by LeadFollow (LX AI)
I started LeadFollow because I saw too many agents send vague “let’s chat” notes after a showing. Those notes leave the prospect wondering what to do next and often cause the conversation to fade. I wanted a system that names the exact action while keeping the tone warm and low‑pressure. The approach below shows how we turn a follow‑up into a clear step forward without sounding pushy.
When a prospect sees a specific request they can act on, the mental load drops. They know whether to reply, schedule, or review a document. This clarity reduces the chance of the message being ignored or filed away for later. In our tests, messages that named a concrete step got replies twice as often as those that ended with an open‑ended question.
Another benefit is that the sender feels more confident. Instead of wondering if the tone was too soft or too hard, you can focus on whether the request is doable for the recipient at that moment. This shift from guessing to knowing improves consistency across the team.
Our drafting process starts with the showing notes. We pull out the property details, any questions the prospect raised, and any personal touches they mentioned. From that raw material we build a short sentence that states the next move.
To illustrate the pattern we use a simple table that shows three common scenarios and the exact wording we generate.
| Scenario | Generated next‑step sentence |
|---|---|
| Prospect liked the kitchen but wants to see the basement | Can we schedule a basement walk‑through for Thursday at 4 pm? |
| Prospect asked about HOA fees | Would you like me to send the HOA fee breakdown by end of day? |
| Prospect mentioned needing a mortgage pre‑approval | Shall I connect you with our preferred lender for a quick pre‑approval call? |
Each sentence is under twelve words, uses plain language, and ends with a question that invites a quick reply. We avoid jargon and keep the focus on the prospect’s schedule or information need.
Even a clear request can feel pushy if it is framed as a demand. We soften the ask by placing it inside a sentence that acknowledges the prospect’s time and priorities. The opening line often references a positive moment from the showing, then we slide into the request.
These small linguistic shifts make the message feel like a helpful nudge rather than a sales push. Prospects tell us they appreciate the respect for their schedule and are more likely to respond when they feel in control of the timing.
When we added an AI layer to help draft the next‑step sentences we had to check that the model does not produce unsafe or misleading content. We consulted the OWASP Top 10 for Large Language Model Applications to identify the most relevant risks.
We specifically addressed prompt injection, data leakage, and output bias. For prompt injection we sanitize user input before it reaches the model and we restrict the model to a narrow set of allowed templates. To prevent data leakage we never feed personal identifying information into the model; we only give it anonymized showing notes. Output bias is managed by reviewing a random sample of generated sentences each week and adjusting the fine‑tuning data if we see a pattern.
For more details see the OWASP resource: https://owasp.org/www-project-top-10-for-large-language-model-applications
Beyond the OWASP checklist we aligned our AI workflow with the NIST AI Risk Management Framework. This framework helps us structure governance, risk assessment, and mitigation steps in a repeatable way.
We defined three core functions: map, measure, and manage. In the map phase we list all data flows that involve the model, including input sources and output destinations. In the measure phase we log metrics such as refusal rates, edit distances from human‑written baselines, and user satisfaction scores. In the manage phase we set thresholds that trigger a model retrain or a rollback to the rule‑based template.
For more details see the NIST resource: https://www.nist.gov/itl/ai-risk-management-framework
This method does not guarantee a reply from every prospect. Some leads may still need more time or may have changed their circumstances. It also does not replace a genuine conversation; the clear next step is a tool to move the process forward, not a substitute for listening to the prospect’s needs.
We do not claim that the approach works for every industry or every product type. It was built for residential real‑estate follow‑ups where the showing creates a natural touchpoint. If you try it elsewhere you should test the wording and adjust the tone to match your audience.
I keep a written checklist next to every release that touches this topic. The list is boring on purpose: name the owner, name the evidence artifact, and name the rollback. If a step cannot produce a file, a ticket, or a log line, it is not a step — it is a wish.
| Signal | What to record | Stop-ship? |
|---|---|---|
| Unexpected write or send | Tool name, args, identity | Yes until gated |
| Missing confirmation on destructive action | Call path and bypass | Yes |
| Untrusted text treated as instruction | Source surface and resulting action | Yes for agent reach |
| Scanner clean, judgment open | Deferred items + owner | Only if core path safe |
leadfollow helps with the measurable first pass. Architecture choices, legal sign-off, and customer-facing claims still need a human owner. Treat every checklist row as decision support, not a certificate.
When a prospect asks whether the product "covers everything," I answer with the boundary: automated checks catch known patterns; novel chains and policy judgment still need review. That honesty keeps support load down and keeps the blog aligned with what the product actually does.
If you need a second opinion, pair this write-up with the references below and with your own staging reproduction. Do not skip the reproduction step just because a model wrote a confident summary.
Post-showing and nurture emails that keep you top of mind.
Want to try the product? LeadFollow
Generated 2026-09-17 · primary sources only · no fabricated traffic metrics