See how our real estate API helps serious real estate investors automate underwriting, tailor offers to preset buy boxes, and powers acquisition workflows at scale.
Our Real estate AI API is execution infrastructure for acquisition teams. It allows an agent to underwrite properties and applies buy box rules so offers can be generated at scale all without human input.
Dottid AI gives you two ways to run acquisition execution. Use real estate AI agents when you want packaged underwriting, ARV, rehab, and MAO logic that can generate offers, send them to listing agents, watch replies, process responses, and surface deals for review without building the system first.
Use real estate underwriting API infrastructure when you want that same underwriting, offer, and response logic embedded inside your own stack. That choice matters operationally: agents give you working execution now, while API infrastructure gives your team programmable control over how underwriting thresholds, offer rules, and response handling fit into your acquisition process.
Property data comes in from your inbound flow or acquisition queue.
Dottid AI underwrites the property and calculates ARV, rehab, and MAO.
Your buy box rules and thresholds decide whether the deal moves forward.
If it clears, the system generates an offer from the underwriting output.
The offer is sent to the listing agent.
Dottid AI monitors inbound replies and watches for counteroffers, rejections, and follow-up needs.
Responses are processed, exceptions are escalated, and only the right opportunities are pushed into the review queue.
Your team reviews surfaced opportunities and handles anything outside the standard path.
04 Operator Use Case
An acquisition team is running a steady stream of off-market and inbound leads each week. They set a buy box around property type, price range, and minimum spread, then route each new address through Dottid AI to underwrite ARV, rehab, and MAO before anything reaches a human.
Deals that clear thresholds get offers drafted and sent. If a listing agent comes back with a counteroffer, unusual terms, or a response that falls outside the standard path, the system routes it to a human reviewer. The team stays focused on exception handling and final decision points instead of re-underwriting every property, rewriting every offer, or digging through every reply thread.
Manual acquisition
Manual acquisition breaks when the same team has to underwrite each property, draft each offer, send each offer, and then keep up with inbox replies on top of it. Underwriting drag slows first response. Offer drafting adds another pass. Sending volume becomes a bottleneck. Then reply handling and follow up start piling up, which is how missed replies and stale counteroffers get buried.
Dottid AI workflow
That breakdown is not caused by one slow step. It comes from too many manual tasks per property stacking together as volume rises. Faster analysis alone does not fix that, because the work still has to move through offer creation, delivery, monitoring, triage, and review before a deal is actually executed.
Send more offers without turning every new property into a new manual workload.
Keep underwriting moving when property volume rises instead of letting the queue stall.
Reduce inbox drag by routing replies, counters, and exceptions into a cleaner review path.
Review fewer low-value deals and spend more time on the opportunities that clear your thresholds.
Handle more acquisition work without adding headcount linearly with every increase in volume.
It is the use of software and AI agents to move property intake through underwriting, offer generation, offer sending, reply monitoring, and response triage. The practical boundary is important: the automated path handles standard properties and standard replies, while anything unusual still gets pushed to human review before a final decision or counter move.
Dottid AI can automate the standard acquisition path from underwriting through offer drafting, sending, response monitoring, and initial response processing. If a property misses your thresholds, or a reply includes unusual terms, the deal should route out of automation and into a human review queue so your team handles the exception directly.
It removes the repeated manual steps that slow offer volume down. One operator can underwrite more properties, apply buy box rules, draft offers from consistent logic, and keep replies moving without spending the day rewriting the same outputs, chasing inboxes, or rechecking every counteroffer by hand.
Anything outside the rules still needs human review. That includes threshold misses, unusual seller or agent terms, counteroffers that change the economics, and deals that need a judgment call before you send the next offer or exit the opportunity. Automation should surface those exceptions, not override them.
Choose AI agents when you want packaged execution now. Choose API infrastructure when you need the underwriting, offer, and response logic embedded in your own acquisition stack. That difference matters because agents get a team moving quickly, while API infrastructure gives builders and operators control over where the logic lives and how it connects to their existing process.
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