Case StudyIndustrial Investor & AdvisorOrange County, CA
How NewFront Properties turned raw exports into owner conversations at a 15% response rate
A Southern California industrial principal was losing hours a day to list work before any conversation could start. An AI employee now turns any export into enriched, qualified owner leads and runs the outreach in his own voice, at a client-reported 15% response rate.
15%
Any export
0
The Challenge
What was actually in the way.
NewFront Properties acquires industrial property across infill Southern California, including environmentally impacted sites that most buyers will not touch. Finding the right owner to call is the job, and it is almost entirely manual.
That meant pulling exports from Yardi, CoStar, and wherever else a list could be found, working out which owners were actually worth approaching, and then hunting down contact details. Hours of it, every time, before a single conversation could start.
Some of the highest-value target sets had no list to export at all. Registered investment advisors, wealth managers, business owners, community lenders: real targets with no clean source to pull from.
The Solution
What PSV built.
PSV built an AI employee that works by email, in the principal's own name. Any export gets dropped in and comes back as qualified owner leads with ownership resolved and contact details enriched, so the list work happens before the day starts rather than during it.
When there is no list to drop in, it builds one from a target profile instead, which is what made the profile-shaped target sets reachable at all.
Outreach runs as approved sequences against those owners, written for the specific angle. The environmental-buyer sequence leads with the thing that makes NewFront a credible buyer of a contaminated site, and the general industrial sequence runs separately. Sequences started in draft until the language was approved, then began sending on their own, with warm replies routed straight back to a person.
One capability came out of the work rather than the scope. Ahead of a refinance, the employee compiles every bank and credit union with a Southern California office and finds the loan-officer contact at each, using public data only. It is trained to know the large names are already covered, so it prioritises the smaller local and community lenders in Orange County, where the relationship is actually worth building.
Corrections go back the same way the work comes out. If a returned name does not fit, a reply saying which one and why sharpens the next list.
01
Owner resolution and enrichment
Any export becomes qualified leads with the owner resolved and contact details attached, so the file that arrives is one you can act on.
02
List building from a profile
When there is no list to start from, the target set is built from a described profile instead of bought or assembled by hand.
03
Approved outbound sequences
Sequences run per angle in the principal's own voice, starting in draft until the language is approved, with warm replies routed back to a person.
04
Community-lender research
Banks and credit unions with a local office are compiled with their loan-officer contacts from public data, weighted toward the smaller names.
The Impact
15% of owners reply. Zero hours spent building the list.
Owner outreach runs at a client-reported 15% response rate. It goes out in the principal's own name and voice, which is most of the reason it gets answered rather than filtered.
The list-building hours at the front of the day are gone. Exports come back enriched and qualified, and target sets that had no list to export are built from a profile instead.
Lender relationship-building that would have meant a paid data subscription and a week of research now runs on public data, weighted toward the local names that would otherwise never get contacted.
15%
Response rate on owner outreach
Client-reported on the live owner sequences. Outreach runs in the principal's own name and voice, which is most of why it gets answered.
Any export
Yardi, CoStar, or no list at all
The employee takes whatever export exists and enriches it, or builds the list from a target profile when there is nothing to start from.
0
Paid subscriptions for lender research
Community-lender research runs on public data only, prioritising the smaller local and regional names rather than the ones already on speed dial.
The system behind it
AI Sourcing & Outreach Employee, built for one firm at a time.
Nothing here is a product with a login. Every build starts from the firm’s own models, templates, and criteria, which is why the output lands in a format the team already trusts.
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