Unique Research · Insight / Unique Friend profile · May 5, 2023 · Guest company: Moxiang Tech (摸象科技)
Inventory type remains insight (Unique Friend / 非凡伙伴 series — not partner welcome). Seeking Series B; Unique Capital as financial advisor. Originally via 36Kr.
Context
AI 1.0 NLP wave → translation/CS/marketing. Today LLM breakthrough reframes survivors: not “find a scene” but “do we rebuild with LLMs in the scene we already own?” Peers (e.g. Zhuiyi, Emotibot) already moved to BERT post-2018; ChatGPT wave is the next test.
Moxiang founded 2007; from 2016 pivoted big-data precision marketing → “AI+finance” digital employees. Product: conversational Xiangxiaomi AI-employee platform + model-training for bank marketing/ops — SaaS annual or private deploy.
Tech
2020: introduced Google BERT atop prior deep-learning + domain KB — fine-tune on finance data, compress structure, raise concurrency → banking vertical models; per-bank / per-scene proprietary models with regular refresh. CEO Gao Peng: >150 pretrained bank-business vertical models; billions of real dialog turns/year. Why BERT: classic KB bots good on specialist Qs but weak on general intent/common-sense — hard to fix with KG alone.
Product & economics
Xiangxiaomi integrates into bank workflows — web / WeCom / phone. Helps RMs filter cohorts, proactively pitch fitting products, hand off warm leads; auto-logs RM–customer talks into memos/reports. After 2–3 days scene training: NLP recognition >95%; ROI vs pure human marketing 5–10×. Pricing: per AI-employee seat / year; exploring outcome bonuses. Clients: CCB, ICBC, BOC, PSBC, Industrial, SPDB + dozens SOE/joint-stock banks. Profitable; repurchase 90–95%.
TAM: ~6 majors × ~40 provincial branches + 12 joint-stocks × ~40 + ~1,500 city/rural banks ≈ ~2,200 addressable entities. Goal by 2025: +100 banks/yr and 20,000 AI employees (now ~8,000 at branches; >10,000 by end-Q2’23).
Private deploy mandatory under finance data rules — limits some proprietary data for continuous vertical iteration; per-client private model ops raise cost. Gao: banks still want desensitized corpus training; Moxiang built one-click upgrade so bank IT can self-ops. Evaluating open LMs for private deploy vs closed general models.
Team
Gao Peng PhD — student of ZJU academician Pan Yunhe; creator of China’s first mobile BOSS billing model; director/chief scientist of ZJU–Moxiang Data Intelligence Joint Lab (with ZJU AI Institute + data analytics center, 2020). Core from ZJU AI, Alibaba, ZTE. 17 AI patents; 78 software copyrights.
1 CN image; attach on ingest.
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