Fluent AI Quoting for Epicor Kinetic

The webinar gives Epicor Kinetic users a practical view of how an AI agent can participate in a real-world manufacturing quoting workflow, rather than discussing artificial intelligence only at a conceptual level. Attendees can see how Fluent interprets RFQs, matches customers and parts, creates priced quotes, interacts with the Epicor configurator, and addresses challenges such as incomplete information and complex pricing. The session is therefore especially relevant to manufacturing, sales, estimating, operations, and ERP teams evaluating where AI agents can reduce manual steps within their existing Epicor processes.

Fluent AI is positioned as an automation layer that works with enterprise systems, not as a replacement for Epicor Kinetic. TCP describes Fluent more broadly as providing AI workflows and automation for enterprise systems, while Epicor remains the ERP environment containing operational information and business processes. In an AI quoting use case, this means the agent can help interpret requests and automate workflow steps while Epicor Kinetic continues to serve as the underlying enterprise platform.

An AI quoting agent can streamline the RFQ-to-quote process by interpreting incoming customer requests and matching them with the ERP data needed to prepare a quote. In the Fluent AI Quoting workflow demonstrated by TCP, Fluent can match customers and parts against Epicor, prepare priced quotes, and support interactions with the Epicor configurator. This approach can reduce repetitive data-entry steps while giving sales and estimating teams a more automated workflow for managing manufacturing RFQs.

AI quoting can be one component of a larger ERP automation strategy that connects customer requests to downstream sales and operational processes. TCP's Fluent portfolio already includes enterprise automation capabilities such as data synchronization, messaging automation, analytics, invoice automation, and other AI-driven workflows. For manufacturers using Epicor Kinetic, automating RFQ processing and quote creation can therefore be considered alongside related opportunities to reduce manual work across order management, customer communications, analytics, and other ERP-dependent workflows.

Customer RFQs do not always contain every piece of information required to generate an accurate manufacturing quote. The Fluent AI Quoting webinar specifically addresses how the agent handles missing information as part of the quoting process, rather than assuming every request is complete. For Epicor Kinetic users, this illustrates how an AI quoting workflow can support exception handling while keeping incomplete or ambiguous requests from being treated as automatically quote-ready.

Manufacturers often receive requests for quotation through documents and other formats not structured for ERP entry. Fluent AI interprets RFQ information and uses the extracted details in an automated Epicor Kinetic quoting workflow. This helps bridge the gap between unstructured customer requests and structured ERP quote data without requiring every RFQ to start in a standardized Epicor format.

The Fluent AI Quoting workflow presented by TCP includes interaction with the Epicor configurator, extending automation beyond simple quote data entry. This is particularly relevant for manufacturers selling configured or variable products where the final quotation may depend on product rules, options, and configuration requirements. Connecting AI-driven RFQ interpretation with Epicor configuration processes can create a more integrated path from customer requirements to an ERP-based quote.

Manufacturers should first identify which quoting steps are repetitive and rules-driven and which require human judgment, engineering input, or commercial approval. Customer and part master-data quality, pricing complexity, configured-product requirements, missing RFQ information, and exception handling are particularly important considerations because these factors directly affect how effectively an AI quoting workflow can operate. The webinar's focus on customer and part matching, complex pricing, missing information, and Epicor configurator interaction provides a practical framework for evaluating these automation requirements.

AI-assisted quoting may be particularly relevant to manufacturers that process significant numbers of RFQs, manage large customer and part databases, sell configured products, or depend on ERP data during the estimating process. The Fluent workflow is designed around Epicor Kinetic and addresses tasks such as customer matching, part matching, pricing, quote creation, and configurator interaction. Organizations should still evaluate the workflow against their own quoting rules, approval requirements, data quality, and manufacturing processes before determining where automation provides the greatest value.

Yes. The event demonstrates Fluent matching RFQ information against customer and part data in Epicor before building the quote. This matters for manufacturers because AI-assisted quoting becomes far more useful when the quoting agent can work with existing ERP master data rather than treating each incoming request as isolated information.