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.