Shilo Ai Tool Turns Agent Calls Into Personalized Coaching Playbooks

Shilo has launched Signals, an AI-powered personality assessment that builds DISC behavioral profiles for real estate agents directly from their call recordings, the company announced.

The Phoenix-based AI conversation analysis platform said the new feature analyzes weeks or months of agent conversations to surface each agent’s core motivators, fears, conflict style and social orientation. Signals then generates individualized coaching recommendations based on how an agent actually communicates, rather than how they describe themselves in a survey.

Removes bias and adjusts to agent’s behavior changes

Traditional DISC personality tools rely on self-reported questionnaires, which can bias results or go stale as an agent’s behavior changes. Shilo positions Signals as a way for teams to continuously measure communication style in the background of day-to-day work and tie that to coaching, script changes and lead follow-up strategies.

Every insight generated by Signals is linked back to specific calls with confidence scores, giving team leaders a clear audit trail for why the platform labeled an agent as a particular DISC type or suggested a specific coaching action, according to the announcement.

Brokerages and teams spend heavily on coaching and training but often deliver the same content to every agent. Shilo cites National Association of Realtors (NAR) data showing that 87% of agents leave the industry within five years, and internal estimates that teams waste 40% to 60% of their lead investment due to inconsistent call execution.

Signals is designed to make coaching more precise by tailoring recommendations to how each agent processes information and takes action. For housing leaders, the pitch is that personality-aware coaching could improve conversion on existing leads and reduce churn among agents who may struggle under one-size-fits-all training programs.

Platform has processed more than 3 million calls

Signals runs on Shilo’s proprietary models trained on what the company says is more than 21 years of continuous talk time across more than 7,000 real estate agents. Since launch, the platform has processed more than 3 million calls, which Shilo describes as the largest dataset of analyzed real estate conversations in the industry.

“Transparency to data is core to who we are at Shilo because at a fundamental level it builds trust,” Justin Benson, CEO and co-founder of Shilo, said in the release. “We don’t suggest blind trust of AI in the same way we usually wouldn’t suggest blind trust of another person without the historical backdrop that proves trust. Each signal is given a transparent confidence score and backed by cited evidence from previous conversations you can click into and verify.”

The system automatically builds personality insights from calls agents are already making through existing phone systems and CRM integrations. That removes the need to schedule separate assessments and reduces friction for adoption on large teams.

Each Signals profile includes: DISC personality insights with spectrum bars for Dominance, Influence, Steadiness and Conscientiousness, an “About me” narrative, a plain-language summary drawn from call patterns, core motivations and fears, conflict style and social orientation describing how an agent handles disagreements and builds relationships and personalized coaching recommendations tailored to the agent’s DISC mix

These recommendations are meant to be specific and situational rather than generic. In one example provided by Shilo, the platform suggests that an agent with an SC profile adjust how they speak with high-D or high-I clients by leading with the fastest path to listing rather than process details, and saving the details for the end of the conversation.

Updated as agent makes more calls

As agents make more calls, Signals updates profiles and confidence levels and surfaces new suggestions as patterns change. For managers, that creates a living personality and coaching layer on top of existing call metrics such as talk time, contact rate and appointment set rate.

For real estate and mortgage teams, coaching quality is often the difference between converting online leads and burning them. Conversation analytics platforms have focused largely on script adherence and keyword tracking. Shilo’s move into personality-based insights reflects a broader trend of applying AI not just to what is said on calls, but to who is saying it and how.

By tying personality insights to verifiable call data, Signals aims to give team leaders a framework to decide which agents should be on the phone, which should focus on in-person consultations, and how to adjust scripts for different communication styles. In an environment of tighter lead budgets and higher scrutiny on agent productivity, tools that help align coaching with behavior could influence hiring, routing and training decisions.

Shilo’s broader platform scores calls on a 1-to-5-star scale, delivers per-call coaching with script replacements, creates both agent-level and organization-level insights, automates CRM updates, and generates AI roleplay scenarios from real conversations. Current integrations include Follow Up Boss, Sierra Interactive, BoldTrail, Lofty, CINC, SureSend and Bonzo, with an API-only option for enterprise companies that want custom models.

Editor’s note: This article was generated using HousingWire Automation and reviewed by a HousingWire editor before publication. The system helps convert company announcements and industry data into HousingWire-style news coverage.