In the AI era, what enterprises must protect is customer sovereignty: data stays on their side, and models sit on a foundation they own. The judgments below are written directly into how we design products.
Customer sovereignty
Adopting AI means protecting two things—your data, and control over AI capability.
Data sovereignty. Data stays in domain: private deployment and trusted data spaces keep critical industry data on the customer side. We adapt to domestic CPUs including Hygon, Kunpeng, Phytium, and Loongson, and to Kylin and UnionTech UOS, with heterogeneous compute. Security covers SM-series encryption, data classification, and full audit trails for government, finance, and defense-grade requirements.
AI sovereignty. Database kernels and storage engines are fully in-house, with IP we control. From multi-model scheduling to AI-native apps, capability sits on our own foundation—not someone else’s platform. Domestic CPUs/GPUs and heterogeneous stacks keep customers from locking into a single ecosystem.
Our choice. A sovereign data-plus-AI foundation for critical industries: self-developed database kernels plus AI-native applications, fully domestic from data to apps—data in domain, models under control, ownership clear.
Industry trends
As large models land faster, the role of the enterprise data foundation is changing. It is no longer only a tool for “storage and analytics”; it is becoming AI data infrastructure—unifying the data shapes that training, RAG, and inference depend on.
The shift has a clear market backdrop. Per iResearch, China’s database market is projected at RMB 60.9 billion in 2026, with a CAGR of 17.04%. Multimodal fusion, AI-native design, and domestic stacks are the durable directions.
Meanwhile, localization has become a hard requirement in government and enterprise procurement. Xinchuang substitution and data sovereignty are no longer nice-to-haves—they are entry conditions. Full-stack sovereignty is the ticket into critical industries.
The opportunities we see share the same profile: AI research, enterprise knowledge bases, institutional trading automation, traffic supervision, industrial intelligence, and macro-cycle AI. They are data-sensitive, compliance-heavy, and deeply integrated with business systems—exactly the scenes that need a sovereign full stack.
Technical route
A collage of specialized databases has a ceiling.
Stitching multiple specialty databases and extending them with plugins hits limits in performance and consistency—cross-modal queries move data repeatedly, transactional consistency is hard, and operations cost grows with every extra engine. We believe the next form is one logical kernel, multiple physical engines, unified SQL, and cross-modal joins: one kernel for relational, vector, time-series, document, and graph data; one SQL for cross-modal queries; cross-modal ACID.
That is the design starting point for Antelope Database. The further implication is that AI capability should sink into the data layer wherever possible: vector search pushed into the kernel, in-database RAG, LLM inference cache—so data does not have to be moved just to be used by AI.
Engineering creed
This principle was forged in core financial workflows. We believe it applies to every correctness-sensitive scenario.
Large models excel at understanding and planning; they should not own the correctness of the final decision. In the AI trading-instruction module we enforced a clear split: the LLM cognition layer parses intent, collects missing parameters across turns, resolves ambiguity, and drafts instructions—never connecting directly to order placement. A deterministic layer hard-checks permissions, funds, quotes, and compliance, then splits and executes; the counter interface is the only execution path. Customer replies are templated, structured, and validated—not free-form model text.
The extension is the governable agent: humans in the loop, full traces, auditability. AI provides insight and advice; critical actions land only after human confirmation. That is not a conservative limit on AI—it is the precondition for allowing AI into production in critical industries.
Delivery principle
Our stance on quality is firm: acceptance is based on measured business results.
Model quality is re-evaluated on real business data and tuned against golden test sets. Product delivery is accepted on outcomes in the customer’s scenarios—not lab metrics or demo polish. “Determinism first” is our delivery rule: we would rather draw a clear capability boundary than over-promise beyond it.
The same principle applies to data: customer data is never shared. We do not use one customer’s data to serve another.
Long-term moat
We judge that the long-term moat is not any single point of “doing analytics” or “doing models,” but connecting all three into a loop.
The chain starts with data—a self-developed database kernel that is commercial, reliable, and Xinchuang-ready. Knowledge sits in the middle—a vertical LLM platform for scheduling, tuning, and knowledge extraction. Action is the AI-native application layer across industries. Results feed back into the data system, forming a self-iterating loop of sense, analyze, decide, and act.
That structure compounds: industry data keeps improving models; agents that are governable and auditable can enter core workflows; a fully domestic stack meets compliance and substitution requirements; and measurable ROI shows up in financial risk and compliance, industrial predictive maintenance, and intelligent traffic dispatch.