Shilo

Shilo's transcription engine is optimized specifically for real estate terminology, targeting high accuracy for property-specific vocabulary including neighborhood names, financing terms, and construction terminology. The system is designed for sub-2-second latency processing to enable coaching suggestions to appear while the conversation is still in progress. Comparable real-time transcription engines in the conversation intelligence space (such as those used by Gong and Chorus) report word error rates between 5–10% for domain-tuned models, though Shilo's specific accuracy benchmarks are vendor-reported and have not been independently verified. The transcription engine handles multiple accents and speaking styles commonly encountered in real estate transactions.

During active calls, Shilo surfaces contextual coaching cards on the agent's screen with suggested responses, qualifying questions, and closing techniques. The platform draws from a library of over 200 common real estate objections with AI-generated rebuttals, covering scenarios from price negotiations and inspection concerns to financing questions and competitive property comparisons. Cards are prioritized by relevance to the current conversation context and can be customized by team leaders to align with brokerage-specific scripts and compliance requirements.

Shilo's AI engine learns from successful calls within each team's own history, meaning its suggestions become increasingly tailored to the specific market, property types, and buyer demographics that the team encounters. This team-specific fine-tuning differentiates it from one-size-fits-all coaching scripts and improves recommendation relevance over time. The adaptive learning process requires a sufficient volume of team calls—typically several hundred according to the vendor—to establish meaningful patterns, so new teams should expect a ramp-up period of approximately 4–8 weeks before seeing fully customized suggestions.

Team leaders access a centralized dashboard to review call recordings, track coaching adherence metrics across their roster, and identify specific training opportunities. Post-call analytics generate scorecards that quantify how well agents followed coaching prompts and correlate coaching moments with deal-stage progression, enabling managers to focus mentoring time on agents and skills that need the most improvement. The dashboard supports filtering by agent, date range, deal type, and coaching category.

Shilo integrates with real estate CRMs to automatically log call details, transcripts, and coaching metrics without manual agent input. The vendor's website lists compatibility with platforms including Follow Up Boss, kvCORE, and Salesforce, though prospective buyers should verify current integration support for their specific CRM during the evaluation process. This integration ties coaching data directly to pipeline progression, allowing managers to correlate specific coaching interventions with downstream conversion outcomes across the sales cycle. The number of supported CRM integrations continues to expand.