Suply builds AI-native intelligence that reads shipments end-to-end, uniting cargo behaviour with real-world context to explain events with clarity.
Atlas unifies the critical domains that govern global movement: the physical state of the load, the thermodynamic and environmental forces acting on it, the operational reality of vessels, aircraft, ports, and handovers, and the external pressures of weather, markets, and geopolitics.
All resolved into one living model of truth that explains what happened, why it happened, and what comes next.
Low-cost hardware. High-value intelligence.
Historically, low-cost sensors provided data without context. You knew the temperature, but not the impact.
Echo changes the paradigm. By combining cost-efficient edge hardware with Suply's intelligence layer, we allow companies to answer complex questions previously impossible to solve in the perishable supply chain.
We build the intelligence layer that powers decision-making across the supply ecosystem — and pair it with our own edge hardware to create an undisputable source of truth. Together, they enable partners in any industry to answer complex questions, reduce waste, and price risk with confidence.
Suply gives finance partners a verified, tamper-evident view of goods in transit, reducing fraud and enabling dynamic risk pricing based on the actual state of the asset — not assumptions or paperwork.
Predict degradation and failure modes early. Move from reactive damage control to proactive management using biological signals.
Data-backed narratives replace subjective debate, lowering discounts and strengthening the trust between exporters and importers.
Explaining the "why," not just the "where." The industry gains access to a complete view—internal conditions aligned with external forces.
We work with companies that want to build on our intelligence layer to better serve the trade ecosystem.
Claims resolved on objective context, not hearsay. Suply provides a factual account of what happened, when it happened, and how conditions changed in transit — giving insurers the clarity they need for fair, fast, and defensible outcomes.
The global cold chain is accelerating toward $1.3T, yet its critical data remains fragmented. Sea and air cargo move at unprecedented scale, but the industry still lacks a single, contextual source of truth capable of answering the most fundamental questions about what actually happened in transit — and why.
Supply-chain data has long been fractured across carriers, ports, airlines, and forwarders. Industry efforts like the DCSA are pushing toward standardization, but the underlying signals remain inconsistent, delayed, and incomplete.
We built AI-native systems that ingest the best available industry data and fuse it with our own intelligence — trained on over one billion global shipment transactions. This gives us a foundation strong enough to interpret risk with clarity, not guesswork.
We built models that understand the realities of global logistics: transshipments, vessel rolls, dwell times, routing anomalies, and operational noise that traditional systems routinely misread. We resolve conflicting signals and infer what actually happened — and why.
Our intelligence works best with truth from the edge. Our labs develop ultra-low-cost hardware — around $2 BOM — to capture the essential signal directly from inside the cargo. In a world drifting toward expensive, over-complex devices, we chose precision, scale, and purpose.
Once Suply’s inexpensive devices are deployed at scale and data density builds across shipments, the value quickly extends far beyond monitoring. With a continuous stream of movement, condition, and timing data around every shipment, Suply can rapidly improve a wide range of operational challenges that are directly or indirectly affected by how cargo actually behaves in transit.
This density of real-world data, combined with intelligence layered on top, allows teams to separate noise from true risk, reduce unnecessary intervention, and make better decisions across routes, services, and operational cycles. Our forward-deployed logistics engineers work alongside your teams to apply these workflows to your real problems, helping you resolve issues faster than ever before and turn insight into action. Suply operates within your existing technology stack, integrating cleanly without forcing system change, while continuously compounding value as more shipments move through the platform.
Many organisations lose time and money not because they lack data, but because no one trusts it enough to act on it. Suply provides a single, objective record of what actually happened to a shipment, reducing internal disagreement, shortening decision cycles, and giving teams confidence to act without escalation.
Supply chains repeat the same failures because organisations don’t retain operational memory. Suply becomes a living record of how routes, ports, and services actually behave over time, allowing teams to learn from history instead of relearning the same lessons during every disruption or lane shift.
Operational teams are overwhelmed not by problems, but by noise. Suply filters thousands of events into a small number of meaningful signals, ensuring human attention is reserved for issues that are unusual, impactful, and actionable, rather than consumed by routine variation.
Disputes with carriers, terminals, and partners often escalate due to incomplete or subjective narratives. Suply provides neutral, time-stamped evidence of movement and condition, allowing conversations to focus on facts instead of blame, and preserving commercial relationships.
Most organisations make routing, carrier, or process changes without a clear way to measure whether those changes improved outcomes. Suply tracks behaviour before and after decisions, allowing teams to validate what worked, what didn’t, and where further adjustment is needed.
Risk rarely appears evenly distributed. Suply reveals where delays, dwell, and instability are clustering across routes, services, or specific global corridors, helping teams understand not just individual issues, but where systemic exposure is building quietly.
Audits are expensive not because of findings, but because of reconstruction. Suply maintains a continuous, verifiable record of shipment handling and movement, allowing organisations to respond to audits or investigations without manual data assembly or retrospective explanation.
Late visibility removes options. By identifying emerging deviation patterns early, Suply gives teams time to adjust routing, reallocate volume, or reset expectations while alternatives still exist, preserving flexibility instead of forcing reactive decisions.
Different teams often operate with different versions of the truth. Suply provides a shared, factual view of shipment behaviour that aligns operations, procurement, quality, finance, and leadership around the same reality, reducing friction and misalignment.
Most reputational damage starts with small, poorly handled issues. Suply helps teams identify when an issue is genuinely serious versus operational noise, enabling proportional response and preventing overreaction that can escalate minor problems unnecessarily.
We pull data from all layers of the supply chain: device telemetry from our edge nodes inside the container, carrier milestones, AIS for sea, ADS-B for air, vessel and flight schedules and external risk feeds (strikes, congestion, weather, conflict, canal and airspace closures). We auto-extract cargo context from AWBs, bills of lading, POs and packing lists — commodity type, packaging, expected set temps, ventilation settings and handling rules. Everything is normalised into a single shipment schema that grounds all reasoning.
We convert the raw stream into grounded logistics and thermodynamic features: expected vs actual temperature bands, heat-flow behaviour inside reefers, defrost cycles, temperature gradients, set-point compliance, vessel and aircraft deviation signatures, port and airport dwell risk, trans-shipment likelihood and lane-specific anomaly profiles. Each shipment gets a compact context object built from engineered features, extracted cargo rules and the closest historical patterns.
Atlas reasons using that grounded context object plus relevant historical shipments. The model never guesses — every conclusion must reference structured data, device behaviour, extracted rules and known physical patterns. When a shipment completes, the full trace feeds back into the dataset, improving the feature engine and strengthening the logic across commodities, vessels, aircraft, reefer types and routes.
TRUTH AT THE EDGE. INTELLIGENCE IN MOTION.
The global supply chain is a chaotic system of fragmented signals. We refuse to accept "estimates" as truth.
We believe in a future governed by absolute clarity and grounded physical reality—where data doesn't just track cargo, it explains the physics of the journey.