AI Voice Agent for Tech Support: 2,260 Calls in One Month
In 30 days, SARA managed 2,260 calls with immediate response and 76.5% useful outcomes. How do you bring an AI voice agent to operational value?
An AI voice agent for technical support can transform the first point of contact: immediate triage, complete data, and no lost calls. In this case, SARA (powered by DeepAgent) handled 2,260 calls in one month with a 100% response rate and an operational efficiency of 76.5%, closing 1,730 requests. Here's how. ## TL;DR - 2,260 inbound calls handled by SARA, with immediate and 24/7 response: no waiting and no loss during peak times or outside business hours. - 1,730 requests closed with objectives met: information resolved over the phone or cases opened with scheduled callbacks. - On-the-fly triage, data collection, and verification (customer code, system type, account holder, urgency) to deliver complete tickets to technicians. - +31 percentage points of efficiency in 4 weeks: from an initial 45.5% to 76.5% thanks to calibration based on real conversations. - Technicians freed from phone duty; SLAs and urgent issues managed with 24/7 customer care based on an AI voice agent. ## Context and Challenge ### Why calls were getting jammed - The same questions every day: filters, CO₂, salt, appointments. - Technicians and operators on the phone instead of on-site. - Incomplete data: without customer code, system type, or account holder, the case was returned. - After-hours and peaks: unanswered calls, even for urgent breakdowns. ### Objective Answer everyone and send technicians only complete requests, reducing callbacks and idle time. ## Solution: SARA, an AI Voice Agent on DeepAgent ### 4-step Flow 1) Answers: Every call is answered, without waiting and at any time. 2) Triage: Immediately recognizes the nature of the request (maintenance, breakdown, CO₂, salt, appointment, administrative tasks). 3) Collects and verifies data: customer code, account holder, system, and urgency during the call. 4) Closes or Passes: Informational questions are resolved on the spot; otherwise, a case is opened with a scheduled callback. ### Why step three changes everything Ticket opening only works if the form is complete. The person taking charge already knows the system, account holder, and priority. No more callbacks for clarification; the technician can intervene immediately. !AI voice agent for tech support: 2,260 calls in one month — figure 1 ## A Real Call: From Request to Ticket - Immediate recognition of the system (water softener, water dispenser, or filter) from the first phrases. - Identification of the need: scheduled maintenance or breakdown. - Data collection and verification: customer code and account holder. - Case opening and callback confirmation with clear timelines. Result: No request remains pending, and the technician receives a ready-to-act brief. !AI voice agent for tech support: 2,260 calls in one month — figure 2 ## Results of the First 30 Days - **2,260** inbound calls managed. - **100%** calls answered. - **4,177** minutes of conversation (approx. **70 hours**). - **1,730** requests closed with objectives met. - Operational efficiency: **76.5%** (from **45.5%** in the first days, +**31** pp). Operational efficiency measures calls closed with a useful outcome (answer given or case opened). The initial automatic flag was **48%**: human re-reading of conversations corrected the under-reporting, establishing the real value at **76.5%**. ### KPIs at a Glance | Indicator | Value | |---|---| | Inbound Calls Managed | 2,260 | | Calls Answered | 100% | | Conversation Minutes | 4,177 (~70 hours) | | Requests Closed with Objective | 1,730 | | Operational Efficiency (first days) | 45.5% | | Operational Efficiency (month 1) | 76.5% | | Efficiency Increase | +31 pp | | Initial Automatic Flag | 48% | !AI voice agent for tech support: 2,260 calls in one month — figure 3 ## Continuous Improvement: From 45.5% to 76.5% The leap doesn't come from a different flow, but from systematic listening to calls. SARA was calibrated to technical jargon (filters, CO₂, water softeners, water dispensers) and to registration/routing criteria. Each week of real conversations improved the next. ## Impact on Team, Processes, and Customers ### On the team Repetitive requests no longer reach those who plan interventions: time is reallocated to maintenance. ### On processes The ticket already includes system type, customer code, account holder, and urgency. ### On service First contact is covered 24/7: peak times and after-hours do not interrupt customer care, and urgent matters are not left behind. ## Lessons Learned - The first month is calibration: starting at **45.5%** is normal; the trajectory matters. - Automatic data is not enough: compare flags with the reality of conversations. - Data quality is as important as the answer: a complete ticket reduces time and callbacks. ## Conclusion No call unanswered, no technician tied to the phone. In 30 days, SARA handled **2,260** calls, closed **1,730** requests, and boosted efficiency to **76.5%**. Want to see the same impact on your inbound operations? Request a DeepAgent demo and test SARA on a real workflow: measure KPIs and operational value in just a few days. ## Frequently Asked Questions ### How does the AI voice agent integrate with existing CRM and management systems? DeepAgent integrates via API with CRMs and ticketing systems (e.g., collection/validation of customer code, master data, system type). The AI voice agent writes fields into the record, opens the ticket, and sets up the callback. This ensures triage is tracked, data is complete, and technicians receive actionable cases without needing to call the customer back. ### Can it provide 24/7 technical support and manage peak loads? Yes. The AI voice agent answers 24/7, scales multiple calls in parallel, and maintains consistent SLAs even during peak times. Urgent issues are recognized during triage and routed with priority. This prevents queues, missed calls, and transfers, ensuring service continuity even after hours for purifiers, water softeners, and water dispensers. ### Which KPIs should I measure in the first month of calibration? Focus on: response rate, operational efficiency (useful outcomes), average conversation time, data completeness (customer code, system, account holder), percentage of avoided callbacks, and triage quality. Compare automatic flags with a sample re-reading: it helps calibrate the agent and move, as in this case, from 45.5% to 76.5%. ### Does it recognize the system type without tone menus? Yes. Natural recognition allows it to distinguish between a water softener, water dispenser, filter, or other water treatment solutions from the first phrases. The AI voice agent does not force IVR tone menus: it guides the collection of relevant data, reduces friction, and speeds up the opening of the technical support ticket. ### How does it handle urgent breakdowns and routing to technicians? During triage, urgency is explicitly requested and validated. If it's a critical breakdown, the AI voice agent immediately opens the case with priority, notifies the team according to on-call rules, and schedules the callback. This way, urgent reports do not go unanswered, even after hours. Talk to sales → · Create your agent →