u/Gaya031

Hey everyone,

I’ve been talking to a few mid-market e-commerce and B2B SaaS founders recently, and I noticed a huge, recurring operational nightmare.

Every company has Standard Operating Procedures (SOPs). They live statically in Notion or PDFs. But in reality, nobody actually follows them perfectly because edge cases happen constantly.

For example: A package is delayed by 4 days. The static SOP says "No refunds after shipping." But last week on Slack, a manager approved a $20 voucher for a VIP customer facing the exact same issue. The support agents have to constantly dig through old Slack threads, pins, and emails just to answer: "How did we handle this exception last time?"

Standard AI chatbots/RAG fail here completely. They just read the static text, but they can't map out the non-linear human decision-making history or institutional memory.

I’m currently validating an idea called SOP-Flow AI to solve this.

How it works under the hood: Instead of just reading a static handbook, it ingests your documentation plus your live dynamic channels (like Slack approval channels). It cleans out the noise (memes, bot alerts) and builds a live "decision graph."

When a ticket comes in:

1. It runs a stateful multi-agent workflow in the background.
2. It cross-references the strict policy with historical manager approvals.
3. It drafts the exact programmatic action schema (e.g., issue $20 voucher, update address).
4. Human-in-the-loop: If the AI's confidence score is low (less than 85%), it doesn't auto-send. It surfaces the drafted action to a human agent on a clean dashboard to approve/reject with one click.

We are backing this with a robust engineering stack (Express + BullMQ for handling high-volume webhook spikes safely, and single PostgreSQL with Row-Level Security so no tenant data ever leaks).

My questions for the community:

1. For founders/ops managers: Does your team actually waste time hunting down "how did we handle this last time" exceptions, or is this a solved problem for you?
2. If an AI could auto-draft these complex resolutions with 100% data isolation and a human-in-the-loop safety net, would you trust it in your workflow?
3. What integrations would be absolute dealbreakers for you? (Shopify, Intercom, Slack, Zendesk?)

I'm not going to write a single line of production code until I validate whether this is a real burning pain point or just a "nice-to-have" feature. Would love some brutal, honest feedback. Thanks!

https://preview.redd.it/qmnr6ak38k4h1.png?width=896&format=png&auto=webp&s=8324f39cc2912ab684c6fdd530b299789322a5ab

Hey everyone,

I got tired of spending 20 minutes scrolling through walls of text on food delivery apps just trying to decide what to eat based on stale photos. At the same time, I noticed how local food reviewers on Instagram drive massive traffic to small cafes, but they rarely get a fair cut of the sales.

So, I’m building BiteSized—a mobile-first modular monolith platform that merges a TikTok-style short video feed with instant hyperlocal food pre-ordering.

How the App Works:

1. The Feed: You open the app and see a vertical video feed of 15-second reels showing dishes cooked by restaurants or reviewed by local foodies within a strict 3–5 km radius.
2. The Left-Swipe: If you crave what’s on screen, you left-swipe the video. It skips the generic restaurant page and lands you directly on that specific dish page.
3. The Flash Deal: Restaurants can activate real-time, 15-minute countdown flash deals (e.g., a ₹399 pizza drops to ₹249 for 15 minutes) to clear inventory during slow hours.
4. Zero-Logistics Fulfillment: No delivery drivers. It supports Self-Pickup (Takeaway) to save time or Dine-In pre-orders with an upfront holding fee to prevent no-shows.
5. The Creator Loop: If someone buys a dish via a blogger's video, the creator automatically gets a 5% affiliate commission credited to their wallet in a single atomic database transaction.

The Tech & Architecture:

• Frontend: Mobile Native Views using optimized virtualized lists to mount/unmount off-screen video streaming to prevent memory leaks and maintain 60fps scrolling.
• Backend: Node.js Express modular monolith.
• Database & Caching: PostgreSQL + PostGIS. I'm using a GEOGRAPHY(Point, 4326) column with a GIST index to run highly optimized ST_DWithin proximity queries to instantly filter nearby restaurant rows. I'm grouping users into 1 km neighborhood coordinate grids and caching the feed strings in Redis with a 5-minute TTL to protect the database from getting crushed.
• The Performance Strategy: To protect the database from redundant calculations on minor movements, I'm grouping users into 1 km neighborhood coordinate grids by truncating their live lat/long data to 2 decimal places. This grid ID acts as a Redis lookup key with a 5-minute TTL to serve feeds in <2ms.
• Security: A secure QR code handshake at the physical shop counter to close the transaction loop and prevent restaurants from bypassing platform commissions.

I’ve attached screenshots of the working UI prototypes.

I want your brutal honesty:

1. As a consumer, would you use an app like this to skip queues, watch food reels, and grab instant takeaway/dine-in discounts nearby?
2. To the engineers here: Is the neighborhood coordinate rounding strategy on Redis efficient enough for an early-stage MVP scale, or should I change the caching logic?