Sobot AI achieves 90% accuracy and 88% independent resolution with Generative and Multi-Faceted AI

AI for Customer Support That's Built for Real Work

Sobot has released test results from 100+ customers: an average response accuracy over 90% and an independent resolution rate of 88%. The gains come from pairing Generative AI with Multi-Faceted AI inside its Five-AI system. The point isn't fancy tech for its own sake-it's practical application that improves service outcomes.

Why this matters for support leaders

• Higher containment with accurate, brand-aligned answers means fewer escalations and faster response times.
• Multilingual support across chat, voice, email, and social gives consistent service without scaling headcount one-to-one.
• Agents work faster with summaries, drafts, and one-click ticket fields handled by AI Copilot.
• Leaders get a single view of 300+ indicators-quality, VOC, and operational data-to make clear decisions.

Inside Sobot Generative AI

Sobot centers its Generative AI on Retrieval-Augmented Generation (RAG) combined with leading large language models. It integrates models from Claude, OpenAI, Amazon Bedrock, DeepSeek, and others, then applies tone and style controls so responses match your brand. This mix helps cut hallucinations and improves factual accuracy.

• Intelligent Chunking: Semantic and structure-aware splitting preserves meaning, so the system retrieves the right context.
• Precise Retrieval: Automatic query rewriting, recall thresholds, and re-ranking pull the best matches before generation.
• Enhanced Generation: Model orchestration plus fine-tuned tone produces clear, human-like answers.

Background on the RAG approach: Retrieval-Augmented Generation (original paper).

SLMs for specific tasks

Beyond general LLMs, Sobot uses Small Language Models (SLMs) for focused tasks by industry. In retail and ecommerce, SLMs handle order tracking, product recommendations, returns, and refunds with higher precision. The result is targeted reasoning where it counts-the last mile of an actual customer issue.

Multi-Faceted AI: built for every role

• AI Agent: Delivers fast, human-like service on chat, voice, email, and social. Configure tone, style, and sentence length. Multilingual out of the box.
• AI Copilot: Assists human agents with summaries, content polishing, and one-click ticket fields. It reduces busywork and keeps the agent focused on the customer.
• AI Insight: A single dashboard for reports, analytics, intelligent QA, and VOC. Track 300+ metrics to spot gaps and act quickly.

How it works in practice

• Ingest and structure knowledge; apply semantic chunking.
• Embed and index content for fast, accurate retrieval.
• Rewrite inbound questions for clarity; retrieve and re-rank results.
• Generate responses using the best-fit model and brand tone.
• Trigger actions (e.g., order status) or hand off to an agent when needed.
• Log interactions and outcomes into AI Insight for ongoing improvement.

Rollout checklist for support teams

• List top intents (e.g., refunds, order changes, shipping, account access) and gather the source-of-truth content.
• Set recall thresholds and fallback rules to control when to answer vs. clarify vs. escalate.
• Define voice, tone, and length; provide examples so the AI stays on-brand.
• Establish agent handoff criteria, including data capture and ticket fields the AI should pre-fill.
• Pilot in one channel with clear success metrics (accuracy, containment, AHT, CSAT). Iterate weekly.
• Expand to more intents and languages; introduce SLMs for high-volume workflows like order tracking and returns.

As the company puts it, "Sobot AI is not just the stacking of functions, but the deep integration with applications and users." The focus is clear: practical gains across roles-customers, agents, and admins-without adding friction.

Resources

Learn more: Sobot AI

If you're upskilling your support org on AI, explore role-based learning paths: Complete AI Training - Courses by Job