Tellia: The Farm Doesn’t Need Another Dashboard. It Needs a Voice.
How a Franco-American startup is turning everyday conversations into agricultural intelligence, and why the future of farming software might begin with something as ordinary as a phone call.
Agriculture has spent decades becoming more technologically sophisticated. Tractors navigate fields using GPS, satellites monitor vegetation, sensors measure soil conditions, and artificial intelligence promises to predict everything from crop diseases to optimal harvest windows. Yet behind this increasingly digital industry lies a surprisingly ordinary problem: much of the information needed to run a farm still exists in handwritten notebooks, scattered WhatsApp conversations, telephone calls, or simply in the minds of the people working in the fields. A technician notices the first signs of disease on a vineyard leaf. A farm manager discovers an irrigation problem. An agronomist identifies a potential pest outbreak. These observations can influence thousands of dollars’ worth of decisions, but recording them often requires returning to an office, opening specialised software, and manually entering information that was already communicated hours earlier. Agriculture has become remarkably good at generating data, but considerably less effective at capturing the knowledge that its workers produce every day.
This is the problem that Tellia, a young company operating between San Francisco and Paris, wants to solve. Founded in 2024 by Coline Labadie de Faÿ and Vincent Trastour, the startup has developed an AI-powered assistant that allows farmers, agronomists and agricultural workers to record information simply by speaking. Rather than introducing another complicated farm management platform, Tellia integrates into familiar communication habits: a telephone call, a WhatsApp voice note, a photograph, or a text message. Its technology interprets these inputs, extracts relevant information, and converts them into organised digital records, reports and operational tasks. What might appear to be a relatively modest application of artificial intelligence reveals a much larger strategic ambition. Tellia is positioning itself not as another agricultural software provider, but as the interface connecting human expertise to the digital infrastructure of modern farming.
The Agricultural Software Paradox
To understand Tellia’s opportunity, it is worth examining an apparent contradiction in the digital transformation of agriculture. The industry has witnessed an extraordinary proliferation of technologies intended to improve productivity, sustainability and resource management. Precision agriculture systems can determine where fertilisers should be applied, automated machinery can reduce labour requirements, and increasingly sophisticated analytical platforms can transform historical information into recommendations. However, the usefulness of these systems depends on a basic assumption: the necessary data must first exist in a structured, accessible form. A sophisticated dashboard cannot analyse a pest observation that was never entered into the system, just as an intelligent irrigation model cannot account for a broken water line that somebody mentioned in a telephone conversation but never documented. The problem is therefore not necessarily a shortage of analytical capabilities. In many cases, it is the gap between what agricultural workers know and what agricultural software can see.
Official European statistics help illustrate the scale of this gap. According to Eurostat figures published in July 2026, only around 11% of EU farms used farm management information systems in 2023, while approximately 18% of farms with agricultural land applied some form of precision farming technology. Those precision-technology users nevertheless managed approximately 44% of the EU’s agricultural area, indicating how strongly adoption is concentrated among larger operations. A separate 2025 European Commission study, based on 1,444 surveyed farmers across nine EU countries, found that digital tools were widely used but that agricultural data collection remained heavily dependent on manual processes or basic software. Costs, limited digital skills, connectivity and concerns over data control continued to obstruct more advanced adoption. The problem is not simply persuading farmers that technology is valuable. It is making technology compatible with the realities of agricultural work.
Traditional enterprise software is generally designed around structured interactions. It expects users to select fields, complete forms, navigate menus and enter information according to predefined categories. That approach makes sense for office-based employees who work at computers throughout the day. It is considerably less intuitive for someone inspecting vines under the sun, driving between orchards, or supervising a multilingual harvesting crew. Agricultural observations are also rarely communicated in perfectly structured sentences. A field worker might mention that a particular section was sprayed, that another requires irrigation, and that a machine needs repairing, all within the same conversation. Converting that account into separate, accurately categorised records introduces administrative work that may offer little immediate benefit to the person performing it. Over time, this creates a familiar cycle: software is purchased, employees find it inconvenient, records become incomplete, and the system fails to deliver the insights that justified its adoption.
Tellia approaches this problem from the opposite direction. Instead of expecting workers to behave like software users, it attempts to make software understand how workers naturally communicate. Its strongest competitor, as Labadie de Faÿ explained in a September 2026 interview with AgNavigator, is often not another technology company at all. It is the notebook and the group chat. This is an important distinction because it changes the nature of the commercial opportunity. Tellia is not exclusively competing for budgets already allocated to agricultural technology. It is also trying to digitise activities that have historically taken place outside specialised software altogether. Its potential market includes not only farms looking to replace existing systems, but operations that have never found conventional software worth the trouble.
From Paris to California: Building Around the Problem
Tellia’s founding story reflects the increasingly international character of European technology entrepreneurship. Labadie de Faÿ and Trastour met through Entrepreneur First in Paris, an organisation that brings together prospective founders and helps them develop companies around promising technologies and commercial problems. The pair subsequently developed Tellia between France and the United States, launching its agricultural product in California in autumn 2025. Labadie de Faÿ leads the company as CEO, while Trastour serves as CTO. Their decision to develop the business in California before expanding further across Europe is strategically significant. Rather than attempting to address the entire agricultural industry immediately, Tellia began in an environment where large commercial farms, specialist crop advisers and complex field operations provide clear opportunities to test whether conversational technology can replace established administrative routines.
California’s agricultural landscape offers a particularly useful testing ground. Commercial growers manage large areas, employ workers with different linguistic backgrounds, coordinate multiple teams and depend on timely agronomic decisions. In crops such as almonds, walnuts and wine grapes, observations made during field inspections can directly influence decisions about pest management, irrigation and harvest timing. These are valuable but repetitive workflows, providing a practical setting in which to demonstrate the benefits of faster documentation. Importantly, Tellia did not need to convince its initial customers to adopt a completely unfamiliar activity. Agricultural workers were already describing their observations to colleagues, sending voice messages and making telephone calls. The startup’s proposition was to capture the value of those interactions without adding another layer of work.
This reveals an important characteristic of Tellia’s commercialisation strategy: the company is using an existing behaviour as the entry point for a new technology. Instead of marketing artificial intelligence through its technical sophistication, Tellia markets the removal of administrative friction. A farmer does not necessarily need to understand language models, automated data extraction or AI agents to appreciate the benefit of producing a field report without spending an evening typing. The product therefore translates a technically complex capability into a familiar economic proposition: fewer hours spent on paperwork, faster communication, and greater visibility over daily operations. For startups operating in industries with fragmented technology adoption, this approach can be more effective than selling an ambitious vision of transformation before demonstrating immediate usefulness.
What Happens After Someone Speaks?
The simplest way to understand Tellia’s technology is to imagine an agronomist inspecting a vineyard. During a routine visit, she identifies symptoms consistent with fungal disease in one block, observes that another section is developing normally, and recommends that the manager review a treatment schedule. Instead of writing everything down and preparing a report later, she sends a voice note describing what she has seen. Tellia processes the message, identifies the relevant field, crop, observations and proposed actions, and creates structured records. Depending on the workflow and available information, the system can associate observations with locations, timestamps, photographs and weather conditions, before generating a report or assigning a follow-up task. What began as an informal conversation becomes information that the wider organisation can search, share and act upon.
The underlying commercial value is in the transformation from unstructured communication into usable operational data. Speech recognition alone is not sufficient. A conventional transcription service might reproduce every word of the agronomist’s message without understanding which information belongs in a scouting report, which observation concerns a particular block, and which statement requires somebody to take action. Tellia’s proposition combines natural-language interpretation with agricultural context and business workflows. It must recognise that the same conversation may contain several distinct records and that seemingly minor details, such as the number of a vineyard block or the timing of a spray application, can be crucial. The company’s public materials describe the use of large language models to organise multilingual inputs, although the precise technical architecture, underlying model providers and independently measured extraction accuracy have not been publicly detailed.
This contextual capability becomes particularly relevant when agricultural operations involve several languages. A research employee might document a field trial in German, a production worker might describe crop conditions in Italian, and a farm manager might review the resulting information in English. Tellia is designed to accommodate multilingual communication and convert it into consistent records, reducing the need for every worker to use the same reporting language. The practical significance becomes clear in one deployment described by Labadie de Faÿ: staff working across seed research and production operations in Germany, France and Italy use different languages to describe trial activities, spray applications and pest observations, with the information standardised for central records. The technology is not creating agricultural expertise. It is attempting to make expertise transferable across languages, locations and organisational boundaries.
Tellia also allows businesses to supply their own technical documents, agronomic protocols, training materials and internal knowledge bases. This is an important distinction from a general-purpose chatbot. Agricultural recommendations are highly context-dependent: a treatment appropriate for one crop or region may be unsuitable elsewhere, while individual businesses often operate according to specific procedures. By drawing on customer-provided materials, Tellia aims to make its responses more relevant to the organisation using it. Its website also describes functions for retrieving historical field information, preparing reports, generating reminders and supporting operational questions. The company is moving beyond passive documentation toward systems that can initiate or coordinate tasks. Nevertheless, the boundary between assistance and professional judgment remains critical. Tellia’s own CEO has emphasised that the agronomist remains responsible for diagnosis. The AI is intended to reduce the time between observation and action, rather than independently assume responsibility for agricultural decisions.
A Walnut Farm Shows Where the Value Really Lies
One of the most revealing examples of Tellia’s commercial proposition comes from Campos Brothers Farms in California. In a case study published by the startup in March 2026, pest control adviser Rob Weaver described the difficulty of coordinating information across an extensive walnut operation involving five farm managers. Before using Tellia, obtaining a complete overview of daily activities required multiple telephone calls, followed by the familiar process of writing notes, transcribing reports and distributing information. This is not a particularly glamorous problem, but it is precisely the kind of recurring inefficiency that can make an enterprise technology commercially valuable. A skilled adviser may spend a substantial portion of the working day collecting information that other employees already possess, leaving less time for the expertise for which the business actually employs him.
Tellia’s deployment changed the information flow. According to the company’s account, the assistant contacts ranch foremen, collects updates on completed activities and upcoming work, and brings the information into a central location that Weaver can review. Field reports can also be generated directly from spoken observations. The value therefore extends beyond faster transcription. Tellia reduces the number of separate conversations required to coordinate work and makes operational knowledge available to more people without depending on a single manager’s memory. This demonstrates a transition from an individual productivity tool to an organisational coordination system. If the information from several workers becomes continuously accessible, managers can monitor tasks, identify unresolved issues and make decisions with less communication overhead.
The distinction matters for Tellia’s business model. A transcription assistant might be evaluated according to how many minutes it saves per report. An operational coordination platform can potentially justify its price through several benefits at once: reduced administration, fewer duplicated conversations, improved documentation, better task visibility and increased capacity for agronomists to serve more clients. An independent crop adviser who spends less time producing reports may be able to handle additional growers without expanding the workforce at the same rate. A large agricultural business might benefit from fewer delayed interventions or more consistent operational records. These are economically different sources of value, and they create opportunities for Tellia to serve customers with very different sizes and budgets. The published Campos Brothers case is company-produced rather than an independent return-on-investment study, but it illustrates the logic of the product particularly well.
The Commercialisation Strategy: Sell the Workflow, Then Become the Infrastructure
Tellia’s commercialisation strategy is arguably more interesting than the AI technology itself. At first glance, the company resembles a vertical software-as-a-service business: it offers specialised digital capabilities to agricultural organisations, with onboarding and ongoing customer support. Its website emphasises demonstrations, tailored configuration and assistance with incorporating a customer’s protocols and operational knowledge. This suggests a relationship-driven enterprise sales approach, where potential customers experience the product in a specific workflow before expanding usage across an organisation. Rather than selling generic artificial intelligence, Tellia can demonstrate what happens when an agronomist’s field observations become a completed report or a manager’s spoken update becomes a task visible to the entire team.
However, Tellia’s ambitions extend beyond selling its own interface directly to farms. The company is also developing an application programming interface, or API, through which existing agricultural technology providers can integrate its capabilities into their products. This creates a second commercial route: instead of persuading every farm to adopt an independent Tellia workspace, the startup can potentially reach users through software platforms they already know. An agricultural management system, for example, could use Tellia to transform spoken observations into records inside its existing product. The software provider would gain a more accessible data-entry system without developing the underlying voice technology itself, while Tellia would gain distribution through an established customer relationship.
This dual approach (direct software sales alongside embedded infrastructure) is central to Tellia’s growth potential. Direct customers provide the company with feedback, demonstrate real-world use cases and allow it to refine the technology around actual agricultural workflows. Integration partnerships offer a different form of scale, potentially reaching many organisations without the same level of direct customer acquisition. The strategic attraction is particularly strong in a fragmented software market where numerous companies already provide farm management, agronomic consulting, crop monitoring and agricultural reporting tools. Rather than replacing every one of these platforms, Tellia can seek to become the input mechanism that improves them.
There is also a subtler strategic advantage. Many software businesses compete to become the central system in which customers store information. Tellia has publicly emphasised that it does not want to build another platform that claims ownership over all agricultural data. By presenting itself as an interface that can work with existing systems, it reduces the threat it poses to potential partners. This can make integration more attractive than it would be with a company openly trying to replace the partner’s software. Nevertheless, the distinction creates a longer-term tension. If Tellia becomes the primary way users communicate with agricultural software, the company could gain significant influence over the customer experience even without controlling the underlying database. The interface through which information enters a system may eventually become as strategically important as the system where that information is stored.
A particularly revealing development arrived in October 2026 with the introduction of Tellia Lite, a version designed for independent agronomists, crop advisers and smaller agricultural teams. The company’s current website presents Lite as available, with onboarding arranged through an introductory call and a mobile experience on iOS and Android. The product allows users to record observations through voice notes, messages or photographs, associate them with specific fields, and retrieve previous information when required. It also advertises offline recording with synchronisation once connectivity returns, an important practical consideration in rural operations. The strategic implication is clear: Tellia is expanding beyond larger organisations and enterprise-style deployments toward users with simpler needs. This could broaden the potential customer base, while providing a path for smaller customers to adopt more extensive capabilities as their operations develop.
The two approaches create an interesting commercial balancing act. Enterprise clients may require tailored onboarding, organisational integration, dedicated support and complex reporting workflows. Independent advisers, by contrast, are likely to value simplicity, predictable pricing and minimal setup. Serving both segments successfully requires Tellia to preserve its core technological advantages while keeping acquisition and servicing costs proportionate to customer value. As of October 2026, the company has not publicly disclosed sufficiently detailed pricing, revenue, customer acquisition costs or software margins to assess those economics. The commercial model is therefore best understood through its positioning and distribution strategy rather than through unverified assumptions about subscription revenue.
Five Million Dollars, One Million Acres, and the Question of Traction
Tellia’s progress attracted significant attention in September 2026, when the company announced a $5 million pre-seed funding round, equivalent to approximately €4.3 million. The investment was led by Revent, with participation from Grey Silo Ventures, Jeriko and Fund F. The funding was particularly notable for a young company operating in an industry where commercial adoption can be difficult and sales cycles may be influenced by agricultural seasons. Tellia said the capital would support further development of its voice AI, expansion of its agentic capabilities, integration with other agricultural software, and recruitment across the United States and Europe.
The composition of the investment group offers some insight into the company’s strategic appeal. Revent invests in ambitious early-stage technologies, including businesses addressing substantial problems in physical industries. Grey Silo Ventures, meanwhile, is the corporate venture capital arm of Italy’s Cereal Docks Group, giving the funding syndicate a direct connection to the broader agricultural and food industry. This combination of technology-focused and industry-connected investors is relevant because agricultural startups need more than promising software. They also require access to operational knowledge, commercial networks, sector-specific credibility and customers willing to test unfamiliar products. Strategic investors cannot guarantee commercial success, but they can offer knowledge and relationships that may be especially valuable during the transition from early adoption to broader market penetration.
Tellia reported that its technology was being used across approximately one million acres of agricultural operations, with deployments involving Campos Brothers Farms and Duckhorn wineries in the United States, and organisations including the Institut Français de la Vigne et du Vin, Val de Gascogne and seed producer KWS Saat in Europe. The variety of these deployments matters. Almond and walnut farming, vineyards, agricultural cooperatives and seed breeding involve different production processes, yet they all depend on observations and operational updates that must be accurately recorded. This suggests that Tellia’s core technology may be adaptable across several agricultural segments rather than restricted to one crop or narrow use case.
Perhaps the more interesting figure is the company’s claim that 80% of US field teams using Tellia interact with it daily within two months of onboarding. High-frequency engagement is potentially more meaningful than the number of customers signed, particularly in agricultural software, where purchases do not always translate into consistent use. An assistant that becomes part of daily reporting routines has a better chance of producing recurring value than a platform opened only occasionally. However, both the acreage and usage figures are company-reported. The publicly available disclosures do not establish the number of paying customers, how the engagement figure is calculated, or whether daily usage persists across multiple agricultural seasons. Nor does deployment acreage translate directly into revenue or market share. Tellia has presented encouraging evidence of product adoption, but its financial scalability is not yet publicly demonstrated.
The Competition Is Becoming More Intelligent
Tellia’s opportunity exists within an increasingly competitive agricultural technology landscape. Companies such as FarmQA already provide digital systems for agronomic scouting, field observations and reports, demonstrating that the underlying demand for agricultural information management is well established. Voice-based agricultural data capture is not entirely new either. AgVoice, which developed specialised voice-to-data technology for agricultural professionals, received patent protection for its approach in 2021. These examples matter because they challenge any assumption that Tellia has invented the idea of using speech to document field activity. Its differentiation must instead emerge from usability, natural-language understanding, multilingual capabilities, integration flexibility, customer experience and the practical sophistication of its workflows.
Competition is also arriving from considerably larger technology businesses. In September 2026, John Deere introduced JD, an artificial intelligence assistant integrated into its Operations Center platform. The product allows farmers to ask questions about historical field, machine and operational data, reducing the need to navigate multiple reports and dashboards. John Deere’s offering focuses heavily on interpreting information accumulated within its existing ecosystem, while Tellia places greater emphasis on capturing the observations and conversations that might otherwise never enter a digital system. The products are therefore not identical, but their ambitions increasingly overlap: making agricultural data accessible through conversational interfaces. John Deere’s extensive installed base and established customer relationships also demonstrate the advantages that incumbents possess when introducing new AI capabilities.
This creates both an opportunity and a risk for Tellia. On one hand, the entry of major industry players validates the growing importance of conversational interfaces in agriculture and may increase customers’ willingness to adopt them. On the other, speech recognition and general-purpose language models are becoming increasingly accessible, lowering some barriers to imitation. The startup cannot depend indefinitely on the novelty of voice interaction. Its longer-term defensibility will depend on how accurately it interprets domain-specific information, how reliably it integrates with agricultural workflows, how much customers trust it, and how effectively it establishes distribution partnerships. The challenge is to make Tellia valuable because it understands agriculture, not merely because it understands speech.
Trust, Agricultural Knowledge and the Ownership of Data
An interesting feature of Tellia’s strategy is the emphasis it places on protecting customers’ information. Agricultural data can be commercially sensitive, revealing production conditions, treatment decisions, yields, operational weaknesses and business practices. A system that collects previously undocumented field observations may create an especially detailed picture of how a farm operates. Understandably, customers may question where this information is stored, who can access it, and whether it could eventually benefit competitors, suppliers or other third parties. These concerns are not theoretical. The European Commission’s research on agricultural digitalisation identifies privacy, data security and control over information as important influences on farmers’ willingness to adopt and share digital data.
Tellia positions privacy as part of its commercial proposition. According to its published policy explanations, it does not sell customers’ farm records to third parties, provides export and deletion options, and uses separate infrastructure for European and American customers. The company also states that enterprise AI providers do not use customer inputs to train their commercial models, while acknowledging that strictly anonymised inputs may be used to improve Tellia’s own voice technology. These are company statements rather than independently verified guarantees, and the details of contractual safeguards remain important for prospective enterprise customers. Nevertheless, they illustrate a strategic choice. Tellia is attempting to earn revenue by making customers’ information useful to them, rather than presenting the resale of agricultural data as its primary business opportunity.
There is a deeper question beneath the privacy discussion: who should benefit economically from knowledge that already exists within an organisation? Agricultural workers accumulate expertise through experience, including familiarity with particular fields, recurring problems and subtle changes that might not be captured by sensors. Much of this knowledge has historically been difficult to organise or share because it is embedded in individual judgment and informal communication. Tellia makes some of it accessible to the wider business. That creates potential productivity gains, but it also raises questions about accuracy, consent, accountability and the balance between human expertise and increasingly automated managerial oversight. A worker’s observation can become a valuable record, yet interpreting it incorrectly could produce a misleading report or inappropriate recommendation. The system’s value therefore depends not only on capturing more information but also on maintaining its meaning.
This is why Tellia’s human-centred positioning matters. Rather than promising to replace agronomists, it describes artificial intelligence as a tool that handles documentation, coordination and information retrieval while allowing professionals to retain responsibility for agricultural judgment. Such positioning may make adoption easier in an industry where practical experience and professional relationships are central to decision-making. It is also a commercially sensible way to introduce AI into high-consequence workflows. Customers may be more willing to trust a system that organises their expert observations than one claiming it can independently make complex agronomic decisions. Tellia’s strongest proposition is not artificial intelligence replacing agricultural knowledge, but artificial intelligence making existing agricultural knowledge more usable.
The Next Challenge: Turning Adoption Into a Lasting Business
Tellia has identified a compelling entry point into an industry with substantial operational complexity. But moving from a promising product to a durable technology company will require answers to questions that early funding announcements cannot provide. Can the system maintain high accuracy across different crops, regional terminology, accents and languages? How much human checking is required before automatically generated records can be used for compliance or consequential agronomic decisions? Can integrations with established farm management platforms be standardised, or will every enterprise customer require expensive customisation? And can Tellia demonstrate measurable financial returns that justify recurring expenditure during periods of pressure on agricultural margins?
Seasonality introduces another important consideration. Agricultural workflows change significantly across planting, growing, harvesting and post-harvest periods. A product that proves indispensable during one part of the year may experience different usage patterns during another. Tellia’s ability to capture routine activities, equipment maintenance, inventory information and livestock-related records could help broaden engagement beyond isolated scouting periods. Yet the company still needs to demonstrate that users retain the habit over time and that the value generated is sufficiently consistent to support renewals. The most persuasive evidence would be independently measured reductions in reporting time, sustained customer retention, improvements in operational responsiveness and expansion from initial users to additional teams.
Its platform strategy creates a second test. Embedding Tellia inside other agricultural products could significantly accelerate distribution, but partnerships also introduce dependency. Established software companies control their customer relationships and may eventually build their own conversational features. Tellia must therefore provide enough specialised value for partners to prefer integration over internal development. One possibility is that its advantage will accumulate through deep understanding of agricultural language, flexible workflow configuration and experience across different production environments. Another is that conversational interfaces become standard software features, making standalone differentiation more difficult. Neither outcome is predetermined, and the company’s current scale is too early to establish a lasting competitive moat.
There is also a question of how far the underlying technology can travel. Tellia describes its ambition in terms broader than agriculture alone: developing a voice-first operating interface for field work. The challenges it addresses are not unique to farms. Construction sites, logistics operations, industrial maintenance teams and environmental inspectors also generate valuable information in environments where opening a computer is inconvenient. A technology capable of converting informal speech into structured, reliable operational records could potentially find applications across numerous physical industries. Agriculture, with its linguistic diversity, complex workflows and difficult working conditions, may serve as a demanding first market. However, expansion beyond the sector remains a strategic possibility rather than a proven commercial direction.
The Quiet Innovation Behind Tellia
What makes Tellia interesting is that its innovation does not depend on inventing a new agricultural machine, discovering a novel biological process or producing a breakthrough in artificial intelligence research. Its central insight is about the relationship between technology and human behaviour. For years, digital transformation has often required workers to accommodate the limitations of software: adapting their routines, entering information according to fixed categories, and learning increasingly complex interfaces. Tellia proposes the reverse. Instead of changing how people communicate, software should become capable of understanding those communications and transforming them into something useful.
This is a particularly relevant lesson for the commercialisation of artificial intelligence. Technological sophistication does not automatically produce customer demand. A startup must identify where an available capability creates a meaningful improvement in how people already work, determine who benefits economically, and design a business model that allows value to be captured repeatedly. Tellia’s initial use cases focus on an immediately recognisable problem: agricultural professionals spend too much time translating their knowledge into administrative records. Its larger ambition is to build a position within the information infrastructure that connects agricultural workers, management systems and decision-making processes. The commercial journey from one to the other is still underway.
Whether Tellia ultimately becomes an independent agricultural technology platform, a widely embedded software infrastructure provider, or an attractive acquisition target will depend on its ability to turn promising deployments into durable competitive advantages. For now, the company represents a revealing shift in the direction of technological innovation. The most important advances are not always those that generate entirely new information. Sometimes, they are those that recognise the value of information already being produced and remove the obstacles preventing it from being used.
The future of agricultural intelligence may not begin with another sensor, another dashboard or another algorithm. It may begin with a farmer describing what happened in the field and, for the first time, the software actually listening.
