Enterprise AI keeps stopping at the demo.
We take it to production.
MoMu Intelligence is an embedded AI engineering team. We diagnose, customize, train, and stay on the ground until the system runs — and your team can own it.
AI doesn’t lack tools. It lacks the last mile.
Companies don’t lack AI news or tool lists. What stops them are three more concrete questions: where should this start? Who signs off on the result? And why, once the system is live, does the business still not use it?
We have seen too many projects stop at the demo: impressive on stage, fragile outside it. Data, permissions, system integration, organizational adoption — if no one owns these four, AI stays in the slide deck.
MoMu Intelligence takes that stretch: from the first diagnosis to the system becoming part of daily work.
A demo proves possibility. Production creates value.
Figures from a July 2026 comparison across five real manuscripts and an internal engine experiment. They apply to those samples only, not to every project.
Diagnose · Customize · Train · Embed
Four problems, four moves. Use one, or go from diagnosis all the way to launch.
Diagnose
See clearly where AI fits — and what to do first.
Customize
Every company gets stuck differently. The solution shouldn’t be generic.
Train
Two days of hands-on work, applied to the job.
Embed
Launch is the starting line. Adoption is the finish line.
We don’t sell slide decks, and we don’t leave after handover. Every stage has an acceptance criterion.
We don’t pass off demo videos as results.
These are projects we can talk about publicly. Every number carries its source and its boundary. If we can’t stand behind it, we don’t publish it.
AI Proofreading
A publishing-grade AI proofreading engine, live on web, Windows, and macOS.
Ad Ops Automation
Bulk campaign creation and creative processing, cutting repetitive work from hours to minutes.
Eight-stage AI Book Pipeline
From topic selection and research to writing, proofreading, and final manuscript — our own content production runs on it every day.
FDE: put the engineer in the room.
FDE stands for Forward Deployed Engineer. The term comes from Palantir; OpenAI and others use it now. The idea is simple: engineers don’t sit in an office waiting for requirements. They go into the business, watch how the work actually gets done, find the highest-leverage change, build it on the spot, and only scale it once it works.
Embed
Go on site. Watch the real workflow, the data, and the exceptions.
Build
Skip the slide deck. Build something that runs.
Prove
Use evaluation sets and acceptance criteria, not demo videos.
Scale
Leave the method, the code, and the docs behind. Your team takes over.
| The usual way | Ours |
|---|---|
| Sell you a tool and leave you to figure it out | Own the outcome: does it get used, save time, make money? |
| Teach a generic method and send you home to implement it | Implement inside your workflow and fix what breaks on the spot |
| Deliver and leave | Leave the method with your people so they can keep it running |
The code goes into your repository. We don’t lock you in.
Reviewable. Transferable. Portable.
An embedded delivery team, backed by a network of specialists.
Our core members come from Peking University, the Chinese Academy of Sciences, and leading model companies. Our advisors come from Tsinghua, CAS, Zhipu AI, ModelBest, and other institutions. We don’t stack names on a page; you meet the actual people during delivery.
Advisors join solution design and production quality reviews. Their involvement is scoped per project.
Use AI first. Prosper first.
If a workflow is slowing your business down, or an AI project is stuck at the demo, we can tell you in 15 minutes whether it’s worth doing — and how.