Diagnose
One day on site. We map your data, your workflows and your margins, then tell you which three AI opportunities are worth money — and which ten are not.
AI that ships.
We build working AI systems for manufacturers and multi-store retailers. Not pilots. Not demos. Systems that run every day and show up in the P&L.
- Delivery
- 3–8 weeks
- Engagements
- Diagnosis → build → operate
- Measured in
- Money, not accuracy
- Team
- Senior only, no handoffs
01 — What we do
Three ways in. One direction: production.
Build
Knowledge bases, support agents, store analytics, internal tools. Shipped in weeks, owned by you, running on your data.
Operate
An annual retainer that keeps the system alive: monitoring, tuning, new use cases. The part most vendors disappear for.
02 — Work
Four systems in production.
Names withheld under NDA. Numbers are real.
Retail · Multi-store
Store margin dashboard
A 40-store auto-service chain ran on spreadsheets. We built an agent that reads POS, inventory and membership data and answers questions in plain language.
- Manual reporting
- −92%
- Time to insight
- 3 days → 30s
Healthcare · Devices
Post-treatment follow-up agent
Follow-up calls were manual and inconsistent. We built a compliant agent that runs structured check-ins, flags deterioration, and hands off to a nurse.
- Follow-up completion
- 41% → 94%
- Nurse time saved
- 18 h / week
Brand · Content
Content pipeline
A DTC brand published on five platforms with one person. We built a pipeline that collects sources, drafts variants per channel, and queues them for human approval.
- Output
- 1 → 5 posts / day
- Review time
- 4 h → 40 min
Education · Consumer
Vocabulary companion
A PWA that turns rote memorisation into short daily loops with spaced repetition and a tutor agent that adapts to mistakes.
- Day-30 retention
- 2.1× baseline
- Install to first lesson
- 38 seconds
03 — How we work
Four steps. No surprises.
-
01
Diagnose on site
One day. We interview the people who do the work, pull the data, and write down what is actually broken.
-
02
Agree on the number
Every proposal names the metric it will move and the baseline. If we cannot name it, we do not take the project.
-
03
Ship in weeks
Working software in production, not a slide deck. Weekly demos on your data, with your team in the room.
-
04
Stay for the boring part
Monitoring, retraining, new use cases. Most AI systems die at month four. Ours are on a retainer because that is when the work starts.
04 — What we believe
Five positions we do not negotiate.
Value over volume. One system in production beats ten pilots in a deck.
The model is not the moat. Your data, your process, your people are. We build there.
Say no early. If AI is the wrong tool, we say so on day one and refund the diagnosis.
Hand over everything. Source code, prompts, documentation, and a trained owner.
Small and senior. No account managers, no offshore handoff. You talk to the person building it.
05 — Contact
Tell us what is
Tell us what is
actually broken.
One paragraph is enough. We reply within one working day, and we will tell you honestly whether this is a project worth doing.
Email
hello@motus-ai.pages.dev
Founder
Wang Yu
Based in
Chengdu, Sichuan, China (GMT+8)
Working with
Manufacturing · Retail · Healthcare · Education
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