CRE & AI FAQ
Signal Intelligence FAQ
The platform, the evidence, and the rules that keep a useful answer useful.
What is Signal Intelligence?
Signal Intelligence is Diehl Development’s platform for connecting commercial real estate information with research, CRM context, and workflows. The purpose is to bring scattered records into a usable picture of properties, companies, opportunities, and decisions. HAL provides a conversational way into that work. Start with the decision or task you need help with, then choose the relevant research or workflow.
Link to this answer ↗Where does Signal Intelligence get its data?
The sourcing philosophy starts with public records, government and open-data sources, authorized publisher material, and information supplied with the right to use it. Ownership, permitting, transactions, economics, and property information do not arrive as one neat dataset. Connecting them requires identity checks, dates, source records, and a visible distinction between an observation and an inference. Publicly accessible does not automatically mean unrestricted redistribution.
Link to this answer ↗Does Signal Intelligence use CoStar data?
No. Our rule is no CoStar data in any form. The sourcing boundary also excludes LoopNet and Crexi. We build from permitted sources and retain the evidence behind the work; we do not bypass logins, paywalls, or technical access controls to fill a gap. If the permitted evidence is incomplete, the answer should say so. A blank is more useful than a number wearing somebody else’s confidence.
Link to this answer ↗What does “human in the loop” mean here?
It means a person remains responsible for the objective, the important assumptions, and the consequential decision. In my report work, the platform helps assemble and analyze evidence; I review the reasoning and put my name on the conclusion. Outreach is drafted for human review and sending. Researching a possible action, preparing it, and authorizing it are different steps.
Link to this answer ↗What is adversarial review?
It is a deliberate attempt to find the hole in the answer: the wrong entity match, the convenient comp, the future information leaking into a historical test, or the assumption doing all the heavy lifting. The reviewer’s job is to challenge the conclusion against independent evidence, not compliment the prose. Another model agreeing is not proof; reviewers can share the same blind spot. The discipline is to preserve disagreements and resolve them against sources and tests.
Link to this answer ↗How do you handle missing, stale, or conflicting information?
Show the source, the observation date, and the uncertainty. A listing disappearing is not proof that it sold. An asking cap rate is not a closed transaction. An estimated value is not an appraisal. Where sources conflict, the conflict belongs in the work until it is resolved. “Data you can rely on” includes knowing where you still need a phone call.
Link to this answer ↗Can Signal Intelligence predict who will sell a property?
The Cube research explores ranking properties by their likelihood of transacting using information available at a particular point in time. Historical ranking results are not a guarantee about a specific owner, a sale date, or future performance in another market. I treat the output as a way to prioritize research and conversations. It should make the first question better, not pretend the owner has already answered it.
Link to this answer ↗What principles govern AI actions in Signal Intelligence?
Keep access proportional to the task, separate untrusted source content from instructions, require appropriate authorization for consequential actions, and retain evidence of what actually happened. Human review and adversarial review are complementary checks. These are operating principles; the exact controls and available actions depend on the product and release. They are not a claim that any agent is incapable of making a mistake.
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