This is a hypothetical business case by SasaDaily.
First, let’s be clear.
This is not a real company that has already implemented HomeAgent.
UGREEN HomeAgent is still in crowdfunding, with the HA100 expected to ship as early as late January 2027, and Taiwan is not yet included in the official delivery regions.
So today, the goal isn’t to prove:
“HomeAgent has saved a specific company a certain amount of money.”
Instead, we’re using the officially announced capabilities to imagine how a small home business might design its workflow if it adopts local AI in the future.
Hypothetical Scenario: A 3-Person Home Appliance Brand
Three people run a small lifestyle products brand from the first floor of a residence.
Daily tasks include:
- Receiving parcels from couriers and suppliers
- Packing and shipping orders
- Taking product photographs
- Organizing short video materials
- Handling returns and exchanges
- Checking conditions at the studio entrance
- Turning on/off studio lights and other equipment
The issue isn’t that the work is complex.
It’s that much time is wasted on:
“Finding what just happened.”
For example:
“What time did that box get delivered yesterday?”
“Where did the courier leave the package?”
“Where is the last photo of the white product’s front view stored?”
“Was that person at the door last night a courier or just passing by?”
Each query takes only a few minutes.
But looking back repeatedly every day gradually adds up.
Verified Capability: HomeAgent Can First Organize 'What Happened'
UGREEN’s publicly released HomeAgent and SynCare Camera features include:
Recognition of people, vehicles, pets, and packages.
Detection of lingering presence.
Creation of AI event summaries.
Keyword searches to find specific video segments.
This means the e-commerce brand wouldn’t need to watch surveillance footage from start to finish daily.
For example, employees can first ask in the morning:
“Which packages arrived yesterday evening?”
The AI searches local footage for those events.
Humans then only review the necessary clips.
Here, AI’s role is to:
Narrow down the search scope.
It does not decide, for instance, whether:
“The goods are complete.”
“Suppliers have officially delivered.”
“Who should be claimed from.”
That remains human responsibility.
SasaDaily Hypothetical Workflow 1: Local AI Handles Package Events First
Assuming this company averages:
15 times per week they need to verify delivery or entrance events.
This is a SasaDaily hypothetical figure.
Previously, each check involved:
Opening surveillance footage,
Finding the date,
Scrolling through the timeline,
Repeatedly reviewing footage.
Average time spent:
6 minutes.
Also a SasaDaily hypothetical figure.
If AI event summaries and keyword searches find candidate clips first, and then staff confirm:
Assumed reduced time:
2 minutes per check.
Still a SasaDaily hypothetical figure.
Saving 4 minutes per check, 15 checks a week approaches:
60 minutes saved weekly.
The real benefit isn’t “not having to watch cameras.”
It’s:
“Only watching what needs to be watched.”
SasaDaily Hypothetical Workflow 2: Storing Large Amounts of Product Photos Locally for Search
Small brands easily accumulate a large amount of media.
The same cup may have photos:
From the front.
From the side.
In packaging.
In lifestyle settings.
On a white background.
Short videos.
Older versions.
New versions.
Months later, the real time sink is often not taking photos, but:
“I know I took that before, but where is it?”
HomeAgent’s official aim is to keep photos, videos, and other media in a local library and allow natural language searches to find needed content.
The official demos mainly show family photo scenarios.
Applying this capability to “product media libraries” is a SasaDaily hypothetical workflow, not an officially announced enterprise client case by UGREEN.
Assuming this company has:
10 searches for product photos or videos weekly.
This is a SasaDaily hypothetical figure.
They used to spend around:
8 minutes per search.
With local AI narrowing down options first, time is estimated to drop to:
3 minutes.
Saving 5 minutes each time, 10 times a week results in:
50 minutes saved weekly.
Combined Savings Per Month?
Counting just these two modest tasks:
Package/event verification:
60 minutes/week.
Product media search:
50 minutes/week.
Total:
110 minutes/week.
Roughly:
110 × 4 weeks =
440 minutes per month,
or approximately:
7.3 hours per month.
Again, these are SasaDaily hypothetical numbers.
If this company estimates internal labor costs at:
NT$500 per hour,
Also a SasaDaily assumption.
Then 7.3 × NT$500 =
An estimated time value of about:
NT$3,650 per month.
However, this cannot simply be reported as:
“HomeAgent earns the company NT$3,650 a month.”
Because this does not account for:
Hardware costs,
Hard drives,
Cameras,
Installation,
Maintenance,
Learning curve,
Review of misjudgments,
Or how much work is truly suitable for automation.
Plus, the product isn’t officially shipped yet.
So this can only serve as:
A demonstration on how to estimate workflow value,
not a product ROI proof.
SasaDaily Hypothetical Workflow 3: Let Smart Devices Handle 'Environmental Actions' but Not 'Business Decisions'
HomeAgent can also connect to smart lights, temperature controls, door locks, curtains, and other devices.
This raises the obvious question:
If AI can control devices, should it manage the entire studio?
Not necessarily.
A safer approach is to divide tasks into two categories.
First category:
Low-risk environmental actions.
Examples include:
Turning on specified lights at start of work,
Shutting some equipment off at end of day,
Adjusting curtains,
Switching workspace scenes.
Second category:
Actions involving business responsibility.
Such as:
Allowing strangers entry,
Official confirmation of supplier delivery,
Refunds,
Order changes,
Official shipments,
Customer compensation promises.
These should not be automatically executed simply because AI “seems to know what happened.”
AI Seeing a Package Doesn’t Mean Order Acceptance Is Complete
This is the most important part of the whole workflow.
Cameras can confirm:
A package is at the door,
But cannot verify whether it contains:
The correct product,
The right quantity,
Is undamaged,
Or matches the purchase order.
So AI event detection only answers part of the question, like:
“Was it delivered?”
But actual business acceptance must ask:
“Did we receive what we actually ordered?”
These two things cannot be conflated.
Therefore, this company configures its workflow so that:
AI can search videos,
AI can organize events,
AI can search media,
AI can assist with low-risk environmental device control,
But:
Inspection is done by humans.
Refunds by humans.
Shipping by humans.
Customer commitments by humans.
The Greatest Business Value of Local AI May Not Be “Smarter AI”
The truly interesting part of this case is data location.
Door cameras might record many hours daily.
Product photos might accumulate tens of thousands.
If the goal is just to:
Search delivery events,
Summarize what happened yesterday,
Locate previously taken product media,
it is not always necessary to upload all raw data to Cloud AI first.
HomeAgent’s approach is to:
Keep storage and AI processing in the same local environment.
This way, most raw data stays on local devices.
Cloud AI is only engaged when more powerful AI functions are truly needed.
The biggest change for small companies may not be:
“AI answers are better.”
But rather:
“Instead of moving data to AI, AI moves closer to the data.”
But Local AI Must Not Become a New Blind Spot
Just because data is stored on-premises doesn’t mean management isn’t required.
The company still needs to handle:
Who can view the cameras,
Who can access product media,
Who can log in remotely,
How hard drives are backed up,
Account protection,
System updates frequency,
Removing access rights from departed staff.
More importantly, UGREEN explicitly reminds:
The camera’s monitoring, detection, and care functions are just aids.
They cannot replace human oversight.
Legal compliance and others’ privacy rights must also be observed.
So if this company installs cameras,
it does not mean “AI running locally can film anything anytime.”
Local AI solves data processing methods,
Not cancellation of privacy responsibilities.
How Should a 3-Person Small Company Really Start?
Not by buying a bunch of AI hardware immediately.
Start with one task that really wastes time now, for example:
Someone checking surveillance footage daily,
Someone searching for old photos daily,
Someone repeatedly toggling between apps to check the same space.
Then answer three questions:
How many times per month does this task occur?
How many minutes does each instance currently take?
After AI supports it, what still needs human confirmation?
With these three numbers, decide if the equipment is worth purchasing.
This approach is more practical and closer to real business adoption than ordering based solely on “26 TOPS Local AI” specs.
It’s Not Yet a Success Story
As of September 8, 2026:
HomeAgent is still in crowdfunding.
Kickstarter is expected to launch on October 27.
The HA100’s earliest shipping date is late January 2027.
Officially announced delivery areas in Asia Pacific include Australia and Singapore but exclude Taiwan.
Therefore, the reasonable action for SasaDaily now is to:
Study workflows.
Once the hardware ships, then evaluate:
Search accuracy,
Event detection reliability,
Local AI speed,
Third-party device integration stability,
Actual time saved.
Only then can we discuss:
Real ROI.
Small Companies Don’t Really Need a “Fully Automated Home”
Summarizing today’s case into one workflow:
Local data → AI finds events/media → Human confirms → Business actions.
HomeAgent doesn’t have to run the entire store for this small company.
It only needs to shorten tasks that are:
Repeatedly searched,
Repeatedly browsed,
And repeatedly confirmed,
With data already onsite.
This already holds business value.
The final steps involving money, customers, and responsibility:
Still entrusted to humans.
This way, Local AI isn’t just another chatbot box in the home.
It begins to become:
A foundational layer of operational infrastructure for small physical businesses.
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