Tekst.ai is an AI tool that automates repetitive workflows and handles incoming communications across enterprise support and operations teams. Utilizing an Agentic Process Automation layer, the platform automatically analyzes the context, intent, and sentiment of incoming messages to label, route, and prioritize them. It also performs intelligent document processing to extract structured data from attachments and inbound emails, sending the extracted information directly into customer relationship management and enterprise resource planning systems. Tekst.ai enables automated case enrichment and order intake, reducing the burden of manual data entry and sorting. The platform is designed for enterprise operations managers, customer support leaders, supply chain and logistics coordinators, and IT process automation specialists seeking to streamline shared inbox management and ticket handling. Custom AI models can be applied to enterprise data to provide real-time operational visibility insights. Pricing details for Tekst.ai are not publicly specified.
Problem: Enterprise teams are often overwhelmed by a high volume of unstructured emails in shared inboxes, leading to missed opportunities and slow response times.
Solution: Tekst.ai implements an intelligent automation layer that interprets the context and intent of every message, automatically labeling and routing them to the correct department.
Example: A customer service department at a large retail firm uses Tekst to sort thousands of daily emails into categories like 'Refunds' or 'Logistics' with 90% accuracy.
Problem: Manually entering order data from emails or attachments into ERP systems like SAP is time-consuming and highly susceptible to human error.
Solution: The platform uses Intelligent Document Processing to extract relevant data from orders and syncs it directly with existing enterprise software without manual intervention.
Example: A manufacturing company automates its quote-to-cash workflow by allowing Tekst to extract product quantities from PDF orders and create records in their CRM.
Problem: Support agents often lack the necessary context to handle complex incidents quickly, resulting in long resolution times.
Solution: Tekst classifies incoming tickets and enriches them with contextual insights and background information before they even reach a human agent.
Example: A MedTech firm uses Tekst to automatically identify and prioritize high-urgency product incident reports, attaching customer history to the case for immediate action.
Target audience: Best for: Enterprise operations managers, Customer support leaders, Supply chain and logistics coordinators, IT and process automation specialists
Pricing: Unknown · Categories: Customer Support, Suggested Tools
Tags: Customer Support, human resources, research, startup tools
Tekst.ai provides an Agentic Process Automation layer that manages shared inboxes and repetitive operational workflows. It performs contextual intent and sentiment analysis to automatically label and route emails. Additionally, it automates document and order intake by extracting data from attachments and syncing it with CRM and ERP platforms, while providing real-time operational visibility insights.
Tekst.ai is built primarily for enterprise teams handling large volumes of incoming communications. Its target audience includes enterprise operations managers, customer support leaders, supply chain and logistics coordinators, and IT process automation specialists who need to eliminate manual data entry, reduce ticket triage delays, and enrich incoming cases with relevant customer context.
Tekst.ai automates order intake using intelligent document processing. The system reads incoming emails and document attachments, such as purchase orders, and extracts necessary data fields like product quantities. It then passes this structured data directly into enterprise software, including ERP systems like SAP and connected CRM platforms, without requiring manual data transcription.
Yes, Tekst.ai is designed to integrate directly with enterprise CRM and ERP systems. This integration allows the platform to extract information from incoming messages and documents, update core databases automatically, and pull contextual background records to enrich support cases before human agents review them.