See how AI helps serious real estate investors automate underwriting, offer generation, outreach, and acquisition workflows at scale.
Dottid AI is execution infrastructure for real estate acquisition. It is built to underwrite properties, apply buy box rules and thresholds, generate offers, send them to listing agents, monitor replies, and surface the deals that still deserve human review.
It is not just underwriting software or an analysis dashboard. The point is to move the work that sits between finding a property and actually getting an offer out, followed up, and triaged.
Dottid AI comes in two modes. Use pre-built real estate AI agents when you want packaged acquisition execution now: they can underwrite a property, estimate ARV, rehab, and MAO, draft the offer, send it, watch the inbox, process the response, and push exceptions into a review queue.
Use API infrastructure when you need that same underwriting, offer logic, and response workflow embedded inside your own stack. That matters when your team already has an acquisition process and wants Dottid AI logic to sit inside it, instead of forcing the team to work inside another system. For teams that need that programmable control, the closest fit is a real estate AI API that can sit inside your own intake, routing, and review flow.
Property intake starts with an address or deal record.
Dottid AI underwrites the property and calculates ARV, rehab, and MAO.
Buy box rules and thresholds decide whether the deal moves forward.
Offer logic drafts the offer from those outputs.
The offer is sent to the listing agent.
Dottid AI monitors inbound replies and watches for counteroffers, status changes, and missing responses.
Responses are processed and routed for triage.
Only deals that clear the team’s rules are surfaced for review.
04 Operator Use Case
An acquisition team is moving 80 to 150 properties a week through intake. They set a buy box that filters by property type, price band, and MAO threshold, then route every address into Dottid AI for underwriting. If the numbers clear the threshold, the system drafts the offer and sends it out. If a listing agent comes back with a counteroffer, a change in terms, or a missing response after the first pass, that deal moves into a human review queue. The team only steps in on exceptions, tighter terms, or deals that need a manual decision on price, rehab, or exit assumptions.
Manual acquisition
Manual acquisition breaks when the same team has to underwrite, draft offers, send them, check replies, and follow up on every property. Underwriting drag slows the next decision. Offer drafting slows the send. Inbox handling buries replies. Follow up slips. Then counteroffers and exceptions stack up on top of the backlog.
Dottid AI workflow
Faster analysis alone does not fix that. If a team can value a deal quickly but still has to write the offer, send it, monitor the inbox, and triage every response by hand, the bottleneck just moves downstream. The work still has to be done for each property, and volume still runs into headcount.
Send more offers without adding the same amount of manual underwriting and drafting work.
Keep reply handling from slowing down the next round of offers.
Move more deals into a clear review queue instead of letting responses pile up in inboxes.
Review exceptions faster because the standard path is already handled.
Handle more acquisition volume without adding headcount one person at a time.
It is the use of software to move a deal from intake to underwriting, offer drafting, sending, response monitoring, and reply triage without a person touching every step. The practical boundary is simple: the standard path can run automatically, while exceptions and threshold misses still go to a human reviewer.
Dottid AI can automate the standard acquisition path: underwriting, ARV, rehab, and MAO estimation, offer generation, offer sending, inbound response monitoring, and response processing. If a reply includes unusual terms, a counteroffer outside thresholds, or a deal that falls outside the buy box, it routes to review instead of forcing a blind automation decision.
It removes the repeated work that normally sits behind every offer. The team does not have to manually underwrite each property, draft each offer, send each message, and chase each reply before moving to the next deal. That reduces the number of touches required per property, so offer volume can rise without adding the same amount of back-office labor.
Human review still matters for counteroffers, unusual terms, threshold misses, and anything that falls outside the team’s buy box. Dottid AI can surface those cases, but the final call on price, terms, or exception handling should stay with the acquisition team.
Choose AI agents when you want packaged execution now. Choose API infrastructure when you need the logic embedded in your own stack. The tradeoff is operational: agents give you a working acquisition workflow faster, while API infrastructure gives your team control over where underwriting, offer logic, and response handling live inside your system.
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