Wallboard 2.1 brings a giant leap forward with AI-assisted data sources. These come in two flavours:
AI Datasource: Casts a wide net and finds a ton of sources via a web search. It then goes and analyzes those results to get its final output (along with the parameters you specified). You can prompt for the information you want, and the AI will try to find the relevant information.
AI Webscraper: Scans a specific webpage and scrapes it for the parameters you defined. Unlike the AI datasource, this is much more accurate if you have a specific source you want to get information from. Additionally, this can scrape images which the AI datasource cannot.
Content is the hardest part of digital signage. Not because designing it is difficult, but because maintaining its freshness is an ongoing task. You update the menu on Monday. By Thursday, some items have changed. You extract restaurant hours from a website. The website changes its layout. You collect a news feed. It's outdated by the afternoon.
Wallboard 2.1's AI-powered datasources handle this. You specify the content you require. The system retrieves it, updates it, and keeps it current—automatically.
Here are some useful prompts you can run today. Each one saves time and keeps your screens up to date without manual intervention.
Local Events Feed
The Prompt
Create a datasource that pulls upcoming local events in our area
(3-mile radius from our office). Show event name, date, time, and
brief description. Update every day or week hours. Skip corporate events,
focus on community, arts, and entertainment.
What Gets Automated
- Web scraping runs every 6 hours across local event websites
- AI filters out corporate and B2B events, retains community-focused content
- Manages variations such as "tonight at 8pm" versus "April 15, 2026 20:00"
- Deduplication ensures that if the same event appears on multiple sites, it is shown only once.
Time Saved Per Week
- 3 to 4 hours of manual event research
- Eliminates outdated events showing on screens
- Content updated while you sleep.
Typical Setup
10 minutes. Content is live within 15 minutes.
A corporate campus cafeteria aimed to promote local events to staff. Previously, an employee spent 2 hours each week searching for and entering events. Now, the data source retrieves them automatically. Staff see 6 to 8 new local events every week, and the team has those 2 hours free.
Restaurant Menu Updates
The Prompt
Pull the lunch menu from [restaurant website], extract items,
prices, and descriptions. Update daily at 10:30am. If the website
structure changes, adapt to find the menu data anyway. Flag any
price changes greater than 15% since yesterday.
What Gets Automated
- Daily web scraping of restaurant menus
- Extracts prices and compares them, sending alerts for major changes
- Adaptive parsing ensures the AI finds the menu even if the restaurant redesigns their site
- Automatic formatting for display.
Time Saved Per Week
- 45 minutes of menu transcription
- Eliminates stale pricing on display
- Alerts to significant price changes useful for budgeting and contracts.
Typical Setup
5 minutes. Data is live within 20 minutes.
A corporate dining partner had three restaurant menus displayed on eight screens across two buildings. An administrator spent 2 hours per week manually transcribing menu changes. With this data source, menus update automatically. When one restaurant changed all appetizer prices by £2, the team was immediately alerted and could communicate the change internally. The setup saved 104 hours of manual work each year.
News Aggregation by Topic
The Prompt
Pull news headlines about [industry] from 5 major news sources.
Show only articles published in the last 8 hours.
Extract headline, source, publication time, and 1-2 sentence summary.
that Rank by relevance to these keywords
[keyword1], [keyword2], [keyword3].
What Gets Automated
- Multi-source news scraping
- Keyword-based ranking displays most relevant first
- Time-based filtering shows only fresh news
- Duplication handling ensures the same story from multiple sources appears only once.
Time Saved Per Week
- 4 to 5 hours of manual news research
- Ensures news is relevant to your audience
- Updates throughout the day without intervention.
Typical Setup
15 minutes. News begins updating every 2 hours.
A financial services office displayed news on lobby screens. Staff spent 30 minutes each day curating finance and market updates. With this datasource, headlines automatically refresh with the latest financial news, ranked by relevance to their main markets. Clients reported that the news display increased credibility, demonstrating they stay informed about markets. The office expanded the news coverage to four additional screens.
Job Board and Careers Feed
The Prompt
Pull open job listing for the City of McKinney, Texas. Show job title,
department, location, posting date, and link to job posting.
Only show job listing that were posted this week.
What Gets Automated
- Scraping job postings from multiple sources
- Automatic status tracking to distinguish new, open, and closed jobs
- Visual highlighting for fresh postings
- Removal of duplicate listings for the same job.
Time Saved Per Week
- 2 to 3 hours of manual job listing updates
- Ensures screens show current openings
- Recruiter visibility is great for career fairs or office lobbies
Typical Setup
15 minutes. Job updates run on schedule.
A 400-person tech company displayed current job openings on lobby screens during campus recruitment. Previously, recruiters manually updated the display whenever new roles became available. With this data source, openings automatically appear and close. During a busy recruitment period, they added 8 new roles in a single week. The automated display ensured candidates saw up-to-date openings without recruiter intervention.
Real Estate Listings
The Prompt
Pull available residential realtor listings for
a specific zipcode in City X.
Find and display the price, address, square footage,
and link to the listing/source.
Focus on listing less then 30 days old.
What Gets Automated
- MLS and real estate scraping
- Filtering properties by criteria
- Extracting and optimising photos
- Ranking based on age with newer listings highlighted.
Time Saved Per Week
- 3 to 4 hours of manual property entry
- Ensures clients see current listings
- Highlights new properties automatically.
Typical Setup
20 minutes for API connection setup. Data updates daily.
A commercial real estate brokerage had three lobby screens displaying available properties. An agent spent two to three hours weekly updating listings manually. With this data source, the display automatically refreshes each day, highlights new properties, and removes sold listings. Client foot traffic increased because the displays always showed up-to-date inventory. The brokerage added two more screens after observing the impact.
Sports Scores and Standings
The Prompt
Pull results for recent Dallas Mavericks games.
Show results within the last 14 days.
What Gets Automated
- Sports data scraping
- Local team highlighting.
Time Saved Per Week
- 2 hours of manual score entry
- Always shows current data
- Keeps employees engaged, since who doesn't check sports scores?
Typical Setup
10 minutes. Data updates automatically.
A corporate office displayed sports scores on break room screens. Break room traffic increased by 35%. An unexpected side effect was improved morale and conversations around the local sports team's performance.
Daily Specials and Promotions
The Prompt
Pull specials from [restaurant/retailer website].
Extract item, discount or price, and any relevant restrictions.
Ask for a discounts field if an item is on sale and filter those results
towards the top of the list.
Remove items when promotion ends or reaches daily limit.
What Gets Automated
- Daily promotion scraping
- Sorting discounts with the biggest deals first
- Filtering by time to show specials only on valid dates
- Tracking inventory to remove sold-out items.
Time Saved Per Week
- 3 to 4 hours of manual special entry
- Ensures current promotions are displayed
- Highlights best deals.
Typical Setup
10 minutes. Specials update daily at 5 am.
A quick-service restaurant had four screens displaying daily specials, happy hour offers, and limited-time promotions. A staff member spent 20 minutes each morning updating them. With this data source, specials are automatically pulled from the POS system and appear on the screens. Larger deals automatically received prime positioning. When they introduced a flash-limited special with 100 units, the system automatically tracked inventory and removed the special from the screens when stock ran out. No manual removal was necessary.
Getting the Most From These Datasources
A few practices ensure successful automation:
Start with one — Choose the datasource that saves the most time or would significantly improve your content. Get comfortable with it, then add another.
Set refresh schedules carefully — News requires hourly updates. Job boards can wait 12 hours. Event listings every 6 hours. Consider what makes sense for your specific content.
Use filtering rules — The AI scraper can filter by engagement, date, category, or custom logic. Use these to keep content relevant.
Handle edge cases — When a restaurant temporarily closes, the datasource should recognise it and stop pulling their menu. Build these conditions into your setup.
Setup and Rollout
All of these data sources are available in Wallboard 2.1.
Ready to automate? Set up your first datasource using Wallboard's AI scraper. Choose one that suits your content needs.
The true value lies in freshness: screens displaying current information, content that reflects reality in real time, and an audience that trusts what they see because it's consistently accurate and up-to-date.
Which data source would you automate first?
Want to learn more? Join our upcoming webinar. Details coming shortly
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