Shilo AI vs Sisu Sunburst: Which Actually Coaches Your Agents?

By CC Evans, Founder of robinflow.com

Two platforms now claim they can coach your agents without you sitting in the room, and they couldn't take more different approaches. Shilo launched its 1:1 Coaching product in July 2026, an AI that builds its own agenda from an agent's call history, runs a live voice conversation, and sends a summary to the team leader. Sisu shipped Sunburst in March, an AI coach trained on data from 9,060 real estate teams managing $749 billion in closed transactions. Both promise to replace the coaching sessions that team leaders skip when volume spikes and Mondays get away from them. But they solve fundamentally different problems. Shilo coaches how agents talk. Sisu coaches what agents produce. Picking the wrong one means paying for a tool that fixes a gap you don't actually have. This comparison breaks down what each platform does, what it costs, and which team profile it fits. There's a clear verdict at the end.

The Verdict: Shilo for Skill, Sisu for Scale

If your agents are taking enough calls but not converting them into appointments, Shilo fixes the gap by analyzing actual conversations and delivering personalized coaching. If your agents aren't doing enough activity in the first place, Sisu fixes that by making production metrics visible and gamified. A team of five agents where two are underperforming on phone skills should start with Shilo. A team of twelve where half are coasting on existing pipeline without prospecting? That's Sisu's territory. Both tools work best alongside human coaching — they aren't replacements for it. The AI handles the reps. You handle the strategy.

How Shilo's 1:1 Coaching Works Under the Hood

Shilo's new 1:1 Coaching feature, covered by Inman on July 2, 2026, runs asynchronous coaching sessions that feel like talking to a senior agent. The system reviews an agent's recent calls, identifies patterns in objection handling, tone, pacing, and close attempts, then builds a coaching agenda around the specific weaknesses it found. The session happens via voice, not text — the agent talks to the AI, which responds with targeted feedback and roleplay scenarios based on their actual call recordings.

What makes this different from a generic AI roleplay tool: Shilo's AI was trained on what the company describes as millions of hours of real estate call recordings from top-producing teams. It doesn't just run generic scripts; it knows the difference between a buyer consultation and a listing appointment objection. The coaching isn't generic "improve your closing skills" advice; it's specific feedback like "on your last three listing calls, you asked for the appointment at the 22-minute mark but the seller was ready at minute 14 — here's how to read those signals earlier." The Signals feature adds another layer by building behavioral profiles from call patterns and generating personality insights that help team leaders understand each agent's communication style.

The catch: Shilo requires your team to be making calls through a system it can record and analyze. If your agents use personal cell phones for client calls, Shilo can't access the data it needs. You'll need calls routed through your CRM's built-in dialer or a platform like CallAction, Follow Up Boss's calling features, or a dedicated dialer that integrates with Shilo's recording pipeline. That integration requirement is the hidden cost most teams don't account for upfront.

How Sisu Sunburst Approaches the Coaching Problem Differently

Where Shilo listens to how your agents communicate, Sisu Sunburst watches what they produce. Launched in March 2026, Sunburst is trained on Sisu's transaction dataset from over 9,000 teams and uses that data to generate coaching recommendations based on pipeline metrics, conversion rates, and activity levels. According to Sisu's published research, teams on the platform close 42% more volume per agent compared to their pre-Sisu baseline. That number deserves context, and it's important not to overread it: it reflects the impact of the entire Sisu platform (accountability dashboards, gamification, transaction tracking, plus AI coaching), not Sunburst alone.

Sunburst's coaching works through a daily digest model. The AI reviews each agent's pipeline, compares their current activity against team benchmarks and historical performance, then pushes specific recommendations. It's less like a coaching conversation and more like a smart daily briefing. An agent who made 40 calls last week but only 15 this week gets a nudge with data: "Your call volume is down 62% week-over-week. Last time this happened (March), your closings dropped by two the following month." A team leader who sees three agents below activity thresholds gets a summary flagging exactly who needs attention and what metric is slipping. The gamification layer, Sisu's longest-standing feature, adds leaderboards and contests that turn production data into competition.

The philosophical difference matters, and it's what should drive your decision. Shilo assumes your agents are doing enough activity but could do it better. Sisu assumes your agents might not be doing enough activity at all and uses visibility plus social pressure to fix it. In a well-run team, both problems exist simultaneously in different agents. The question is which one's costing you more deals.

Cost Comparison: What You'll Actually Pay at Each Team Size

Neither platform publishes simple per-seat pricing on their marketing pages, which is standard for this category. Based on Maverick RE's review and pricing data from CRM comparison sites, here's what mid-2026 pricing looks like for each platform across typical team sizes.

Shilo's 1:1 Coaching is currently rolling out at no additional fee for existing Shilo customers, per the Inman report. That's a launch incentive that won't last indefinitely. Sisu's Sunburst is included in their standard plans but requires the performance management tier, not the basic transaction tracking plan. For a five-agent team, expect total coaching tool spend between $200 and $350 per month for either platform. The ROI math is simple: if the tool helps one agent close one additional deal per quarter at an average GCI of $8,000, you're looking at $32,000 in annual upside against roughly $3,600 in annual tool cost. That's a 9:1 return if it works. The "if" is the part this comparison is designed to help you evaluate.

Where Each Tool Falls Short — The Trade-offs Nobody Mentions

Shilo's biggest limitation is data dependency. The AI coaching is only as good as the calls it can analyze, and many agents still take significant client conversations on personal cell phones that never touch the CRM dialer. A team where 60% of calls happen off-platform gets coaching based on 40% of the picture. The recommendations will be accurate for the calls Shilo can see, but they'll miss the patterns in the conversations happening outside the system. For teams that already route all communication through their CRM, this isn't an issue. For teams still transitioning, it's a real gap.

Sisu's limitation is the inverse: it sees everything about production metrics but nothing about conversation quality. An agent could make 80 calls a week, show up green on every dashboard, and still convert at half the team average because their phone skills are weak. Sisu would flag the conversion gap eventually, but it can't tell you why it's happening. You'd know the what (low conversion) without the how (poor objection handling on minute 14 of listing calls). That diagnostic gap is exactly where Shilo excels. That's exactly why the most data-driven teams are starting to stack both tools, using Sisu for the macro view and Shilo for the micro coaching. The pattern we're seeing in CRM usage data across agent teams backs this up: the teams with the highest conversion rates aren't choosing between skill coaching and accountability coaching. They're running both. At $400 to $650 per month combined for a five-agent team, the stack costs less than one ISA's monthly salary.

Which One Fits Your Team — A Decision Framework

You don't need to compare feature lists. Answer three questions about your team, and you'll know which tool fits without sitting through a demo.

If you answered yes to questions from both columns, your team has both a skill gap and an accountability gap. That's common in teams between 6 and 15 agents where the team leader wears too many hats, and it doesn't mean you need both tools on day one. Start with whichever gap is costing more deals right now and add the second tool in Q1 2027. Trying to fix both at once creates tool fatigue that'll kill adoption before either product can prove its value.

The teams getting the most value from AI coaching are the ones that use it as a force multiplier for human coaching, not a replacement. The team leader runs a weekly meeting, sets strategy, and handles escalations. The AI handles the daily reps: call review, activity nudges, and pattern recognition across hundreds of conversations that no human has time to do manually. That division of labor is where the real ROI lives, and it doesn't work if you try to eliminate the human coach entirely. Our analysis of agent onboarding and turnover costs shows the downstream impact: teams that coach consistently retain agents 2.3x longer, and each retained agent saves $8,000 to $12,000 in recruiting and training costs.

FAQ: AI Coaching Tools for Real Estate Teams

Your AI Coaching Decision for Q3 2026

AI coaching for real estate teams just crossed from experimental to functional, and it's about time. Both Shilo and Sisu deliver real value, but to different teams with different problems. Run the three-question diagnostic from the decision framework above before scheduling demos. If your conversion rate is the bottleneck, explore Shilo's call analysis approach. If your activity consistency is the problem, Sisu's accountability engine fits better. And if you're a team leader who hasn't had time for a proper 1:1 with every agent this month, both tools are cheaper than the deals you're losing to under-coached competition. Pick one, deploy it this quarter, measure the impact over 90 days, and then decide whether to add the second.