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Christie Peyton Team

How 24 agents kept closing deals when their only TC went on maternity leave with ListedKit AI

Company

Christie Peyton Team

Team

25 members

Location

New Jersey

The Team

Christie Peyton has sold over 3,000 homes across 25 years in real estate. Today she runs three teams across North and South New Jersey: a Zillow Flex team in the north with four agents, a Zillow Flex team in the south with six or seven, and a Realtor.com team in the south with around fourteen. That's roughly 24 agents, all funneling through one person: their transaction coordinator and executive administrator. She does everything.

The Challenge

New Jersey is a two-step state. You don't go straight to contract. You start with a proposal to purchase, then write the contract, then enter a three-day attorney review period. The deal isn't done until attorney review clears. Once it does, everything triggers at once: inspections, loan commitments, radon disclosures, title coordination. Their TC was managing all of those moving pieces for every agent on the team.

Before ListedKit, the team used Open to Close, and they hated it. Christie described it as “not user-friendly” and “awful.” It required extensive manual setup and data entry. Their TC was spending 30 minutes to an hour per file just on initial processing: manually entering information, counting calendar dates from the contract, and coordinating with inspectors, attorneys, and listing agents, all before she could send the first email.

When Christie asked her TC how many transactions she could handle at once, the answer was about ten. And that was with the TC working at full capacity.

When she told me she was going on maternity leave, I was like, we are not prepared for you to leave. What are we going to do?

Christie PeytonTeam Lead

The Decision

Christie was already exploring AI early, asking ChatGPT whether it could help with transaction management. ChatGPT pointed her to ListedKit. But the real urgency came when her TC announced her pregnancy. All of the team's operational knowledge (every process, every template, every coordination workflow) lived in the TC's head. There was no backup plan.

Christie rushed to get ListedKit set up. The TC had it for maybe a week or two before leaving, and she wasn't sold on it yet; she was about to have a baby, and learning a new system wasn't top of mind. Then one Friday, the text came: “Had a baby last night.” She was gone, weeks earlier than expected. And the team had to figure it out.

Before ListedKit

  • Open to Close: manual, painful, "we wanted to strangle it"
  • TC manually counting calendar dates and inputting information for 30–60 minutes per file
  • All institutional knowledge locked in one person's head
  • TC gone = complete operational breakdown
  • No agent accountability for deadlines or tasks

After ListedKit

  • Drop a contract in and Ava reads it, extracts dates, builds timelines
  • Instant timeline extraction: contracts processed in minutes
  • Agents self-serve: drop contracts in, ask Ava questions, run their own deals
  • Business continuity: team survived surprise maternity leave with Ava
  • "There were no excuses because it was in front of them"

The Solution

What happened next surprised everyone. Christie set up the checklists, met with the ListedKit team for help, and gave every agent the same instruction: drop your contract in here. It will do everything for you.

And they did. All 24 agents plugged into ListedKit. They dropped their contracts in, started asking Ava questions, and ran their own transactions. It wasn't perfect (Christie will tell you it was messy at first), but it worked. Agents knew when their inspection periods were over. They knew when loan commitments were due. They knew what to do next because it was right in front of them.

When the TC came back from maternity leave, she found Christie had already set up email templates and checklists. She cleaned things up, adopted the task templates that had been released while she was away, and started working alongside Ava. Now, instead of manually processing each file for 30–60 minutes, she drops the contract in and ListedKit has the dates ready.

Christie's reframe for the team was simple: the TC's job isn't to do the tasks anymore. Ava does the tasks. Her job is to make sure Ava is running correctly.

Your job is to manage the AI now. She used to do the tasks of the transactions. Now Ava does that, and it's her job to make sure Ava's running correctly. If she can let go, she could do thousands of transactions.

Christie Peyton

Team Lead

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The Results

Their TC went from handling ~10 deals to 2–4x that capacity with Ava

2–4x capacity

Transaction setup dropped from 30–60 minutes to just a few minutes per file

30–60 min saved

24 agents kept closing deals when the TC unexpectedly went on leave

Zero downtime

The most dramatic proof of impact? The team survived without their TC. For weeks, 24 agents used ListedKit to manage their own transactions. It wasn't seamless (Christie is the first to say it was messy), but deals kept closing. No one dropped the ball because the deadlines and tasks were visible.

Now that the TC is back and working alongside Ava, the capacity increase is real. Christie estimates she can handle two to three times more transactions than before, maybe four times if they keep refining their setup. The time savings per file alone (eliminating the manual date counting and information entry) add up fast across dozens of active deals.

And the agents? They have no excuses. The deadlines are in front of them. The tasks are laid out. If something doesn't get done, it's not because nobody told them. It's because they didn't check.

There were no excuses because it was in front of them. They knew when their inspection period was over. They knew when the loan commitment was due. There was no way they could get away from it.

Christie PeytonTeam Lead
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Frequently Asked Questions

The Christie Peyton Team uses ListedKit AI (Ava) to multiply their single TC's capacity by 2–4x. Agents drop contracts directly into ListedKit, where Ava reads them, extracts dates, and builds timelines automatically. Their TC focuses on oversight and coordination rather than manual data entry. When the TC was on maternity leave, the agents ran their own transactions by asking Ava questions, proving the system works even without the TC present.
When the Christie Peyton Team's TC went on surprise maternity leave, all institutional knowledge was in her head. Christie had barely set up ListedKit (1–2 weeks), but the agents plugged in, dropped their contracts, and asked Ava questions. They all ran their own transactions. It wasn't perfect, but it kept 24 agents closing deals without a dedicated TC. When their TC returned, she found email templates already set up and a system ready to refine.
New Jersey is an attorney-review state with a two-step process: proposal, then contract, then a three-day attorney review period. Everything triggers from the end of attorney review: inspections, loan commitments, all deadlines. ListedKit AI reads contracts and attorney correspondence, extracts the trigger dates, and builds the full timeline automatically. The TC no longer has to manually count calendar dates and coordinate across all parties.
The Christie Peyton Team switched from Open to Close to ListedKit AI. They described Open to Close as "not user-friendly" and "awful," requiring extensive manual setup and data entry. ListedKit AI reads contracts automatically, builds timelines from the contract data, and provides an AI assistant (Ava) that can answer questions about any transaction. The team saw their TC's capacity increase 2–4x after switching.
Yes, the Christie Peyton Team proved this when their only TC went on surprise maternity leave. 24 agents kept closing deals by dropping contracts into ListedKit AI and letting Ava extract dates, build timelines, and answer transaction questions. It wasn't seamless (Christie describes the transition as messy), but no deals fell through. The key was having AI handle the contract reading and deadline tracking that previously required a TC. When the TC returned, she found the system already set up and her capacity multiplied 2-4x.
Before ListedKit AI, the Christie Peyton Team's TC could handle about 10 transactions at full capacity, spending 30-60 minutes per file on manual date counting, information entry, and coordination. With Ava reading contracts and extracting dates automatically, setup time dropped to minutes per file. Christie estimates her TC can now handle 2-4x more transactions because Ava does the repetitive extraction work, freeing the TC to focus on oversight, communication, and exception handling.
ListedKit AI offers transparent, usage-based pricing up to $9.99 per intake, with bulk discounts that bring the cost down. Your first intake is completely free so you can experience how our AI reads your contracts and listing agreements and helps you manage them through closing. Learn more on our pricing page.

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