Meet Fin Apex 1.0The best-performing model for
for customer service

Introducing the
Fin model suite

• [ 1 ]

  Fin Apex 1.0 [ new ]

  

  Fin Apex 1.0 is the model that produces the final answer Fin gives to every customer. It takes the most relevant content surfaced by the Fin model suite, applies your policies, and produces a direct answer or decides the question needs a human. Every output is grounded in your knowledge base, not inferred from general training data.

  Specification

  • 1.1Grounded in your knowledge base, not general data
  • 1.2Answers directly, escalates honestly when it can't
  • 1.3Follows your configured guidelines every time
  • 1.4Trained to minimize hallucination at the source
  • 1.5Post-trained on Fin's own production data
  • 1.6Compounds in accuracy as the suite improves
• [ 2 ]

  Fin Retrieval

  The Fin Retrieval model scans all available knowledge sources and pulls out a small set of useful information.

  Specification

  • 2.1Understand intent behind the question
  • 2.2Search across all available content sources
  • 2.3Match on semantics, not just keywords
  • 2.4Select top N most likely candidates
• [ 3 ]

  Fin Reranker

  The Fin Reranker model takes the retrieved content and scores each piece for relevance, accuracy, and usefulness in context. It then selects the final piece(s) for the LLM to use.

  Specification

  • 3.1Score relevance with respect to query
  • 3.2Evaluate context match and resolution fit
  • 3.3Downrank outdated or low-confidence sources
  • 3.4Output: final selected content for generation
• [ 4 ]

  Fin Issue Summarizer

  The Fin Issue Summarizer model detects and extracts the user's issue from conversation history. Built on a fine-tuned 14B model, it transforms unstructured chat exchanges into clear, actionable issue summaries for downstream retrieval.

  Specification

  • 4.1Detect whether conversation contains an addressable issue
  • 4.2Extract summarized issue from multi-turn exchanges
  • 4.3Handle edge cases: greetings, feedback, and noise
  • 4.412.5% cost reduction vs frontier LLMs
• [ 5 ]

  Fin Feedback Parser

  The Fin Feedback Parser model uses a multi-task ModernBERT architecture to interpret user responses. It classifies feedback sentiment, detects follow-up questions, and identifies conversation endings with state-of-the-art accuracy.

  Specification

  • 5.1Classify feedback type: positive, negative, or none
  • 5.2Detect follow-up questions in user messages
  • 5.3Identify when conversations have ended
  • 5.4Match LLM accuracy at a fraction of the cost
• [ 6 ]

  Fin Language Detector

  The Fin Language Detector model uses XLM RoBERTa to accurately identify the user's language across 45 supported languages. It handles real-world challenges like typos, short messages, and script mismatches.

  Specification

  • 6.1Support 45 languages with high accuracy
  • 6.2Handle spelling mistakes and short context
  • 6.3Process script mismatches (e.g., Romanized Hindi)
  • 6.4Enable intelligent language fallback logic
• [ 7 ]

  Fin Escalation Router

  The Fin Escalation Router model uses a multi-task ModernBERT architecture to decide when conversations should be handed off to human agents. It provides reasoning and cites matching business guidelines with over 98% accuracy.

  Specification

  • 7.1Decide: escalate, offer to escalate, or continue
  • 7.2Cite matching business escalation guidelines
  • 7.3Provide reasoning for escalation decisions
  • 7.40.5s faster than LLM-based routing