An AI agent reads your parts list, figures out who sells what, places the orders, and tracks everything so your engineers never have to chase a supplier again.

Reframe's core operational use case deploys LLM-powered AI agents that ingest raw BOMs in various formats (spreadsheets, PDFs, Slack messages), parse and normalize component data using a tabular data ecosystem, and then orchestrate procurement workflows as directed acyclic graphs (DAGs). Each node in the DAG represents a discrete task—vendor lookup, price comparison, order placement, delivery tracking—executed by specialized agents with persistent agentic memory. The system resolves ambiguities (e.g., alternate part numbers, supplier substitutions) through prompt chaining and tool use, querying supplier APIs and internal knowledge bases. When delays or stockouts occur, agents dynamically re-route orders and notify stakeholders in Slack with contextual updates. This eliminates the traditional procurement bottleneck where engineers spend hours manually emailing vendors, cross-referencing spreadsheets, and tracking shipments across disconnected systems.

It's like having a hyper-organized personal assistant who reads your grocery list, checks every store's inventory and prices, places the orders, tracks the deliveries, and texts you only when something actually needs your attention.

An AI handles all the back-and-forth emails with your parts suppliers, catches problems before they derail your build, and fixes them automatically.

Reframe deploys specialized AI agents that serve as the primary communication interface between hardware engineering teams and their supplier networks. These agents compose, send, and interpret vendor emails and messages using LLM-driven natural language generation and comprehension. When a supplier responds with a delay notification, price change, or part discontinuation, the agent parses the response, cross-references it against the active BOM and build timeline, assesses impact severity, and determines the optimal resolution path—whether that's accepting a substitute component, escalating to a human engineer for approval, or automatically sourcing from an alternate vendor. The system maintains a persistent conversation history and contextual memory per supplier relationship, enabling increasingly accurate and nuanced interactions over time. For engineering teams, this eliminates the enormous cognitive overhead of managing dozens of concurrent vendor threads, translating technical specifications across communication formats, and manually triaging procurement exceptions. The agents operate within Slack, surfacing only decisions that require human judgment while autonomously handling routine confirmations, follow-ups, and status updates.

It's like having a multilingual procurement diplomat who never sleeps, never forgets a conversation, and only bothers you when a decision actually matters.

An AI learns from every past order and supplier interaction to predict which parts will be late or unavailable before it actually happens, so you can plan ahead.

Reframe's data layer aggregates and structures every procurement interaction, supplier response time, lead-time variance, pricing fluctuation, and component availability signal into a unified procurement intelligence dataset. Machine learning models trained on this historical data identify patterns that predict supply chain disruptions—such as a supplier consistently missing lead times on specific component categories, seasonal price spikes for certain materials, or early indicators of part end-of-life. The system generates proactive alerts and recommendations: pre-ordering critical components before anticipated shortages, flagging suppliers whose reliability metrics are declining, and suggesting optimal order timing based on historical price and availability curves. Over time, as the platform processes more transactions across its customer base, these models benefit from network effects—each customer's procurement data (anonymized and aggregated) improves prediction accuracy for all users. This transforms Reframe from a workflow automation tool into a strategic procurement intelligence platform, enabling hardware teams to shift from reactive firefighting to proactive supply chain management. The tabular data ecosystem underlying the BOM management system provides the structured foundation for feature engineering, while the agentic memory layer captures unstructured context (vendor sentiment, negotiation outcomes) that enriches model inputs.

It's like a weather forecast for your supply chain—instead of getting soaked by a surprise parts shortage, you grab an umbrella three weeks early.