines interact directly with the blockchain and its smart contracts. The service assigns each machine a unique identity through a public-private key pair. With its private key, the machine signs and broadcasts transactions to anchor its data on the Secret Network. The blockchain’s encryption ensures that no unauthorized third party can access the data. For details on how the network reaches consensus despite encryption, refer to our previous post.
To simplify integration, the Wallet Service offers a straightforward REST API with two endpoints. The ingress endpoint accepts a batch of data in a defined structure for anchoring. After receiving the data, the Wallet Service hashes it and stores the resulting hash in the service’s smart contract through a transaction on the Secret Network. This process creates an immutable fingerprint, allowing anyone to verify the integrity of a data batch through the Wallet Service’s verification endpoint. Since data verification typically occurs in systems other than the one that supplied the data, the Wallet Service supports deployment anywhere. In distributed data processing scenarios like Cosmic-X, entities that consume data instantiate a Wallet Service to verify data integrity before making decisions. For example, an AI service provider might deploy a Wallet Service in its cloud environment to verify data before using it for training or inference.
Two conditions must be met for this workflow to function: first, the verifying Wallet Service must have the appropriate viewing key from the machine that supplied the data. Otherwise, it cannot decrypt and query the fingerprints stored in the smart contract. Second, the format and schema of the data batch must remain standardized across the processing chain. To achieve this, we developed a Data Integrity Protocol as the foundation of the Wallet Service.
To anchor and verify data batches reliably, the Wallet Service requires a standardized protocol. Both the data anchoring and verification processes must adhere to a common data format, schema, and canonicalization standard. For Cosmic-X, we chose JSON as the data format and RFC 8785 as the canonicalization algorithm. Canonicalization ensures reliable cryptographic operations on JSON data by defining methods for handling whitespace, data types, and objects.
Considering use case requirements and the limitations of edge and cloud environments in Cosmic-X, we define a data batch as one hour’s worth of sensor data collected from a machine. The figure below shows an extract of a data batch collected from one of the use cases. The batch includes a metadata object used only for the Wallet Service’s business logic. This metadata contains key-value pairs such as the batchId and placeholders for the payload hash and the transaction hash on the Secret Network blockchain. The payload, which the system hashes during anchoring, consists of discrete sensor measurements. Each measurement uses a composite key created by concatenating the variable name with the Unix timestamp of its recording. The measurements include key-value pairs for variable name, timestamp, absolute value, and data type.
The batchId is the most critical part of a data batch. Since the Wallet Service uses it to anchor and later locate the data batch for verification, the batchId must be unique. In this setup, the batchId combines a machine ID with a Unix timestamp representing the time range of measurements in the batch, rounded to the nearest hour. For example, if machine 2080839 collects measurements from 11:01:23 to 11:59:43 on May 16, 2024, the batchId becomes 2080839_1715853600.
In the next post, we’ll showcase how we integrated the Wallet Service with three live machines and an AI service to enable secure and accurate anomaly detection in machine components.
Curious how our blockchain-based data-integrity solution can help your business? Check out our one-pager for a quick overview of its key benefits!
This article is the sixth and final article in a series about our probabilistic 360° supply chain tracking product, Track & Trust. Our previous articles described how the system works. Now, we dive into the results of our pilot operations. TLDR – We successfully tracked all the goods to their final delivery locations despite serious challenges!
We chose to track shipments of solar equipment for the Track & Trust Pilot. Destined for clinics and schools serving refugees in Beqaa Valley, Lebanon, these shipments were critical to the region. The area is home to over 300,000 Syrian refugees, according to UNHCR, and they all need medical care. Our partners, Aid Pioneers, Multi Aid Programs, and Al-Manhaj, collaborate to provide logistics, education, and medical care on the ground.
The clinics and schools require continuous a continuous electrical power supply. Due to Lebanon’s severe energy crisis, the public grid provides only about two hours of electricity per day, making the delivery of efficient healthcare services an immense challenge. In absence of a stable grid, most of the region’s essential services rely on generators, leaving the financial stability of operations at the whim of the ever-increasing price of diesel. Typical health clinics have thousands of dollars in monthly operating costs due to the need to purchase this fuel. To address this, Aid Pioneers is replacing diesel power systems with clean, abundant solar energy, one clinic at a time. By reliably shipping the equipment from Tripoli to Beqaa Valley, they achieve this goal with our help. Specifically the shipments we’ve tracked during the pilot contained all the equipment needed to outfit two clinics with enough solar power to cover all their needs. Aid Pioneers partner, Multi Aid Programs runs the clinics which received the solar and medical equipment we tracked.
Using Track & Trust, Aid Pioneers and their partners gained a clear view of what was happening to the parts in their shipment. As a result, they avoided extra trips, saving work and potential exposure to danger. Our team planned this deployment long before the recent conflict broke out, and our system performed well in the midst of a very difficult situation. Effective management of the challenges that arose was crucial to the success of the project.
During the shipments, ground personnel encountered outages of critical infrastructure, losing power and 4G connectivity several times. Fortunately, our Track & Trust mesh node infrastructure filled the gap, and our battery backup system enabled the system to run despite the power grid being down. The system’s design allowed it to handle such outages.
When 4G connectivity was lost, our mesh nodes cached delivery data until it could be passed between nodes. Utilizing technologies developed with our partner, Weaver Labs, we ensured the data was secure. Next, we used a satellite-enabled mesh node to post data that would have otherwise been lost via Iridium satellite uplink, developed by our partner Ororatech.
Aid Pioneers received hundreds of updates about the status of the goods from us. To ensure the integrity of the data, we cryptographically signed and anchored these updates to the ASI Alliance blockchain, making them highly trustworthy. This extra step was crucial to the project’s success. Together the result is highly trustable probabilistic 360° supply chain tracking.
Two major sets of shipments were completed under the watchful eye of Track & Trust, and a third set is currently being shipped to Lebanon. With 110 kWp of power, the solar systems make two entire clinics energy independent for the next twenty years. Additionally, we tracked a container of medical goods, which Al-Manhaj and Multi Aid Programs are using to save lives and provide medical treatment in Tripoli and the Beqaa Valley.
The design of Track & Trust allows it to work in various contexts, providing resilience and probabilistic 360° supply chain tracking. Adaptable to different scenarios, our system is highly versatile. As we continue to develop and refine our system, we will meet the changing needs of our partners.
Following this piloting success, we will examine plans to make the system more user-friendly. Logistics organizations that could use more resilience in their field operations are also being contacted. If this series of blog posts has piqued your interest, please reach out, and we will schedule a call or demo.
This article is the fifth in a series of posts about how our probabilistic 360° supply chain tracking product, Track & Trust, works. We described how the system works at a component level in our previous articles. Now, we dive into the challenging environment where our pilot operations have been executed. We selected Lebanon, one of the most difficult operational locations in the world, for our first pilot shipments to really prove the mettle of the system.
We have been working with our humanitarian partner Aid Pioneers for many months to prepare for these shipments. Aid Pioneers connects available resources from donors directly to recipient organizations. Through close collaboration with on the ground initiatives and the private sector, Aid Pioneers connects resources from donors directly with local organizations to foster sustainable, community-led change. They do this in places that need them most, making them a highly innovative humanitarian agency. They take an end-to-end approach to the supply chain, which we believe suits Track & Trust perfectly. Aid Pioneers needs to extend tracking of supplies beyond what typical supply chain tracking products can accomplish. We are helping them achieve this.
Aid Pioneers‘ logistics environment provides a perfect showcase for what Track & Trust can do. When Aid Pioneers ships a container full of medical supplies or solar power generation equipment to a Lebanese clinic or school, they hire a freight forwarder to pick up the goods. The freight forwarder then organizes the delivery to a local port via semi-truck. After that, a freight forwarder loads the container onto a ship. The ship travels to a port of entry in Lebanon, and we track its progress using a typical tracking link. However, once the container clears customs, we take over. We actively track it and pick up where traditional systems stop working.
At this point we encounter tricky conditions. Aid Pioneers local lebanese partner Al-Manhaj breaks down containers into multiple pallets or depalletizes them. They do this before final delivery. After that they deliver goods to one location while others go to other locations at different times. To keep track of what was delivered when, we use probabilistic 360° supply chain tracking. We also developed strategies to deal with power and connectivity outages.
These outages always happen at the wrong time so it’s important that the system is able to handle them. We do this with built in backup batteries and a battery management system. On top of that, the communications landscape is very challenging. Sometimes there’s 4G connectivity and at other times there’s outages. Our mesh nodes can operate no matter, though, by caching incoming data locally. The nodes just wait until the data can be posted or handed off to other mesh nodes. This approach multiplies the effectiveness of our communications assets. On top of that, we positioned one of our satellite uplinks at a local school. As a result, every event is (at the minimum) recorded and transmitted asynchronously – even when conditions are at their worst.
These logistics challenges are not unique to Aid Pioneers’ operations. However, they are particularly pronounced in the places where they work. We believe that if our system works there and brings value to freight forwarders and humanitarian organizations, it will work anywhere. As a result of this testing we’re confident in the capabilities of Track & Trust.
In our next post we’ll describe exactly how the our pilot operations went – and what the big value drivers are.