AI Agent for Pest Control: Inspection Scheduling, Treatment Plans, and Follow-Up

Why Pest Control Companies Need Smart Agents

Pest control companies handle a wide variety of pests, each requiring different treatment protocols, safety precautions, and follow-up schedules. A rodent problem in a restaurant demands a fundamentally different response than carpenter ants in a residential home. An AI agent can identify the likely pest from customer descriptions, recommend the appropriate treatment protocol, schedule the right technician with the correct certifications and equipment, and automate the follow-up schedule that ensures the problem is fully resolved.

The recurring revenue model in pest control makes automated follow-up particularly valuable. A quarterly service agreement generates predictable revenue only if the follow-up visits actually get scheduled and completed.

Pest Identification and Treatment Protocol Selection

The agent maps customer descriptions and inspection findings to specific pest types and treatment protocols.

flowchart LR
    CALLER(["Homeowner"])
    subgraph TEL["Telephony"]
        SIP["Twilio SIP and PSTN"]
    end
    subgraph BRAIN["Field Service AI Agent"]
        STT["Streaming STT<br/>Deepgram or Whisper"]
        NLU{"Intent and<br/>Entity Extraction"}
        TOOLS["Tool Calls"]
        TTS["Streaming TTS<br/>ElevenLabs or Rime"]
    end
    subgraph DATA["Live Data Plane"]
        CRM[("CRM and Notes")]
        CAL[("Calendar and<br/>Schedule")]
        KB[("Knowledge Base<br/>and Policies")]
    end
    subgraph OUT["Outcomes"]
        O1(["Service appointment booked"])
        O2(["Quote sent via SMS"])
        O3(["Tech dispatched today"])
    end
    CALLER --> SIP --> STT --> NLU
    NLU -->|Lookup| TOOLS
    TOOLS <--> CRM
    TOOLS <--> CAL
    TOOLS <--> KB
    NLU --> TTS --> SIP --> CALLER
    NLU -->|Resolved| O1
    NLU -->|Schedule| O2
    NLU -->|Escalate| O3
    style CALLER fill:#f1f5f9,stroke:#64748b,color:#0f172a
    style NLU fill:#4f46e5,stroke:#4338ca,color:#fff
    style O1 fill:#059669,stroke:#047857,color:#fff
    style O2 fill:#0ea5e9,stroke:#0369a1,color:#fff
    style O3 fill:#f59e0b,stroke:#d97706,color:#1f2937

from dataclasses import dataclass, field
from enum import Enum

class PestCategory(Enum):
    RODENTS = "rodents"
    TERMITES = "termites"
    ANTS = "ants"
    COCKROACHES = "cockroaches"
    BED_BUGS = "bed_bugs"
    MOSQUITOES = "mosquitoes"
    WILDLIFE = "wildlife"
    SPIDERS = "spiders"

class TreatmentMethod(Enum):
    BAIT_STATIONS = "bait_stations"
    LIQUID_TREATMENT = "liquid_treatment"
    FUMIGATION = "fumigation"
    HEAT_TREATMENT = "heat_treatment"
    EXCLUSION = "exclusion"
    TRAPPING = "trapping"
    GRANULAR = "granular"
    MISTING = "misting"

@dataclass
class TreatmentProtocol:
    pest: PestCategory
    method: TreatmentMethod
    products: list[str]
    safety_requirements: list[str]
    prep_instructions: list[str]
    re_entry_hours: int
    follow_up_days: list[int]       # days after treatment for follow-ups
    certifications_required: list[str]

TREATMENT_PROTOCOLS = {
    PestCategory.TERMITES: TreatmentProtocol(
        pest=PestCategory.TERMITES,
        method=TreatmentMethod.LIQUID_TREATMENT,
        products=["Termidor SC", "Premise 2"],
        safety_requirements=["EPA-approved respirator", "chemical-resistant gloves", "eye protection"],
        prep_instructions=[
            "Clear 18 inches along interior foundation walls",
            "Ensure access to crawl space or basement",
            "Remove stored items from treatment areas",
        ],
        re_entry_hours=4,
        follow_up_days=[30, 90, 365],
        certifications_required=["WDO inspector", "category 7B"],
    ),
    PestCategory.BED_BUGS: TreatmentProtocol(
        pest=PestCategory.BED_BUGS,
        method=TreatmentMethod.HEAT_TREATMENT,
        products=["Industrial heaters", "Temperature monitors"],
        safety_requirements=["Heat-resistant gloves", "hydration protocol"],
        prep_instructions=[
            "Remove all heat-sensitive items (candles, electronics)",
            "Bag all clothing and linens",
            "Open all drawers and closet doors",
            "Remove pets and plants",
        ],
        re_entry_hours=6,
        follow_up_days=[14, 30],
        certifications_required=["heat_treatment_certified"],
    ),
    PestCategory.RODENTS: TreatmentProtocol(
        pest=PestCategory.RODENTS,
        method=TreatmentMethod.EXCLUSION,
        products=["Copper mesh", "Steel wool", "Expanding foam", "Snap traps"],
        safety_requirements=["Puncture-resistant gloves", "dust mask"],
        prep_instructions=[
            "Note all areas where droppings were observed",
            "Clear clutter near walls and in storage areas",
        ],
        re_entry_hours=0,
        follow_up_days=[7, 14, 30],
        certifications_required=["general_pest"],
    ),
}

class PestIdentifier:
    SYMPTOM_MAP = {
        "droppings near walls": PestCategory.RODENTS,
        "gnaw marks": PestCategory.RODENTS,
        "mud tubes on foundation": PestCategory.TERMITES,
        "hollow sounding wood": PestCategory.TERMITES,
        "sawdust piles": PestCategory.ANTS,
        "bites while sleeping": PestCategory.BED_BUGS,
        "blood spots on sheets": PestCategory.BED_BUGS,
        "roaches in kitchen": PestCategory.COCKROACHES,
        "webs in corners": PestCategory.SPIDERS,
    }

    def identify_from_description(self, description: str) -> dict:
        description_lower = description.lower()
        matches = {}
        for symptom, pest in self.SYMPTOM_MAP.items():
            if symptom in description_lower:
                matches[pest] = matches.get(pest, 0) + 1

        if not matches:
            return {"identified": False, "recommendation": "Schedule inspection for identification"}

        best_match = max(matches, key=matches.get)
        protocol = TREATMENT_PROTOCOLS.get(best_match)
        return {
            "identified": True,
            "pest_type": best_match.value,
            "confidence": "high" if matches[best_match] > 1 else "moderate",
            "treatment_method": protocol.method.value if protocol else "inspection_needed",
            "prep_instructions": protocol.prep_instructions if protocol else [],
        }

Scheduling with Certification Matching

Pest control technicians need specific certifications for different treatments. The agent matches the right tech to each job.

from datetime import datetime, timedelta

class PestControlScheduler:
    def __init__(self, db):
        self.db = db

    async def schedule_service(
        self, pest_type: PestCategory, property_address: str,
        preferred_date: datetime = None,
    ) -> dict:
        protocol = TREATMENT_PROTOCOLS.get(pest_type)
        if not protocol:
            return {"error": "No protocol found for pest type"}

        required_certs = protocol.certifications_required
        search_start = preferred_date or datetime.now() + timedelta(days=1)
        search_end = search_start + timedelta(days=7)

        available_techs = await self.db.fetch(
            """SELECT t.id, t.name, t.certifications, t.vehicle_inventory
               FROM technicians t
               WHERE t.certifications @> $1::text[]
                 AND t.status = 'active'
               ORDER BY t.rating DESC""",
            required_certs,
        )

        for tech in available_techs:
            slots = await self.db.fetch(
                """SELECT slot_date, slot_time
                   FROM available_slots
                   WHERE technician_id = $1
                     AND slot_date BETWEEN $2 AND $3
                     AND is_booked = false
                   ORDER BY slot_date, slot_time
                   LIMIT 3""",
                tech["id"], search_start.date(), search_end.date(),
            )
            if slots:
                return {
                    "scheduled": True,
                    "technician": tech["name"],
                    "date": slots[0]["slot_date"].isoformat(),
                    "time": slots[0]["slot_time"],
                    "treatment": protocol.method.value,
                    "products_needed": protocol.products,
                    "prep_instructions": protocol.prep_instructions,
                    "re_entry_hours": protocol.re_entry_hours,
                }

        return {"scheduled": False, "reason": "No certified technicians available in requested window"}

Automated Follow-Up Management

The agent creates and tracks follow-up visits based on the treatment protocol.

class FollowUpManager:
    def __init__(self, db, notification_service):
        self.db = db
        self.notifier = notification_service

    async def create_follow_up_schedule(
        self, service_id: str, pest_type: PestCategory, treatment_date: datetime,
        customer_id: str,
    ) -> list[dict]:
        protocol = TREATMENT_PROTOCOLS.get(pest_type)
        if not protocol:
            return []

        follow_ups = []
        for days_after in protocol.follow_up_days:
            follow_up_date = treatment_date + timedelta(days=days_after)
            visit_type = "inspection" if days_after <= 30 else "preventive"

            await self.db.execute(
                """INSERT INTO follow_up_visits
                   (service_id, customer_id, scheduled_date, visit_type,
                    pest_type, status)
                   VALUES ($1, $2, $3, $4, $5, 'pending')""",
                service_id, customer_id, follow_up_date,
                visit_type, pest_type.value,
            )
            follow_ups.append({
                "date": follow_up_date.strftime("%Y-%m-%d"),
                "type": visit_type,
                "days_after_treatment": days_after,
            })
        return follow_ups

    async def send_upcoming_reminders(self, days_ahead: int = 3) -> int:
        upcoming = await self.db.fetch(
            """SELECT fv.id, fv.customer_id, fv.scheduled_date, fv.visit_type,
                      c.name, c.phone, c.email
               FROM follow_up_visits fv
               JOIN customers c ON fv.customer_id = c.id
               WHERE fv.scheduled_date = CURRENT_DATE + $1
                 AND fv.status = 'pending'""",
            days_ahead,
        )
        for visit in upcoming:
            await self.notifier.send_sms(
                to=visit["phone"],
                message=(
                    f"Hi {visit['name']}, your pest control {visit['visit_type']} "
                    f"is scheduled for {visit['scheduled_date'].strftime('%B %d')}. "
                    f"Reply CONFIRM or call to reschedule."
                ),
            )
        return len(upcoming)

FAQ

How does the agent handle misidentified pests?

The initial identification is always tagged with a confidence level. When confidence is "moderate" or lower, the agent schedules a physical inspection before committing to a treatment plan. During inspection, the technician updates the identification, and the agent automatically adjusts the treatment protocol, product list, and follow-up schedule. The original assessment is preserved in the record for quality improvement.

Can the agent manage recurring quarterly service contracts?

Yes. The agent creates recurring service records that auto-generate work orders each quarter. Each visit includes a standard inspection protocol plus targeted treatment for any new pest activity found. The agent tracks contract renewal dates, sends renewal reminders 30 days before expiration, and alerts the sales team when a customer's contract is approaching lapse.

How does the agent ensure compliance with pesticide regulations?

The agent maintains a database of EPA-registered products with their approved uses, application rates, and restricted-use designations. Before confirming a treatment plan, it verifies that the assigned technician holds the required state category license for the products being used. It also generates the required application reports showing product name, EPA registration number, quantity applied, and weather conditions — all mandatory documentation for regulatory compliance.

#PestControl #TreatmentPlans #InspectionScheduling #RecurringServices #FieldServiceAI #AgenticAI #LearnAI #AIEngineering