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An AI agent answers questions from real store data. A check layer blocks numbers that AI makes up.
For that check layer, I'd include a test where the answer uses a real store number with the wrong metric or date range. How do you tie each numerical claim back to the source result and its filters?
Good question, this is the case I care about most. Today the protection is mostly in the design. The model does not calculate numbers, SQL does. Each number comes to the model with its context: metric, period, the base for percentages, and attribution type. The model cannot...
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FirstReply — reply first, win the booking
Description
Event venues lose bookings because they reply a day late. By then the client has booked somewhere else. FirstReply answers every inquiry with a priced proposal and a held date in seconds.
The automation
1. An inquiry arrives from the venue page, or staff paste in an email.
2. AI extracts event type, date, guests, budget and requests, each with a confidence level and the exact words it came from.
3. Missing date or guest count? It writes a clarifying reply instead.
4. It matches the smallest free space that fits (or the two nearest free dates when it's booked), prices the right package and holds the date for 5 days.
5. The client gets their own proposal page and books with one tap. The hold becomes a confirmed booking.
6. Unsure details, over-budget totals or Review mode send it to a review queue first. Humans step in only when needed.
7. Views, acceptances and expiring holds update the pipeline and alert the team live. Every step is logged to the second.
The design A full design system first (tokens, type scale, components), built as a clickable prototype, then rebuilt in Base44 from prompts. Client pages are mobile-first; the venue side is desktop-first.
Try it Client side: firstreply.base44.app · Venue side: firstreply.base44.app/dashboard
Different products need different shopping experiences.
When exploring Shopify store design, I like thinking about how the brand, product category, and customer journey should influence the final layout.
For example:
→ A home decor store needs strong visual presentation and an editorial feel.
→ A coffee equipment store needs clear categories and product information that helps customers compare options.
→ A fitness store needs organized collections and a straightforward path to the right equipment.
The goal isn't to use the same design for every business.
It's to understand what each store needs and build an experience around it.
These concepts explore three different directions, but they share one principle:
Good e-commerce design should make products easier to understand and easier to shop.
Which of these three store styles would you be most interested in exploring?
Great point. I’d pick the home decor one, editorial layouts really sell the vibe. 🔥
From a packing-slip PDF to a shipment your team can track.
This TechFoundry IMS demo matches boxes, parts, and weights from the vendor document and connects courier tracking to the shipment record. A practical example of the AI and automation workflows we build into business software.
Watch the vendor shipment demo:
https://www.youtube.com/watch?v=FmjdRRfUZh8
#AI #Automation #SoftwareDevelopment #SupplyChain
Small automations like these can save teams so much manual work and reduce errors.