Taylor AI

Use Case 1: Automated Support Ticket Categorization and Routing

Problem: Customer support teams often face a high volume of unstructured tickets, leading to manual sorting delays, inconsistent tagging, and slow response times for critical issues.
Solution: Taylor allows teams to build a custom taxonomy to classify incoming freeform text automatically. By setting confidence thresholds, the system can reliably categorize tickets into specific buckets (e.g., "Billing," "Technical Bug," "Feature Request") and route them to the correct department immediately.
Example: A user sends an email saying, "I can't log in and my subscription renews tomorrow." Taylor identifies the categories "Authentication" and "Billing," then triggers an automated workflow to prioritize the ticket for the technical support lead and the billing department.

Use Case 2: CRM Data Enrichment from Sales Notes

Problem: Sales representatives often enter detailed but unstructured notes into CRMs after calls. This valuable data remains "dark" because it cannot be easily queried or used for automated reporting on competitor trends or common deal blockers.
Solution: Taylor can be integrated with a CRM to extract specific metadata from freeform sales notes. It transforms messy text into structured fields, identifying key entities like competitor names, specific product interests, or budget mentions.
Example: A sales rep logs: "Client is interested in the Pro plan but mentioned they are also looking at [Competitor Name] because of their API speed." Taylor extracts the competitor name and the "Speed" concern, automatically updating the CRM's "Competitor" and "Risk Factor" fields.

Use Case 3: Scalable Content Moderation for Community Platforms

Problem: Growing online communities generate massive amounts of user-generated content that must be monitored for policy violations. Relying solely on humans is unscalable, while standard LLMs can sometimes be too "black box" or inconsistent for strict moderation rules.
Solution: Using Taylor’s "deterministic way to wrangle text," developers can build high-accuracy classification models for moderation. It provides total control over the labels and thresholds, ensuring that content is flagged or removed according to the platform's specific community guidelines.
Example: A user posts a comment on a forum. Taylor’s bulk classification engine checks the text against a taxonomy of "Spam," "Harassment," and "Self-Promotion." If a post exceeds a 98% confidence threshold for "Spam," it is automatically hidden and logged in a moderation dashboard.

Use Case 4: Structuring Product Feedback for Engineering Roadmaps

Problem: Product managers collect feedback from various sources—App Store reviews, Slack channels, and survey responses. Manually reading thousands of comments to find actionable insights for the engineering team is time-consuming and prone to bias.
Solution: Taylor can process bulk unstructured text to extract specific feature requests and bug reports. By structuring this data, product teams can turn qualitative feedback into quantitative data, seeing exactly which features are being discussed most frequently.
Example: A product team uploads 5,000 recent App Store reviews. Taylor extracts "UI/UX" as the primary category for 40% of the reviews and identifies "Dark Mode" as a specific requested feature, allowing the team to justify the development effort with hard data.