See how our real estate underwriting API helps serious real estate investors automate underwriting, offer generation, and powers acquisition workflows at scale.
Dottid AI is a real estate underwriting API and execution layer for acquisition teams. Plugging our API into AI agents and workflows automates the underwriting of properties, applies buy box rules, and generates offers. It is not just underwriting software or analysis software; it powers and enables full real estate acquisition automation.
Dottid AI comes in two modes. Use pre-built real estate AI agents when you want packaged acquisition execution now: underwriting, ARV, rehab, and MAO calculations, offer generation, offer delivery, response monitoring, and response processing without building the logic yourself.
Use API infrastructure when you want that same underwriting, offer, and response logic embedded in your own stack. That choice matters operationally. Teams that need to send more offers this quarter can start with agents. Teams that already run a custom acquisition stack can plug Dottid AI into their intake, underwriting queue, offer rules, and review queue through API control.
Route a property into Dottid AI from your intake source.
Underwrite the property and estimate ARV, rehab, and MAO.
Apply buy box rules and thresholds to decide whether the deal clears.
Generate an offer from the underwriting outputs and team rules.
Send the offer to the listing agent.
Monitor inbound replies, counteroffers, and status changes.
Process responses and flag anything that misses thresholds or needs human review.
Surface the deals that clear your criteria into a review queue.
04 Operator Use Case
An acquisition team is screening 40 to 80 properties a week. Their buy box says to underwrite every property that fits a target zip code, price ceiling, and condition band, then generate an offer only if the MAO lands within a set spread and the rehab estimate stays inside a hard cap. Dottid AI runs that intake, applies the thresholds, drafts the offer, and sends it to the listing agent.
If a reply comes back with a counteroffer, unusual terms, or a number that breaks the team’s threshold, it goes to a human. If the response is routine and still fits the rules, the deal stays in the automated path. The operator reviews the surfaced opportunities, checks the exceptions, and makes the call on anything that needs judgment before the team commits capital.
Manual acquisition
Manual acquisition breaks when one property creates too many steps. Underwriting takes time. Offer drafting takes more time. Sending offers to listing agents adds another handoff. Then inbox handling starts: replies arrive at odd times, counteroffers need triage, and follow up gets buried under the next round of underwriting.
Dottid AI workflow
The breakdown is not one slow task. It is the stack. When volume rises, teams have to underwrite, draft, send, check replies, and chase follow up on every property. Faster analysis alone does not fix that. If you can score a deal quickly but still have to write the offer, send it, watch the inbox, and sort the responses by hand, the acquisition team still runs into the same headcount ceiling.
Send more offers without adding the same amount of manual underwriting and drafting work.
Keep reply handling out of the main operator inbox so counteroffers and exceptions are easier to review.
Move more properties through the acquisition queue without turning every deal into a full manual process.
Reduce missed replies and delayed follow up when listing agents respond across multiple deals at once.
Give acquisition teams more review-ready opportunities without forcing proportional headcount growth.
It is the use of software and AI agents to move acquisition work across underwriting, offer generation, sending, response monitoring, and surfaced opportunities. The practical boundary is simple: standard deals can move through the system automatically, while misses on thresholds, unusual terms, and counteroffers still route to a human review queue.
Dottid AI can automate the standard acquisition path: property underwriting, ARV, rehab, and MAO calculation, offer generation, offer sending, reply monitoring, and response processing. It stops short when the deal falls outside the rule set, so exceptions still get reviewed before the team acts on them.
It removes the repeated manual work that slows each property down. The team does not have to underwrite, draft, send, and then chase replies on every deal by hand. That lets operators keep a larger offer queue moving while only stepping in for review points, counteroffers, and threshold breaks.
Anything that breaks the playbook still needs a person. That includes threshold misses, unusual terms, counteroffers, and deals that look fine on paper but need judgment before an offer is accepted or revised. The human review gate matters because automated execution should not override buy box discipline.
Choose AI agents when you want packaged execution now. Choose API infrastructure when you need the underwriting and offer logic embedded in your own stack. That choice matters operationally: agents get you live faster, while API control lets your team route properties, thresholds, and review queues through existing systems.
Dottid AI
Enter a property address and run it through Dottid AI to test the underwriting engine and see how it handles ARV, rehab, MAO, and offer logic for your acquisition workflow.
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