The agriculture and food-supply landscape is increasingly exposed to volatile weather, climate shifts, and global disruption, prompting a new wave of AI-powered resilience. Helios is introducing Cersi, a conversational AI analyst designed to monitor the agricultural supply chain in real time, surfacing risk insights for suppliers and customers alike. Built on a foundation of vast climate and signal data, Cersi aims to transform how consumer packaged goods (CPG) firms and food processors track dependencies, anticipate disruptions, and coordinate with suppliers across continents. The initiative follows a pre-seed funding round and is framed as part of a broader platform that ingests billions of signals to provide supplier-level foresight. This deep dive examines what Cersi is, how it works, the technology behind it, and the implications for supply-chain resilience and food security.
Overview and Context: Helios, Cersi, and the push for real-time supply-chain intelligence
Founders, vision, and market aim
Helios was cofounded by a veteran consultant with global strategic experience and a prominent AI/ML engineer from a leading tech giant. The team’s shared objective centers on preventing food-supply shortages through proactive intelligence rather than reactive reactions. Cersi is positioned as “the world’s first supply chain AI analyst,” a conversational assistant designed specifically for the agricultural supply chain, distinct from general-purpose models by tailoring insights to supplier networks, commodity flows, and the unique risk vectors that threaten food availability. The founders argue that traditional approaches—relying on direct supplier updates, generic weather or news monitoring, or generic spreadsheet-based tracking—are insufficient for modern, globalized supply chains. The vision is to deliver precise, real-time assessments that help enterprises anticipate disruptions and coordinate mitigation with suppliers.
The target users: CPg firms, food processors, and broader supply-chain teams
The primary audience for Cersi comprises consumer packaged goods companies and food processors that operate with extensive supplier networks. These organizations historically faced a fragmented toolkit: ad hoc signals about weather, climate events, and political or economic shifts; sporadic supplier communications; and non-specialized tools such as spreadsheets and presentation software to capture and share risk information. Helios’ approach consolidates these inputs into a unified platform and then augments them with a conversational interface that allows users to query the system in natural language and receive structured, actionable responses.
The product promise: speed, depth, and actionable insight
At the core of the offering is the claim that it dramatically reduces the time required to produce supplier risk assessments. Where traditional workflows could take days or weeks through bespoke reports and external consultants, Cersi aims to deliver insights in seconds. The value proposition emphasizes not just speed but the granularity of assessment: a customer with a large supplier base can obtain a real-time snapshot of the highest-risk suppliers and understand the drivers behind each risk score, down to a per-supplier, per-location view. The platform also provides a global visualization of the supplier network, enabling users to click into individual suppliers to learn more about events impacting them. Cersi’s conversational capability is designed to replace manual data extraction and report compilation with a natural-language, on-demand interaction.
The broader platform strategy: signals, events, and a real-time lens
Helios positions Cersi within a broader platform that ingests vast quantities of climate, economic, and political signals. The system tracks force majeure events and catastrophic incidents—earthquakes, tsunamis, floods, and related disruptions—in near real time. This enables a global view of supplier risk and supports a decision-making workflow that accounts for both macro-level trends and supplier-specific conditions. The platform is described as continually updating its intelligence by scanning a large corpus of news sources and other data feeds to keep the risk scores current and relevant for procurement, logistics, and operations teams.
How Cersi Works: From supplier data to conversational risk insight
Supplier data ingestion and risk scoring
Users input supplier details—name, the commodity supplied, and the supplier’s origin location—into a supplier dashboard. The system then generates a risk score that indicates whether a given commodity is at low, medium, or high risk of disruption due to natural or human-driven causes. Each score is accompanied by a justification that explains the contributing factors and events that informed the assessment. The dashboard presents a global map with supplier locations, where users can drill down to learn more about specific suppliers and the events affecting them. This structured approach converts scattered signals into a coherent risk narrative, enabling procurement teams to prioritize intervention and mitigation strategies.
Real-time signals, weather, and climate monitoring
Cersi’s strength lies in its real-time awareness of weather patterns, climate-related changes, and other environmental factors that can influence supply chains. The system tracks climate signals, seasonal variations, and the outcomes of climate change, integrating them with other risk indicators to present a more complete picture of supplier vulnerability. The platform’s ability to surface relevant updates for a given location—such as forecast changes, weather alerts, or climate-driven supply shocks—helps enterprises anticipate disruptions before they occur and adjust sourcing or production plans accordingly.
Conversational interface: natural language access to complex data
A defining feature of Cersi is its conversational assistant. Rather than requiring teams to navigate a dense analytics dashboard or export data into a separate reporting tool, users can pose natural-language queries and receive natural-language responses that synthesize the data. The assistant can retrieve supplier-specific information, summarize risk factors, and present findings in easily digestible formats. This design reduces the friction associated with generating insights and accelerates the decision-making process, particularly for teams that must respond quickly to evolving conditions in multiple regions.
Visualization and storytelling: maps, scores, and supplier narratives
In addition to the conversational outputs, the platform emphasizes visualization as a means to communicate risk. A global supplier map displays locations as interactive points, enabling users to click through to supplier-level detail. The system’s scoring mechanism helps teams quickly identify where disruption risk is concentrated, while the accompanying narrative explains the drivers behind each score. These features collectively support proactive planning, such as diversifying suppliers, preemptively adjusting inventory, or revising logistical routes to reduce exposure to high-risk areas.
Distinction from generic AI wrappers and the GPT family
Helios emphasizes that Cersi does not rely on generic AI wrappers built on top of a broad language model such as GPT. Instead, it leverages a combination of proprietary machine learning models and open-source components, designed specifically for supply-chain analytics. The founders highlight that building this specialized capability has required substantial development effort and data engineering, underscoring the platform’s bespoke nature. The architectural choice is framed as critical to delivering domain-aware insights with the reliability and latency requirements demanded by enterprise customers.
Data architecture, signals, and the technology stack
Billions of climate, economic, and political signals
Helios describes its data ingestion approach as ingesting billions of signals spanning climate, economics, and politics. This expansive data landscape is designed to capture subtle and overt drivers of supply-chain risk, including macroeconomic shifts, policy changes, commodity price volatility, and climate-induced anomalies. The system aggregates these signals to form a cohesive risk assessment at the supplier level, allowing teams to understand not just whether a disruption is likely, but why it might occur and which suppliers are most at risk.
Real-time monitoring of force majeure and catastrophes
The platform integrates real-time monitoring for major events that can disrupt supply chains. By tracking events such as earthquakes, tsunamis, floods, and other force majeure events, Helios aims to provide customers with timely alerts and context for how these events could affect their supplier network. The ability to correlate a supplier’s location with ongoing or developing events enables more targeted risk management and contingency planning.
Global news coverage and continuous crawling
Helios reports crawling tens of thousands of news sources—thousands of daily updates—to inform its platform and Cersi’s responses. This broad news-detection capability is intended to keep risk assessments current and aligned with the latest developments affecting global supply chains. The continuous ingestion of information helps ensure that the platform’s outputs reflect near-term realities as well as longer-term trends.
Supplier visualization: a global network at a glance
The supplier dashboard presents a world map where each supplier is represented as a data point. Users can click into a particular supplier and view the events and conditions that are shaping its risk profile. This visualization supports quick prioritization and deeper investigations, enabling teams to connect macro-level signals with supplier-specific contexts.
The choice of technology: proprietary ML plus open-source components
The technical framework underpinning Cersi is not built on top of a single large language model. Instead, Helios combines proprietary machine learning models with open-source technology to deliver its specialized capabilities. This approach reflects a deliberate choice to tailor models to the domain’s needs, such as forecasting specific weather-driven risks, modeling agricultural crop sensitivity, and translating complex datasets into actionable risk narratives for procurement teams.
Product deployment, user experience, and early adoption
The supplier dashboard: from data entry to risk snapshots
Users begin by entering supplier name, the commodity supplied, and the origin location into Helios’s supplier dashboard. The system then generates a risk score and provides an explanation of the factors behind the assigned level of disruption risk. A world map visualizes the global chain, and users can click into individual suppliers to review the events affecting them. This end-to-end workflow transforms raw data into an accessible risk profile, enabling procurement teams to move from observation to proactive action.
Cersi as a conversational assistant: turning data into dialogue
Cersi enters the workflow as a conversational layer that retrieves and formats the information from Helios’s backend. The assistant eliminates the need for manual data extraction and report compilation, instead answering requests in natural language and delivering structured insights that align with business decision-making processes. This capability is designed to streamline reporting cycles and empower teams to generate up-to-date analyses on demand.
Supplier-level insights and rapid decision support
The combination of a granular supplier view and Cersi’s conversational access enables a rapid decision cycle. Instead of waiting for external consultants to deliver bespoke analyses—often at substantial cost and long lead times—enterprise teams can obtain timely intelligence on demand. The system’s emphasis on speed and specificity is positioned as a major advantage in environments where inventory, production, and logistics must be aligned with fluctuating risk conditions.
Early adoption and beta access
Helios began offering access to its supplier dashboard to a limited set of beta customers earlier in the product’s development. The emphasis has been on refining the user experience, ensuring reliable data integration, and validating the reliability of risk scores and explanations. While the specifics of the beta cohort and use cases are not detailed in every release, the approach signals an ongoing effort to iterate with real-world customers who manage diverse supplier networks and complex supply chains.
User workflows and decision-making enhancements
In practice, users can expect a workflow that blends visualization, data interpretation, and conversational queries. The dashboard provides the structural foundation for supplier risk management, while Cersi supplies natural-language access to findings, enabling teams to generate executive summaries, vendor risk reports, and action-oriented recommendations without departing from the platform. This integrated workflow supports more coherent cross-functional collaboration among procurement, logistics, operations, and risk management teams.
Technology strategy, partnerships, and market outlook
Notable funding and strategic investors
Helios announced a pre-seed funding round totaling $1.85 million, with participation from specialized investment firms and venture partners focused on early-stage technology-enabled supply chains. The funding supports product development, data infrastructure, and go-to-market efforts aimed at CPg organizations and food processors. The involvement of investors with an interest in supply-chain resilience underscores the market appetite for technology solutions that address real-world disruption risks and the need for real-time analytics.
Partnerships and industry influence
Partnerships with leaders across consumer brands and retail ecosystems have informed product design and data insights. While the article does not detail every collaboration, the implication is that established brands in beverages, consumer packaged goods, and retail may influence the platform’s evolution, data standards, and interoperability requirements. These relationships help shape a product that aligns with enterprise procurement processes and the operational realities of large-scale supply networks.
Competitive positioning and differentiation
Helios differentiates Cersi from more generic AI approaches by focusing on domain-specific performance. Rather than acting as a wrapper around a broad-language model, Cersi relies on a bespoke mix of proprietary ML models and vetted open-source components calibrated for supply-chain analytics. This differentiation addresses concerns about model accuracy, reliability, latency, and the need to translate complex data into actionable business insights that procurement teams can trust and act upon quickly.
Growth potential and user value
The platform’s emphasis on real-time risk assessment, supplier-level granularity, and conversational accessibility positions it as a tool to improve resilience across the agricultural supply chain. Enterprises can leverage Cersi to reduce the likelihood of stockouts, optimize supplier diversification, and design contingency plans based on live information about climate events and other disruption drivers. The combination of speed, specificity, and accessibility supports a compelling business case for large CPg and food-processing customers seeking to modernize their risk management playbooks.
Implications for supply-chain resilience, food security, and broader industry impact
Reducing preventable shortages through proactive intelligence
A central theme in Helios’s messaging is the potential to avert food shortages by catching warning signs earlier and translating them into concrete actions. By delivering quick, supplier-specific risk assessments, the platform supports proactive responses such as preemptive sourcing adjustments, inventory buffer adjustments, or alternative routing decisions that can mitigate disruption. This approach aligns with the goal of strengthening the resilience of food systems against a range of shocks, from weather extremes to geopolitical events.
Enhancing collaboration with suppliers and risk-aware procurement
Real-time visibility at the supplier level can foster more proactive collaboration between buyers and suppliers. With clear, explainable risk signals and a shared view of disruption drivers, procurement teams can communicate expectations, coordinate mitigation strategies, and structure contractual protections or contingency plans that reflect current risks. The conversational dimension can facilitate ongoing dialogue, enabling teams to request clarifications, view supplier-specific risk narratives, and align on risk-mitigation actions with a higher degree of confidence.
Practical implications for forecasting, planning, and operations
The system’s real-time risk scoring and location-based insights have immediate implications for forecasting and operations planning. For instance, procurement can adjust demand forecasts based on emerging risk signals, while logistics teams can re-route shipments or seek alternate suppliers to minimize exposure. The ability to assemble reports quickly and explain the rationale behind recommendations supports more agile and informed decision-making, reducing the lag between signal and action.
Ethical, regulatory, and governance considerations
As with any data-intensive platform that aggregates signals from diverse sources, governance and ethics considerations come to the fore. Enterprises must navigate issues such as data privacy, supplier confidentiality, and the responsible use of AI in procurement decisions. While the platform emphasizes real-time insights, organizations should ensure that risk assessments comply with internal governance frameworks and relevant regulatory requirements, particularly in cross-border supplier relationships where data handling and risk reporting may be subject to scrutiny.
Long-term resilience and the role of technology in food security
The broader implication of Cersi and similar platforms is a shift toward resilience as a core operational capability for global food systems. By enabling proactive risk monitoring, early warning, and rapid decision support, technology-driven approaches can contribute to more reliable food supply and price stability. The emphasis on real-time insights, domain-specific modeling, and supplier-level visibility represents a meaningful evolution in how companies manage complex, multi-region supply networks in the face of climate variability and systemic shocks.
The road ahead: roadmap, challenges, and strategic priorities
Product and data roadmap
Looking forward, Helios is likely to continue expanding Cersi’s capabilities by refining risk models, broadening data sources, and enhancing the user experience. Key development priorities may include deeper integration with supplier systems, expanded coverage of commodities, and more granular explanations that help users translate risk signals into concrete procurement actions. Ongoing investments in data quality, latency reduction, and interpretability will be central to building trust and broadening adoption across large enterprises.
Adoption strategy and enterprise focus
As the platform matures, a principal objective is to scale adoption among CPg companies and food processors with extensive supplier networks. The emphasis will be on delivering measurable ROI through reductions in stockouts, improved supplier reliability, and more efficient risk management processes. A scalable onboarding process, robust security controls, and interoperability with existing procurement and ERP systems will be critical enablers for enterprise customers.
Collaboration opportunities and ecosystem development
Helios may pursue additional partnerships to enrich its data ecosystem, including collaborations with academic researchers, industry groups, and other technology providers that contribute climate, geopolitical, and economic signals. Such collaborations could help refine modeling approaches, broaden data coverage, and support the development of standardized risk reporting that aligns with sector norms and regulatory expectations.
Potential challenges and risk considerations
The push toward real-time, supplier-level analytics faces challenges such as data quality, signal noise, and the risk of over-reliance on automated assessments. Enterprises will need to maintain human-in-the-loop governance to interpret AI outputs and decide when to escalate or override recommendations. Ensuring data privacy, respecting supplier confidentiality, and maintaining transparent model explanations will be essential to sustaining trust and achieving durable value.
Conclusion
Helios’s Cersi represents a bold step in bringing AI-powered, supplier-centric risk intelligence to the agricultural supply chain. By combining a robust data architecture—encompassing billions of climate, economic, and political signals—with a purpose-built conversational assistant, Cersi seeks to deliver fast, actionable insights that help CPg firms and food processors anticipate disruptions and coordinate with suppliers more effectively. The platform’s real-time risk scoring, global supplier mapping, and natural-language access to complex datasets aim to streamline workflows, reduce response times, and enhance decision quality in a landscape characterized by climate volatility and global interdependence. The recent pre-seed funding and the involvement of industry-focused investors underscore the market appetite for solutions that meaningfully improve supply-chain resilience and food security. As Helios continues to refine its models, expand data coverage, and scale adoption across enterprise customers, Cersi could become a cornerstone tool for proactive risk management in modern agricultural commerce, enabling more resilient operations and more reliable food systems worldwide.
