Sobot Unites Generative and Multi-Faceted AI to Achieve 90%+ Accuracy and 88% Independent Resolution in Customer Service

Sobot pairs Generative AI with Multi-Faceted AI to improve customer contact outcomes

Sobot has published new test results for its updated Sobot AI: an average response accuracy over 90% and an independent resolution rate of 88% across 100+ customers. The gains come from combining Generative AI with what the company calls Multi-Faceted AI-practical capabilities built for real users and real workflows, not lab demos. This approach sits within Sobot's broader "Five-AI" system, where each AI layer plays a distinct role.

What "human-quality service" looks like

Sobot's goal is simple: give customers fast, accurate, brand-aligned answers across channels while keeping humans focused on complex issues. The platform blends Retrieval-Augmented Generation (RAG), large language models (LLMs), and small language models (SLMs) to ground responses in your policies, data, and tone of voice.

Generative AI built for industry-specific accuracy

Many chat systems still rely on rigid rules. Sobot Generative AI moves past that with RAG+LLMs to generate fluent, context-aware answers that reflect your industry and brand. The result: fewer escalations, clearer replies, and measurable gains in first-contact resolution.

RAG + LLMs: how Sobot does it differently

• Intelligent chunking: Semantic and structure-aware splitting preserves meaning so retrieval captures the right intent and context-especially useful for policies, warranties, and knowledge articles.
• Precise retrieval: Automatic query rewriting clarifies customer intent, then applies a recall threshold, advanced retrieval, and re-ranking to surface the best content.
• Enhanced generation: Integrated with multiple LLMs (including Claude, OpenAI, Amazon Bedrock, DeepSeek and more) to reduce hallucination and keep tone, style, and length on brand.

If you want a primer on RAG, this overview is helpful: Retrieval-Augmented Generation (IBM).

SLMs for targeted tasks

Beyond general-purpose LLMs, Sobot uses small language models to handle recurring, domain-specific scenarios with precision. In retail and ecommerce, for example, SLMs focus on order tracking, product recommendations, returns, and refunds-speeding up resolutions without bloated reasoning steps.

"SLMs are designed for solving specific problems in various industries," said Yi Xu, CEO of Sobot. "Take retail and ecommerce industry as an example, our SLMs support businesses with common scenarios like order tracking, product recommendations, returns, refunds and more."

Multi-Faceted AI: built for every role in support

Generative AI lifts answer quality. Multi-Faceted AI makes the whole operation work better-for customers, agents, and admins.

AI Agent: fast, consistent, multilingual support

• Handles chat, voice, email, and social channels with human-like replies.
• Respects brand tone, answer style, and preferred sentence length.
• Supports multilingual experiences for global teams and customers.

AI Copilot: real-time help for human agents

• Conversation summaries, content polishing, and suggested replies.
• One-click ticket filling to reduce after-call work.
• Keeps agents focused on complex cases that need empathy and judgment.

AI Insight: data for better decisions

• Unified dashboard with reports, analytics, intelligent QA, and VOC analysis.
• Over 300 indicators to monitor performance and spot opportunities.
• Gives leaders a clear view to adjust routing, training, and content.

What this means for support leaders

• Higher containment: More issues resolved by AI Agent without handoff.
• Cleaner knowledge usage: Intelligent chunking and retrieval reduce "I can't find it" moments.
• Brand-safe responses: Tone controls and multi-model generation keep answers consistent.
• Stronger agent throughput: Copilot trims after-contact time and boosts accuracy.
• Operational clarity: Insight highlights what to fix-routing, macros, articles, or training.

Practical setup checklist

• Prepare knowledge: Consolidate policies, FAQs, and SOPs. Remove duplicates and mark source of truth.
• Define guardrails: Set tone, escalation rules, and compliance constraints before going live.
• Map top intents: Start with high-volume, high-impact scenarios (e.g., order status, billing, login issues).
• Pilot with feedback: Launch in one channel, collect ratings, and iterate on articles and prompts.
• Instrument everything: Track accuracy, containment, AHT, CSAT, and deflection-weekly and per intent.

Built for real applications-and real users

Sobot positions AI as a partner across the support journey, not a single feature. By combining Generative AI with Multi-Faceted AI, the platform connects knowledge, workflows, and roles into one system that drives measurable service outcomes.

"Sobot AI is not just the stacking of functions, but the deep integration with applications and users. That's where we stand out," said Xu.

Learn more at sobot.io. If you're leveling up team skills for AI-assisted support, see curated paths by role at Complete AI Training.