Improve FIN AI Deflection Accuracy

@devanshkhandekar

Hi,I am a seasoned Applied AI Engineer & I can help you raise FIN answer accuracy by improving coverage + training examples, which is the fastest lever for higher deflection when UI/core routing is already in place. Relevant Experience: Self-Serve Optimization: Audited support logs to build intent/FAQ datasets,tightening retrieval & grounding Dataset Curation: Created "golden" Q/A sets from noisy logs,capturing edge cases & policy-aligned responses Evaluation Frameworks: Built harnesses to track accuracy, containment & handoff drivers Implementation Roadmap Gap Analysis: Audit logs and KB to cluster intents & identify failure modes (missing articles, ambiguity or hallucinations). Dataset Construction: Build structured training sets including canonical questions, paraphrases & edge cases with citations KB Optimization: Merge & rewrite articles to ensure a single,authoritative source per topic Integration & Import: Deliver Intercom-compatible datasets & manage the import process Performance Validation: Run before/after benchmarks on accuracy, deflection lift & handoff reduction Rollout: Staged deployment with a roadmap for remaining gaps Deliverables: Curated training datasets and KB recommendations Metrics report (Accuracy/Deflection/Handoffs) & maintenance documentation If you share export access to recent chat logs + current FIN knowledge base, I’ll start with the highest-impact intent clusters and deliver measurable deflection lift quickly.

@Hemantt841

I understand you require improving FIN AI’s deflection accuracy by enhancing training data quality to reduce chats reaching human agents. You want a thorough audit of recent conversation logs and the existing knowledge base to identify gaps, followed by creating well-structured, targeted training examples that Intercom can ingest seamlessly. Measuring uplift through before-and-after tests is also a key deliverable. With over 15 years of experience and 200+ projects completed, I specialize in AI development and data integration, including NLP systems and chatbot training. My background in Python and API integration equips me well to handle Intercom’s data formats and automate dataset imports, ensuring your FIN AI model benefits from precise, context-rich inputs. I will start by analyzing your conversation logs and FIN knowledge base to spot weaknesses, then curate FAQs and edge cases reflecting real user intents. I’ll prepare the dataset in Intercom’s required format and assist with import. Finally, I’ll run controlled tests to track accuracy improvements and deflection rate changes, aiming to deliver measurable results within 7-10 days. Let’s connect to discuss how I can help sharpen your FIN AI responses and ease your support team’s workload.

@Mutharasi128

Hello, I’m very interested in helping improve the performance of your **Intercom FIN AI system** by strengthening the training data and knowledge base. I have a background in **Artificial Intelligence and Data Science**, with experience in **NLP, data analysis, and building structured datasets** that improve the accuracy of automated systems. For this project, I can begin by **analyzing recent conversation logs and the existing FIN knowledge base** to identify gaps, weak answers, and missing intents that cause chats to escalate to human agents. Based on this analysis, I will create **well-structured training data**, including improved FAQs, edge-case scenarios, and clearer intent-response mappings derived from real customer conversations. I will then prepare the dataset in the **format required by Intercom FIN**, assist with the **training data import**, and conduct **before-and-after testing** to measure improvements in response accuracy and deflection rate. You will receive a **clear report showing the impact**, along with recommendations for further optimization. My goal is to help your FIN AI deliver **more accurate answers, reduce support workload, and increase successful self-service interactions**. I would be happy to discuss your current setup and start auditing the conversation data. Best regards.

@ashishs719

Hello, I reviewed your requirement about improving FIN AI response accuracy and reducing the number of chats reaching human agents. With my background in Machine Learning, NLP, and data-driven AI systems, I can help improve the quality of automated responses by focusing on training data and knowledge base optimization. My approach will include auditing recent conversation logs and the existing FIN knowledge base to identify coverage gaps, misleading responses, and missing intents. Based on this analysis, I will curate and structure high-quality training data, including improved FAQ pairs, new intents derived from real conversations, and edge-case examples that help the AI respond more accurately. I will then prepare the dataset in the format required by Intercom FIN and assist with integrating it into the system. After implementation, I will perform before-and-after testing to measure improvements in answer accuracy and support deflection rate, and provide a clear report with metrics and recommendations. My goal is to increase automated response accuracy and significantly reduce the number of conversations escalated to human agents. I would also like to know approximately how many conversation logs are available for analysis and whether FIN is currently using a custom knowledge base. Best regards, Ashish Sharma

@vijayt86

I have experience building AI chatbot and RAG-based systems, and I believe I can help improve the accuracy of your FIN AI responses. I can analyze conversation logs, identify knowledge gaps, and create well-structured training data to improve automated responses and reduce tickets reaching human agents. I will also help prepare the dataset for Intercom, test improvements, and provide metrics showing the impact on response accuracy and deflection rate. Since this is my first project here, I’m happy to offer a discounted rate and ensure high-quality results before the deadline. Best regards, Vijay

@sushantr69

Hello, Your analysis makes sense — when too many conversations escalate to human agents, the issue is often not the AI system itself but the **quality and structure of the training data**. I specialize in improving AI response accuracy by refining knowledge bases, training data, and prompt structures. Here’s how I would approach your project: 1. **Conversation Audit** – Review recent Intercom chat logs and the FIN knowledge base to identify coverage gaps, incorrect answers, and missing intents. 2. **Training Data Improvement** – Create structured FAQs, edge-case scenarios, and clearer intent examples based on real customer conversations. 3. **Dataset Structuring** – Prepare the dataset in the format required for Intercom FIN and assist with the import process. 4. **Performance Testing** – Run before-and-after evaluations to measure answer accuracy and track improvements in the deflection rate. My focus would be to **increase automated resolution and reduce tickets reaching human agents**, with clear metrics showing the improvement. I’d be happy to review a sample of your chat logs and knowledge base to quickly identify the biggest opportunities for improvement. Looking forward to discussing this further. Best regards, Sushant