Plateforme Email Automation IA

1# Contextual email generation — 15 client personas
2def generate_email(contact: Contact, campaign: Campaign) -> Email:
3    persona = PERSONA_MAP[contact.industry]
4
5    # Build context-aware system prompt
6    system = f"""Tu es un expert {persona.domain}.
7    Ton interlocuteur: {contact.role} chez {contact.company}.
8    Signaux détectés: {', '.join(contact.buying_signals)}
9    Ton: {persona.tone} · longueur: {persona.email_length} mots"""
10
11    response = openai.chat.completions.create(
12        model="gpt-4o",
13        messages=[
14            {"role": "system", "content": system},
15            {"role": "user", "content": campaign.brief},
16        ],
17        temperature=0.7,
18    )
19
20    return Email(
21        subject=extract_subject(response),
22        body=response.choices[0].message.content,
23        personalization_score=compute_score(response, contact),
24    )

1# A/B test engine — statistical significance via chi-squared
2from scipy.stats import chi2_contingency
3import numpy as np
4
5class ABTestEngine:
6    def __init__(self, campaign_id: str):
7        self.campaign_id = campaign_id
8        self.variants: dict[str, Variant] = {}
9
10    def record_event(self, variant_id: str, event: str) -> None:
11        self.variants[variant_id].events[event] += 1
12        self._check_significance()
13
14    def _check_significance(self) -> None:
15        if len(self.variants) < 2:
16            return
17        a, b = list(self.variants.values())[:2]
18
19        # Contingency table: [opens, non-opens] per variant
20        table = np.array([
21            [a.events['open'], a.sent - a.events['open']],
22            [b.events['open'], b.sent - b.events['open']],
23        ])
24        _, p_value, _, _ = chi2_contingency(table)
25
26        if p_value < 0.05:
27            winner = a if a.open_rate > b.open_rate else b
28            self._declare_winner(winner, p_value)