Spot underpriced properties automatically and get alerted

Scans new property listings every day and flags the ones priced below market value so you can act fast.

How the work actually flows

It branches. Every path runs; runs once per each property listing.

Pattern: Parallel Split (2) ยท Multiple Instances without Synchronization (12)

flowchart TD trig(["daily listing scan runs"]):::trigtime s0[["pull new listings and sales data"]]:::mi s1["compare price to recent sales"]:::svc s2["check broader market trends"]:::svc s3["flag underpriced properties"]:::task trig --> s0 s0 --> s1 s1 --> s2 s2 --> s3 gx{"+ where to send the flag"}:::gate s3 --> gx p00["send email alert"]:::task gx -->|"email"| p00 p10["post slack alert"]:::task gx -->|"slack"| p10 p00 --> out p10 --> out out[/"underpriced properties flagged daily"/]:::out pay{{"acts fast on real deals"}}:::pay out --> pay classDef task fill:#e7f6fe,stroke:#34b8f0,color:#2c2a29 classDef svc fill:#f6f8fa,stroke:#7c8795,color:#2c2a29 classDef mi fill:#e7f6fe,stroke:#0079a8,color:#2c2a29,stroke-width:2px classDef human fill:#fff,stroke:#0079a8,color:#0079a8 classDef store fill:#f6f8fa,stroke:#0079a8,color:#2c2a29 classDef trig fill:#00a4eb,stroke:#0079a8,color:#fff,font-weight:bold classDef trigtime fill:#00a4eb,stroke:#0079a8,color:#fff,font-weight:bold classDef trigdata fill:#8ad4f5,stroke:#0079a8,color:#06314c,font-weight:bold classDef gate fill:#fff,stroke:#e8a23d,color:#6b4708,font-weight:bold classDef out fill:#1f9d6b,stroke:#167a53,color:#fff,font-weight:bold classDef pay fill:#06314c,stroke:#021f33,color:#fff
Starts itA stepAn outside serviceRuns once per itemEvery pathResultPayoff
Build size
Advanced

A larger build with multiple systems, AI reasoning, and custom rules.

Business functions
AI Agents & Autonomous SystemsEmail AutomationMessaging & NotificationsReal Estate & Property
Connects
GmailSlackOpenAIMLS

The problem it solves

You spend hours scanning listings manually, trying to catch a good deal before another buyer does. By the time you spot an underpriced property and run the numbers, it is often already under contract. Your team needs a faster way to separate real opportunities from noise.

Who it fits

Real estate investors, brokerages, and property management firms that need to move fast on new listings.

How it works

  1. The system pulls fresh listings and recent sales data from your MLS sources every day
  2. An AI agent compares each property's price against similar recent sales
  3. A second AI agent checks broader market trends for context
  4. Properties priced well below market value are flagged
  5. An alert with the details is sent to your email and Slack channel
What you get

Underpriced listings flagged for you to check

You get an alert on properties priced below market value, ready for you to review and act on.

What you get

A daily list of underpriced properties, delivered by email and Slack, with the reasoning behind each flag.

What you need

MLS data access, an OpenAI account, a Gmail account, and a Slack workspace.

We can build this. But should you?

The hard question is not how to build it. It is whether this is the right thing to build first.

That is what a Fractional Chief AI Officer figures out with you, before anyone writes a line of code.

Let's Talk Strategy

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