Branching from assessment of [[Case Studies/Pylon/Assumptions & Problems Addressed]], a valuable tool would be to establish a formula for how the "Business" Agent assess support costs - linear scaling with ticket volume (ext. of customer base). This is a thought exercise, designed to abstract the levers of support costs, not a literal accounting model; frame the operational challenges for myself. **The "Outdated" Formula:** $C_{\text{total}} = (V_{\text{total}} \times C_{\text{agent}}) + (V_{\text{esc}} \times C_{\text{eng}}) + C_{\text{tools}}$ **The "Pylon" Redefined Formula:** $C_{\text{new}} = (V_{\text{total}} \times (1 - R_{\text{ai}})) \times (C_{\text{agent}} \div E_{\text{agent}}) + (V_{\text{esc}} \times (1 - R_{\text{esc}})) \times (C_{\text{eng}} \div E_{\text{eng}}) + C_{\text{platform}}$ The magic is in the **four new variables** that Pylon introduces: ## Defining the variables for the "outdated" or current formula: - $C_{\text{total}}$ = Total Cost of Support The final number the "Business Agent" is trying to control. - $V_{\text{total}}$ = Total Volume of Tickets Core linear driver. As the customer base grows, $V_{\text{total}}$ grows, and every other variable tied to it explodes. - $C_{\text{agent}}$ = Average Agent Cost Per Ticket This is the fully-loaded cost (salary, training, overhead) for a support agent to handle one ticket. The only "fix" in this old model is to hire more agents as $V_{\text{total}}$ increases. - $V_{\text{esc}}$ = Volume of Escalated Tickets This is the percentage of $V_{\text{total}}$ that requires engineering. It also grows linearly with the total volume. - $C_{\text{eng}}$ = Average Engineering Cost Per Escalation This is the "Engineering Support Tax" quantified—the fully-loaded cost (an engineer's valuable time) to resolve one escalated ticket. - $C_{\text{tools}}$ = Total Tooling Cost The (often per-seat) licenses for the helpdesk, Jira, etc., which also scales up as the number of agents increase ## And expanding/evolving the formula with/for Pylon: - **$R_{\text{ai}}$ (The AI Resolution Rate)** - It directly attacks **$V_{\text{total}}$ (Total Volume)**; the key lift, represents Pylon's AI Agents and automated deflection. - If AI resolution rate is 50%, you've _halved_ the number of tickets that even need to touch a human. The linear relationship ($V_{\text{total}}$) is broken. (Nice) - **$E_{\text{agent}}$ (Agent Efficiency Gain)** - This lever attacks **$C_{\text{agent}}$ (Agent Cost)**. - This represents Pylon's AI Assistant (suggested replies, surfacing knowledge).1 If an agent can solve tickets 2x faster, you've effectively cut their per-ticket cost in half (or doubled their capacity), fighting burnout and scaling the _existing_ team. - **$R_{\text{esc}}$ (Escalation Reduction Rate)** - This lever attacks **$V_{\text{esc}}$ (Escalation Volume)**. - By giving L1 Support agents a unified view, better context, and AI-surfaced knowledge, they can solve more complex problems without escalating (reduced "engineering tax"). - **$E_{\text{eng}}$ (Engineering Efficiency Gain)** - This lever attacks **$C_{\text{eng}}$ (Engineering Cost)**. - For the escalations that do get through, they are now "clean" (with full context, logs, and user history). This eliminates the back-and-forth, slashing the engineering time-per-escalation from (for example) 45 minutes down to 10. **Takeaway:** Pylon's pitch isn't just "we're a cheaper $C_{\text{tools}}$." The platform fundamentally re-architects the entire cost structure of support by introducing exponential efficiency (deflection and assistance) to a traditionally linear problem. ## Mock Scenario: "Anto-Novation", a Mid-Sized B2B Company Modeling / drawing it out to show impact; the numbers tell the story. This is fun for me. *(Disclaimer: This is a thought exercise and for my own enjoyment. The goal is to model the levers of change, not to be a literal accounting sheet. The numbers are for illustration.)* **Baseline Assumptions (Monthly):** First, establishing baselines under the "Outdated" model. - **Total Tickets ($V_{\text{total}}$):** **2,000** - Avg. Agent Cost Per Ticket ($C_{\text{agent}}$): $20 (Based on a fully-loaded agent salary, training, and overhead) - Escalation Rate: 20% (1 in 5 tickets needs an engineer) - **Volume of Escalations ($V_{\text{esc}}$):** 20% of 2,000 = **400** - Avg. Eng. Cost Per Escalation ($C_{\text{eng}}$): $100 (Based on 1-1.5 hours of a fully-loaded engineer's time for discovery, back-and-forth, and fixing) - Tooling Cost ($C_{\text{tools}}$): $1,500 (Seats for Zendesk, Jira, etc.) **Calculation:** - **Support Agent Cost:** (2,000 tickets $\times$ $20) = **$40,000** - **Engineering "Support Tax":** (400 escalations $\times$ $100) = **$40,000** - **Tooling Cost:** = **$1,500** **Total Monthly Cost: $81,500** > **Key Insight:** In this common scenario, the "hidden" Engineering Support Tax ($40k) is _just as expensive_ as the _entire_ visible support agent payroll ($40k). This is the core problem an engineering leader feels. ### The "Pylon" Model: Redefined Applying Pylon's levers, assumed, to the _exact same_ 2,000 tickets. **Pylon Levers (Assumptions):** - AI Resolution Rate ($R_{\text{ai}}$): 40% (40% of total tickets are deflected by AI, never touching a human) - Agent Efficiency Gain ($E_{\text{agent}}$): 2x (AI-assist helps agents solve tickets twice as fast, halving their cost-per-ticket to $10) - Escalation Reduction Rate ($R_{\text{esc}}$): 50% (Better-equipped L1 agents solve 50% of the issues that would have been escalated) - Eng. Efficiency Gain ($E_{\text{eng}}$): 2x (Clean escalations cut engineering time in half, reducing their cost-per-escalation to $50) - New Platform Cost ($C_{\text{platform}}$): $5,000 (A higher, all-in platform cost) **Calculation:** - **Human-Handled Tickets:** 2,000 $\times$ (1 - 0.40) = **1,200 tickets** - **New Agent Cost Per Ticket:** $20 $\div$ 2 = **$10** - **New Support Agent Cost:** (1,200 tickets $\times$ $10) = **$12,000** - **Original Escalations:** 400 - **New Escalation Volume:** 400 $\times$ (1 - 0.50) = **200 escalations** - **New Eng. Cost Per Escalation:** $100 $\div$ 2 = **$50** - **New Engineering "Support Tax":** (200 escalations $\times$ $50) = **$10,000** - **New Platform Cost:** = **$5,000** **Total New Monthly Cost: $27,000** ### Bottom-Line Comparison What story do the numbers tell? |**Metric**|**"Outdated" Model**|**"Pylon" Model**|**% Change**| |---|---|---|---| |**Total Support Cost**|**$81,500**|**$27,000**|**-67%**| |Engineering Support Tax|$40,000|$10,000|**-75%**| |Support Agent Cost|$40,000|$12,000|**-70%**| |Tickets Handled by Humans|2,000|1,200|**-40%**| |Tickets Escalated to Eng.|400|200|**-50%**| Nice, business isn't just saving money here, I can propose it's reclaiming 75% of its engineering capacity that was being lost to the "support tax."