---
title: "Unique Viewpoint | AI-Native Databases and RAG Unlock AI Infra’s Path from B2B to Consumers"
author: "Unique Research"
sourcePublication: "Unique Research (FFCAP English library)"
originalPublishedAt: "2024-03-26T16:30:00+08:00"
canonical: "https://ffcap.cn/en/research/20240326a06r0r00"
source: "https://ffcap.cn/en/research/20240326a06r0r00"
language: "en"
cnSourceUrl: "https://view.inews.qq.com/a/20240326A06R0R00"
---

# Unique Viewpoint | AI-Native Databases and RAG Unlock AI Infra’s Path from B2B to Consumers

_Unique Research · Unique Viewpoint · March 26, 2024_

## Background

On **January 24, 2024**, Unique Capital and partners held the **2024 AIGC Application Development Summit** in Shanghai, with nearly **1,000** AI practitioners attending. This piece is edited from the guest roundtable segment.

## Guests

-   **Lu Yilei** — Founder & CEO, Baihai Tech (白海科技)
-   **Zhang Yingfeng** — Founder, Infinity (英飞流)
-   **Qin Xuye** — CEO, FutureSpeed (未来速度)
-   **Lin Qunshu** — Founder & CEO, Integer Intelligence (整数智能)
-   Host: **Li Jiarui** — Founder, Juzi.bot (句子互动)

## Q: Looking at the LLM tech stack over the past year, what stood out most?

**Lin Qunshu (Integer):** Before Transformers, datasets were smaller and hand labeling was enough. With large text and multimodal models, hand labeling collapses. Teams moved to automation — iterating algorithms on previously labeled data — and into domains like intelligent driving (AD + cabin) that need huge high-quality datasets. Synthetic data is rising where real collection is hard (e.g., AV). Enterprises that once skipped data-engineering platforms are now buying automated labeling, cleaning, and review.

**Qin Xuye (FutureSpeed):** ChatGPT may be strongest overall, but enterprise adoption hinges on (1) security — finance clients often cannot use certain APIs; (2) scenario fit, not absolute SOTA; (3) cost — if a smaller fine-tuned model works, don’t upsize. Our platform treats models as interchangeable with one-click switch.

**Zhang Yingfeng (Infinity):** Open source pushed the whole stack — models, inference, middleware — toward cheaper enterprise readiness. Expectations for LLMs started high in early 2023 and have cooled as the stack industrializes from C toward B. On infra, SJTU’s open-source **PowerInfer** (ordinary GPUs for large-model inference) is rare domestic originality, even if not yet production-ready.

**Lu Yilei (Baihai):** Stable Diffusion (Oct 2022) was interesting; by spring 2023 LLMs overturned software architecture — from distributed per-scenario AI to foundation-model-centric integrated AI, raising compute and data bars. We burned ~**¥3M** compute building Chinese models on open bases with poor early results; now Chinese understanding in domestic models is strong — underscoring data. For domain models we insist: better **10k** high-quality samples than **1M** noisy ones.

## Q: What is Baihai’s distinctive edge?

**Lu:** Baihai is an AI train/infer cloud platform for fine-tuning and full-lifecycle traditional AI — with an open-sourced AI IDE in **Rust**, strong GPU scheduling, and usability so developers, analysts, even business users can build models. Auto + human eval for accuracy/relevance/consistency; strict review before feedback data reflows (to avoid GPT-4-style quality drift from unclean feedback). **2023** contract value reached tens of millions RMB; dozens of Fortune 500 / SOE clients. Positioning: middleware for the last mile of LLM apps — partner, don’t compete with foundation-model/BAT compute providers.

## Q: Infinity’s AI-native database and RAG — what’s new?

**Zhang:** RAG cannot be vector-DB-only. Strong RAG often comes from search companies (e.g., Baichuan/ex-Sogou; overseas open-source Vesper). Two requirements:

-   **Infra:** multi-path recall — vectors + search (precision) + structured query. The store must first be a real database (tables, ACL, accounts). Infinity’s “AI-native DB” thesis: one enterprise LLM digitization layer + one DB that holds all enterprise data for the model.
-   **Data:** end-to-end open-source RAG including prep / ETL-for-AI to organize data for better retrieval. Fit domains: finance, IT, industry, healthcare, law — knowledge-heavy internal corpora. Industrial buyers are cost-sensitive (tickets may be thousands–tens of thousands RMB vs finance at hundreds of thousands+).

AI-native DB / RAG for the first time unlocks infra’s path from **B2B toward consumers** — everyone may eventually have a personal RAG / cheap personal knowledge base (e.g., uploading meeting audio). That needs cheaper models _and_ cheaper DB infra.

## Q: FutureSpeed’s Xinference — tech edge and commercialization?

**Qin:** Open + commercial editions; GitHub open edition ~**2,000** stars. Solves model variance, sizes, load/throughput; OpenAI-compatible REST API; integrates LangChain, LlamaIndex, Dify.AI; engines Transformers / vLLM / GGML; multi-node distributed deploy; domestic + foreign GPUs including Mac GPU. Commercial edition targets enterprise performance, observability, HA — finance/government with less API dependence, and cost-sensitive verticals like customer service. Ecosystem partners (Infinity, Dify, etc.) matter: China buyers want integrated solutions, not point products.

## Q: Integer’s one-stop data management — core advantages?

**Lin:** Two words: **automation** and **compounding**. Founded from quant/NLP labeling pain — domestic vendors were either AI-naive or pure hand-label shops. Automate 80–90% of labeling; humans review. Algorithms must improve over time (MLOps in-platform) so PMs without deep algo skills can retrain pre-labelers — driving data cost down so previously blocked AI scenarios become viable.

## Q: How does Integer stay ahead in a crowded AI data market?

**Lin:** Intelligent driving is extreme: OEMs may process >**100M km**/year of camera + LiDAR. Tesla’s early hand labeling could take >**500 hours** per video clip; their closed-loop + 4D tooling lifted efficiency ~100× over ~3 years. In **2023** Integer shipped a **4D** labeling platform — fuse multi-frame via 3D reconstruction so one annotation covers the sequence. Vertical customization for OEMs; claimed lead vs competitors (tools ready Mar–Apr 2023 vs peers mid-2024). Transfer overseas AI data-engineering practice to cut Chinese AI companies’ data costs.

## Q: Where is the next tech explosion in the LLM stack?

-   **Lin:** **2024** watch synthetic data — beyond simple augment. Integer will launch an open data foundation with **¥10.24M** (2^10) to create frontier engineering datasets.
-   **Qin:** 2024 as the year of real landing — models, agents, and full-stack readiness crossing thresholds.
-   **Zhang:** Landing year; focus on **RAG** — if RAG fails, agents fail; bottleneck still foundation models, with expected breakthroughs this year.
-   **Lu:** 2023 = foundation-model year; 2024 = tooling year for startups (train/infer, agents, RAG platforms) — without tools, no fast landing.

_2 CN images; attach on ingest._

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Editorial English edition published on UniqueCapital: https://ffcap.cn/en/research/20240326a06r0r00
On-site reading page: https://ffcap.cn/en/research/20240326a06r0r00