Why Agentic AI Is Non-Deterministic (and Why That Matters)

Traditional code is deterministic: the same input always produces the exact same output. Agentic AI, powered by large language models (LLMs), is not. LLMs generate responses probabilistically – they sample from a distribution of possible tokens based on patterns learned during training. Even with identical prompts, outputs can vary due to:

• Temperature and sampling parameters
• Multi-step reasoning loops (planning → tool selection → execution → re-evaluation)
• Hallucinations or subtle prompt sensitivities
• External tool results that feed back into the next reasoning step

When an agent is given the ability to act (send an email, post in Slack, query a database, or trigger a business process), this non-determinism creates real risk. A seemingly harmless prompt can lead to prompt injection, tool misuse, unintended data exposure, or actions outside the intended scope. The system is no longer a predictable calculator – it becomes an autonomous decision-maker operating in a probabilistic space.

That is exactly why Agentic AI must be controlled tightly. Without deliberate, layered safeguards, the blast radius of a single unexpected output can be 

Two Secure Paths to Agentic AI

We guide clients based on their needs and technical maturity:

1. Vendor-Managed Walled-Garden Platforms (ideal for non-technical teams)
For business users who need AI chat, workflows, or integrations (Slack, Gmail, internal docs) without building infrastructure, we recommend fully sanctioned, vendor-managed platforms that meet our strict compliance bar:

Key requirements we enforce:

• ISO 27001, SOC 2 Type II
• Full GDPR alignment
• EU hosting
• No training on customer data
• Transparent sub-processors and DPA
• Built-in (or easily configurable) human-in-the-loop (HITL), confirmation steps, and basic guardrails

Currently under final evaluation:

• Langdock (Berlin/Azure EU):  strong for workflows and agents with Guardrails node, per-action confirmation (HITL), and admin controls.
• Lurus (Hannover):  affordable multi-agent automation for chat-initiated tasks.

The core rule is simple: stay inside the platform. The vendor owns the security model –  but only within their boundary. We help clients configure these platforms (admin hardening, workflow design, HITL policies) so they remain safe and auditable.

2. Custom Bounded Agents (for teams needing maximum control)
When pre-built integrations aren’t enough, we develop fully controlled agents using a robust defense-in-depth stack:

• Orchestration with LangGraph and Inngest
• Guardrails AI for robust validation
• Lasso for traffic inspection
• ToolHive for per-tool isolation

These agents feature narrow tool scopes, comprehensive auditing, and human oversight gates — delivering precision and security.

Datarella’s Agentic AI Services

• Strategy & Architecture: Risk assessment and governance frameworks
• Platform Selection & Hardening: Compliant vendor evaluation and secure configuration
• Custom Agent Development: Production-grade bounded agents
• Tool & MCP Security:  Secure integration of external capabilities
• Enablement & Assurance: Team training, guidelines, and ongoing audits

Why Partner with Datarella?

Agentic AI unlocks major productivity gains, but its non-deterministic nature demands rigorous control. We combine deep expertise with practical, EU-compliant solutions so you can deploy agents confidently –  whether through trusted vendor platforms or tightly bound custom systems.

Ready to implement secure Agentic AI? Contact Datarella today to discuss your use case.

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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.

Why AI “Dies” in Traditional IT, and Why That’s the Wrong Approach

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:

• Wrap it in rigid approval processes
• Demand ROI projections before pilots
• Force it into existing tech stacks without workflow redesign
• Prioritize “safe” use cases over creative experimentation

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. 

History Shows Incumbents Can Win If They Adapt Smartly

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:

• Combine their domain expertise and proprietary data with AI’s capabilities
• Redesign workflows around human-AI collaboration (what some call “co-intelligence”)
• Scale from pilots to enterprise-wide agentic systems under proper guardrails

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. 

The Most Likely Future: Pragmatic, Data-Driven, and Agentic

By late 2026 and into 2027, we expect the following trajectory:

1. From pilots to production at scale — Organizations doubling the share of AI projects in production, driven by agentic AI (autonomous agents that execute multi-step workflows).
2. J-curve productivity — Initial flat or negative returns as companies rewire processes, followed by steep gains once complementary innovations (new roles, data pipelines, decision protocols) are in place.
3. Governance catching up — Mature frameworks for agentic AI, data quality, and responsible use becoming table stakes. Shadow AI will decline as secure, enterprise-grade platforms mature.
4. Incumbents leveraging data moats — Companies with clean, governed data and domain expertise will outperform pure AI-native players in regulated or complex industries.

This future is neither utopian revolution nor failure – it’s an evolutionary transformation, provided organizations avoid the “IT department trap.”

Optimal Use of AI in Businesses: Five Practical Principles

Drawing from Mollick, historical lessons, and 2026 enterprise reports (Deloitte, McKinsey, PwC), here’s how forward-thinking companies are winning:

1. Embrace the weirdness—experiment deliberately
   Give teams space to discover unexpected uses. Mollick advocates leadership that encourages crowdsourced experimentation and “labs” to scale promising ideas. Treat AI like a creative collaborator, not just an automation tool.
2. Build on rock-solid data foundations
   Data quality and governance remain the #1 barrier cited across reports. Without trustworthy data pipelines, AI outputs are unreliable. This is where specialized partners excel – unifying siloed data, implementing real-time pipelines, and ensuring privacy/compliance.
3. Redesign workflows and roles around human-AI co-intelligence
   Don’t automate jobs – augment them. Successful organizations are re-architecting processes so humans focus on judgment, creativity, and relationships while AI handles analysis, drafting, and routine execution.
4. Deploy secure, governed agentic AI
   Autonomous agents are the next frontier, but they require bounded orchestration, threat modeling, and compliance-by-design. Enterprises need platforms that support multi-agent systems without introducing new risks.
5. Measure what matters – and iterate
   Move beyond vanity metrics. Track business outcomes (revenue impact, cost savings, customer satisfaction) and accept that ROI may follow a J-curve.

How Datarella Helps Businesses Navigate This Future

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:

• Secure, production-ready autonomous agents
• Data platforms that make AI reliable and compliant
• Integration of AI into legacy systems without the usual friction
• Or decentralized approaches that enhance trust and data integrity

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.

The post The Most Likely Future of AI: Embracing Its “Weirdness” Without Chaos appeared first on DATARELLA.

What Agentic AI Really Means Technically

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:

• Orchestration frameworks such as LangGraph for building bounded, controllable multi-agent workflows.
• Tool-calling and function execution to interface with databases, property management software, accounting platforms, IoT gateways, and third-party APIs.
• Memory and state management to track long-running processes (e.g., a maintenance ticket from detection to closure).
• Planning and reasoning loops (often powered by large language models combined with rule-based safeguards) to break down high-level goals into executable sub-tasks.

The result is systems that don’t just suggest actions –  they carry them out reliably within defined guardrails.

Core Technical Capabilities in RAAY RE

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:

• Decentralized Data Synchronization
  Property data rarely lives in a single monolithic database. Agents autonomously query and reconcile information from multiple sources — PMS platforms, ERP/accounting systems, IoT sensor networks, external credit bureaus, and document repositories. They perform real-time integrity checks, detect anomalies or conflicts, and orchestrate secure updates while preserving audit trails. This reduces manual reconciliation errors and supports GDPR-compliant data flows through controlled access and logging.
• Multi-Step Workflow Automation
  Agents handle complex, conditional processes by chaining actions: perceive → reason → act → verify → iterate. Examples include:
• Maintenance Operations: Ingest IoT sensor readings and image data → apply predictive models for anomaly detection → evaluate repair options against budget rules and vendor SLAs → dispatch approved contractors via API → monitor progress through status updates → confirm completion and trigger invoice validation.
• Financial Processes: Automate rent collection reminders with probabilistic dunning (adjusting based on payment likelihood models), perform transaction matching across bank feeds and ledgers, flag discrepancies, and generate compliance-ready reports.
• Document Intelligence: Use extraction models to pull structured data from unstructured leases, inspection reports, and invoices → abstract key clauses and obligations → populate downstream systems → enforce regulatory checks.
• Tenant-Facing and Leasing Agents
  24/7 communication agents integrate with chat, email, and voice channels to handle routine inquiries, schedule viewings, and route escalations. Leasing agents combine multi-source data (credit history, behavioral signals, references) to evaluate applicants and score risk, accelerating approvals while maintaining explainability for compliance.

Security and Reliability Engineering

Datarella places strong emphasis on making agents enterprise-ready:

• Bounded agent orchestration (using patterns like LangGraph and Inngest) to prevent uncontrolled autonomy and keep agents within predefined scopes.
• Autonomous agent hardening and isolation — including threat modeling, MCP server security, API security layers, and multi-agent interaction safeguards.
• Compliance-by-design features for data protection regulations, with transparent logging and human-in-the-loop options for sensitive decisions.
• Web3 foundations where appropriate: leveraging blockchain for immutable audit trails, decentralized identity elements, or secure data marketplaces in multi-stakeholder scenarios (building on our experience with Fetch.ai and similar decentralized agent systems).

These measures ensure agents remain controllable, auditable, and resilient even when operating across distributed environments.

Technical Benefits and Scalability Considerations

From an engineering standpoint, agentic systems deliver:

• Reduced operational latency through parallel tool use and automated decision loops.
• Lower error rates via consistent execution paths and automated verification steps.
• Improved scalability: portfolios can grow without linear increases in administrative staff, as agents handle volume spikes in inquiries, maintenance tickets, or reporting cycles.
• Better data quality through continuous synchronization and anomaly detection across heterogeneous sources.

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.

Looking Ahead: Toward Fully AI-Native Real Estate 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.

The post Agentic AI in Property Management: A Technical Introduction appeared first on DATARELLA.

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!

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Building the Foundation for Trusted Machine Communication

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.

Demonstrating Real-World Impact: MOBIX Park & Charge

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.

Scaling Toward Smart Cities

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.

Where Web3 Meets AI

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.

A Foundation for the Future

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.

The post moveID: Wrapping Up Three Years of Mobility Innovation appeared first on DATARELLA.

Visualizing Sensor Data

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.

Verification & Anomaly Detection

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.

Data Integrity Log

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!

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Need for Data Integrity

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.

Wallet Service

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.

Integration

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.

Requirements

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.

Data Integrity Protocol

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.

Batch Structure

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!

The post Securing Data Integrity in Industry 4.0 appeared first on DATARELLA.

The Track & Trust Mission in Southern Lebanon

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.

Tracking Impact

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.

Energy Independence One Clinic at a Time

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.

Track & Trust Proof of Resilience

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.

Next Steps

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.

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Aid Pioneers – an Ideal Pilot Partner

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.

Supply Chain Tracking Challenges

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.

Outwitting 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.

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