Pollinate: AI Agents Rebuilding Supply Chains

Pollinate is a San Francisco–based startup founded in 2024 that is building AI agents for the supply chain—an industry that, despite its scale and importance, still runs largely on spreadsheets, inboxes, and manual work. Backed by the Winter 2026 batch and led by a two-person founding team, Pollinate is tackling one of the most persistent contradictions in modern operations: highly complex global logistics workflows managed with tools that were never designed for how supply chains actually function.

While enterprise resource planning (ERP) systems promise structure and automation, they often fail in practice. They are rigid, expensive to customize, and poorly equipped to handle the messy, unstructured data that dominates real-world operations. As a result, supply chain teams spend an enormous amount of time manually reconciling invoices, matching purchase orders, coordinating vendors, and fixing breakdowns that software should have prevented.

Pollinate exists to change that dynamic. Instead of forcing teams to adapt their workflows to generic tools, the company is building a system that adapts to the workflows themselves—using AI agents that operate on top of an ontology designed specifically for supply chain operations.

Why Do Supply Chains Still Run on Spreadsheets and Inboxes?

Despite decades of investment in enterprise software, supply chains remain deeply manual. Critical processes like invoice reconciliation, vendor coordination, and production scheduling frequently live in spreadsheets, email threads, and ad-hoc internal tools. According to Pollinate, supply chain teams waste roughly 40% of their time on work that their ERP systems are theoretically supposed to handle.

The root cause is not a lack of software, but a mismatch between how software is built and how supply chains actually operate. Off-the-shelf tools require companies to change their processes to fit predefined models. Custom development, on the other hand, takes six to twelve months—often longer—and demands engineering resources that operations teams simply do not have.

Supply chains are also inherently messy. Data arrives from emails, PDFs, scanned invoices, text messages, phone calls, and multiple vendor systems. ERPs expect clean, structured inputs, but reality rarely complies. When exceptions arise—and they always do—teams fall back to manual fixes. Over time, these fixes become the system.

Pollinate’s insight is that the problem is not user error or poor discipline. The problem is that traditional software was never designed for the variability and uniqueness of supply chain operations.

How Does Pollinate Rethink Software for Supply Chain Operations?

Rather than building another rigid platform, Pollinate approaches the problem from first principles. The company is constructing an ontology—a structured representation of how supply chain data, entities, and workflows relate to each other. This ontology allows AI agents to understand context, reason about operations, and produce real outcomes rather than isolated automations.

Instead of hardcoding workflows, Pollinate connects directly to a customer’s existing ERP and data sources. From there, the platform automatically maps data structures and identifies operational patterns unique to that organization. These patterns become the foundation for custom tools and agents that mirror how the team already works.

In practical terms, this means Pollinate does not ask customers to abandon their systems or retrain their teams on generic interfaces. The tools are built specifically for the customer’s processes, terminology, and exceptions. What previously required months of engineering can now be deployed in days.

This shift—from static software to adaptive, agent-driven systems—marks a fundamental change in how operational software is conceived.

Why Did Pollinate Start With Procurement and Three-Way Matching?

Pollinate’s first focus area is procurement, specifically automating three-way matching: the process of matching supplier invoices to purchase orders and receipts. This task is both ubiquitous and painfully manual, especially for companies dealing with high purchasing volumes and fragmented vendor communication.

Invoices often arrive via email in inconsistent formats. Purchase orders live inside ERPs. Receipts may come from warehouse systems or third-party logistics providers. When mismatches occur, humans step in—opening spreadsheets, cross-checking documents, and emailing vendors.

Pollinate’s agents extract invoices directly from inboxes, interpret their contents, and match them to the correct purchase orders and receipts across ERP systems. By operating on top of the company’s ontology, these agents understand not just the data fields, but the operational meaning behind them.

This approach allows procurement teams to process large volumes of invoices without increasing headcount, while dramatically reducing errors and delays. It also creates a foundation for expanding automation into adjacent workflows like vendor management and production planning.

How Does Pollinate’s Platform Actually Work in Practice?

Pollinate’s workflow begins by connecting to a customer’s existing ERP and data sources. There is no requirement to rip and replace current systems. Once connected, the platform analyzes the data structure, identifies recurring operational patterns, and maps how information flows across teams and tools.

From this mapping, Pollinate generates custom applications and AI agents tailored to the customer’s workflows. These tools may handle invoice processing, order intake, vendor coordination, or production scheduling—depending on where the operational bottlenecks lie.

Crucially, the output is not a generic dashboard. Teams interact with tools designed around their actual processes. Emails, texts, and phone-based orders can be ingested directly into the ERP. Invoices are reconciled automatically. Production schedules adjust based on real-time inputs.

The result is a system that feels less like traditional software and more like an extension of the operations team itself.

What Kinds of Workflows Are Customers Already Automating With Pollinate?

Pollinate’s customers are using AI agents to automate some of the most time-consuming and error-prone workflows in supply chain operations. These include order processing from unstructured inputs like email, text, or phone calls, which are automatically converted into ERP entries without manual intervention.

Three-way invoice reconciliation is another major use case, particularly for organizations managing thousands of vendor invoices. Instead of manually matching documents, Pollinate’s agents perform reconciliation at scale while flagging only true exceptions for human review.

Hardware teams are also using Pollinate for just-in-time production planning. By connecting procurement, inventory, and production data, agents help coordinate assembly schedules and reduce delays caused by missing components or misaligned orders.

These automations are not theoretical. Pollinate is already processing over $100,000 in purchasing volume through workflows that customers were unable to build on their own using traditional tools.

Who Are the Founders Behind Pollinate and What Shaped Their Perspective?

Pollinate was founded by Corey Berther and Adeep Mitra, two operators with backgrounds that blend technical depth, entrepreneurship, and real-world execution.

Corey Berther was introduced to coding at a young age and went on to run both an electrical business and a software and marketing agency generating five figures per month. After dropping out of a physics degree, he focused on building infrastructure rather than academic theory—developing a deep understanding of how systems break under real operational pressure.

Adeep Mitra brings experience from agency building, community-driven projects, and the crypto and NFT space, where he operated initiatives with approximately 800 ETH in lifetime volume. His background includes partnerships with high-profile artists and large-scale campaigns generating over one million impressions.

Together, the founders share a bias toward building practical systems that work in messy, real-world environments—an outlook that directly informs Pollinate’s approach to supply chain software.

Why Is Now the Right Time for AI Agents in the Supply Chain?

Pollinate’s timing reflects a broader shift in how software is built. As code becomes increasingly commoditized and AI lowers the cost of development, the bottleneck moves from engineering capacity to operational understanding.

Supply chains are infinitely unique. No two organizations operate the same way, and small differences compound at scale. Point solutions can solve isolated problems, but they break when exposed to real operational complexity.

Pollinate’s founders saw this firsthand while building custom solutions for customers. They recognized that the future lies not in more rigid platforms, but in systems that can adapt dynamically to how teams actually work.

AI agents, when grounded in a well-designed ontology, offer a way to bridge the gap between software and operations. Instead of forcing humans to translate their work into software-friendly formats, the software learns to operate on human terms.

What Does Pollinate Represent for the Future of Supply Chain Software?

Pollinate represents a shift away from one-size-fits-all enterprise tools toward bespoke, adaptive systems built at machine speed. By combining ERP connectivity, data enrichment, and agent-driven execution, the company is redefining what operational software can look like.

If successful, this approach could eliminate large classes of manual work, reduce dependency on spreadsheets, and allow supply chain teams to focus on decision-making rather than data wrangling. More broadly, it suggests a future where software is no longer something teams struggle against, but something that evolves alongside them.

For an industry that underpins the global economy yet remains stubbornly inefficient, Pollinate’s vision signals a meaningful step forward—one agent, one workflow, and one ontology at a time.