AICESP is a dual AI-native platform: our production line turns a conversation about the client's business and strategy into a complete, tested, deployable enterprise system — the requirements spec, architecture, code and documents are all AI-produced and quality-gated. And every system we deliver has AI agents built in, driving the customer's business processes from day one.
"AI that generates enterprise software" is only the face it monetizes today. Underneath is a domain-general, self-governing, self-evolving intelligent system — one that understands, designs, builds and keeps evolving long-horizon complex projects in any field.
An Einstein — a superintelligent agent (SGI) — can discover relativity; but a Manhattan Project needs Long-horizon Complex-project Intelligence (LCI). For large institutions, enterprises and programs, LCI grounds grand intent layer by layer: vision & goals → strategy & conception → processes & steps (what → how → what) — each layer carried into the next, every step executable, verifiable, under control.
Self-governing: every stage exit has deterministic quality gates — substandard output is honestly blocked, never passed downstream; exceptions escalate with a graded, auditable trail, and humans handle only what truly matters. Self-evolving: the defects and gaps each run exposes flow back into the line itself — new constraints, new gates, new knowledge — so the next run cannot repeat them. The more it runs, the stronger it gets.
The engine's architecture is domain-independent: the LCI capability system instantiates to any long-horizon complex project. Enterprise software engineering — req→design→develop→optimize — is the first instance: compilers and tests make right and wrong instantly checkable; verified end to end, re-runnable live. Strategy, science and mega-engineering are next.
Here is the moat: opening a new field hinges on being able to tell right from wrong in its output. Software has ready-made means (compilers, tests); most fields don't — and this system's distinctive ability is building a verification regime for any field (multi-model cross-checks, deterministic checks, simulation, human sign-off where it matters). So the market isn't capped at software engineering; it's "autonomous long-horizon projects in any field." This is also what separates us from the FDE path — "strong people make strong output": the system evolves itself.
Everything below is live on the public site today — real systems built by the production line, running real code with demo data. No slideware.
Government, Enterprise, Retail, Logistics live online; Manufacturing (our industry flagship) ships today. Every one passed a three-gate quality process with runtime-verified auth on every protected endpoint.
Business conversation → delivered system: 30 min requirements + 48 min design + 3 h code. For a listed-company LTC platform, after gathering business needs with the client, requirements/design/development ran from 20:02 and the system shipped 03:57 — overnight, 510 files, 43 pages, all acceptance passed.
Government edition: 43/43 acceptance, 149 secured endpoints, zero auth bypass. Enterprise edition v2.0: 26 business scenarios, 31/31 acceptance, 51 frontend pages, 100% design-coverage.
The production line that builds customer systems is itself under permanent test. Run them yourself in due diligence — we'll hand you the terminal.
✓ Model-agnostic — verified across DeepSeek, Kimi, GLM ✓ On-premise deployable, data never leaves the customer ✓ Free standard editions download-gated → qualified leads ✓ Deployment service at >99% gross margin
Speed can be copied. Acceleration is hard. Jerk is nearly impossible. These four numbers are re-measured every week, fully auditable from our git history and production-line certificates. Week 28 (Jul 7–8, in progress) actuals:
Methodology: L0 product family → L1 platform → L2 self-evolution layer → L3 agent capability system → L4 human (ideas & exceptions only). The higher the layer, the harder to copy.
First-generation China SaaS died on human service costs — more customers, more losses. Our marginal delivery cost is compute. More customers → thicker constraint library → lower cost per system.
DingTalk / WeCom / Feishu sell generic collaboration tools. We deliver each customer's own business system — AI-native, private-deployable, compliance-ready. Their cost structure cannot generate a bespoke system per customer. Ours is built for exactly that.
Hard gates, generation constraints, generability design and honest BLOCKED certificates guarantee output quality regardless of which LLM does the work — verified across three model vendors with negligible variance. This is our leverage in model-vendor negotiations, and the answer to "can AI code be trusted?"
Marketing site, investor materials, mail & web sales assistants, trademark filings, this very page — produced and operated by AI with one human providing direction and exceptions. We are our own first customer, in production, today.
Most enterprises already run systems they will never rip out. Our third delivery form — an AI enablement sidecar generated against their open interfaces, their code untouched, write-back authority opened level by level — turns the entire installed base into addressable market. Lowest sales resistance; the AI knowledge flywheel accrues on our side. Core components field-proven (paying SOE incumbent-IT governance customer; enablement-point engine stable to ±2%); productization underway.
Single tranche, clean terms, no tricks.
≈9.1% offered. Post-money ¥66M.
We fight a three-month war with eighteen months of provisions — aggressive speed, conservative safety margin.
| T+1 month | Platform GA · free standard editions open · first paying customer |
|---|---|
| T+2 months | Five editions on sale · reseller network operating |
| T+3 months | All eight industry solutions live · Series A kickoff (target ¥30–50M, priced on audited operating data) |
Use of funds: compute & model capacity, platform hardening, go-to-market. Every later round is priced on operating data, not narrative.
Watch the production line regenerate a full system live. Run the 1,100+ platform tests yourself. Click through every delivered system online. Then let's talk price.
Contact invest@aicesp.cn Download the deck (EN) 中文版
Our AI investor-relations assistant answers this inbox in minutes, any hour. Material matters reach the founder same-day.