1. Open the file app/lia_rules.py.

2. At the bottom of the file, define a new function:

    def classify_inbound_tier(body_lower: str) -> str:
        """
        Classify an inbound message body (already lower-cased) into a LIA tier:
        - T3_HARD_STOP
        - T4_SCHEDULED
        - T5_MONEY
        - T2_SOFT_OBJECTION
        - T6_SUPPORT
        - T1_GENERAL (default)
        The order of checks matters: strongest intent should be caught first.
        """
        # Tier 3: hard stops / do-not-contact
        hard_stop_keywords = [
            "stop",
            "do not contact",
            "dont contact",
            "remove my details",
            "remove my data",
            "leave it",
            "not interested",
            "go away",
            "unsubscribe",
            "take me off",
        ]
        for k in hard_stop_keywords:
            if k in body_lower:
                return "T3_HARD_STOP"

        # Tier 4: time-based follow-up
        scheduled_keywords = [
            "next week",
            "next month",
            "in a few weeks",
            "in a couple of weeks",
            "in a couple of months",
            "later this year",
            "towards the end of the year",
            "try me again",
            "reach out nearer the time",
            "contact me in",
        ]
        for k in scheduled_keywords:
            if k in body_lower:
                return "T4_SCHEDULED"

        # Tier 5: money / price / payment objections
        money_keywords = [
            "too expensive",
            "too dear",
            "too much",
            "too high",
            "cant afford",
            "can't afford",
            "afford it",
            "payment is too",
            "monthly is too",
            "need it cheaper",
            "need cheaper",
            "price is too",
        ]
        for k in money_keywords:
            if k in body_lower:
                return "T5_MONEY"

        # Tier 2: soft objections / hesitations
        soft_objection_keywords = [
            "not right now",
            "not right just now",
            "bit busy",
            "i'm busy",
            "im busy",
            "maybe later",
            "maybe in a few weeks",
            "maybe in a few months",
            "not urgent",
            "not a priority",
            "leave it for now",
            "ill leave it for now",
            "i'll leave it for now",
            "need to think",
            "need to have a think",
            "thinking about it",
            "not sure",
            "unsure",
        ]
        for k in soft_objection_keywords:
            if k in body_lower:
                return "T2_SOFT_OBJECTION"

        # Tier 6: support / service / repair / mot style queries
        support_keywords = [
            "service",
            "mot",
            "repair",
            "courtesy car",
            "courtesy",
            "update on my car",
            "update on the car",
            "in the workshop",
            "warning light",
            "light on the dash",
            "breakdown",
        ]
        for k in support_keywords:
            if k in body_lower:
                return "T6_SUPPORT"

        # Default: general conversation
        return "T1_GENERAL"

3. Save the file.

4. Now open app/lia_inbound.py and:
   - Add this import at the top if it does not exist:

       from app.lia_rules import LIA_TIER_BEHAVIOUR, LIA_TONE_GUIDELINES, classify_inbound_tier

   - Inside process_inbound_message, after you lower-case the body
     (look for something like: body_lower = payload.body.lower()),
     insert:

       tier = classify_inbound_tier(body_lower)

     and then use that tier when selecting the template:

       template = LIA_TIER_BEHAVIOUR[tier]
       response_text = template["example_reply"]
       final_reply = response_text
       reply = final_reply

5. Do not change any database logic, defer / DNC updates, or inserts into dp_messages.
   Only wire in the classify_inbound_tier() function and the template selection.