Bridges Creative Workflows with Agentic AI & RPA

def exponential_decay(episode, v1, decay_rate):
    return v1 * np.exp(-decay_rate * (episode - 1))

# Fits early episodes (1-5) retention decay rate
popt, _ = curve_fit(
    lambda e, d_rate: exponential_decay(e, v1, d_rate),
    episodes, views_5, p0=[0.15], bounds=(0.001, 1.5)
)
est_decay_rate = popt[0]

def parse_dialog_script(script_text):
    lines = script_text.split('\n')
    dialog_track = []
    for line in lines:
        if line.startswith("F:"): // Lock Chinese Seed 313 (Female)
            dialog_track.append({"voice": "seed_313", "text": line[2:]})
        else:
            dialog_track.append({"voice": "kokoro_v0", "text": line})
    return dialog_track

import pandas as pd
def consolidate_claims(daily_reports_paths):
    dfs = [pd.read_csv(p) for p in daily_reports_paths]
    merged = pd.concat(dfs, ignore_index=True)
    # Drop strict duplicates across regional rights registers
    clean_df = merged.drop_duplicates(subset=['asset_id', 'claim_time'])
    # Query high-value targets (Rick and Morty / Gumball)
    targets = clean_df[clean_df['title'].str.contains('Morty|Gumball', case=False)]
    return clean_df, targets

// Call official Google GenAI SDK to diagnose decay
const response = await ai.models.generateContent({
    model: modelName,
    contents: `Analyze this decaying ad payload:\n${jsonInput}`,
    config: {
        systemInstruction: SYSTEM_INSTRUCTION,
        temperature: 0.7,
    }
});
return { text: response.text, usage: response.usageMetadata };

def observe_and_update(actual_latency):
    # Bayesian update: Adjust risk beliefs based on latency
    if actual_latency > self.latency_baseline * 2.5:
        self.risk_level = (self.risk_level * 1.5) + 0.1
    else:
        self.risk_level *= 0.85
    return min(max(self.risk_level, 0.01), 0.99)

def get_next_delay(profile="smooth"):
    # Tiers: Hyper [0-150s] | Yield [150-400s] | Chaos [2000s+]
    if self.risk_level > 0.7:
        start_idx = random.randint(300, 1200)
    else:
        start_idx = random.randint(0, 60)
    pool = self.all_primes[start_idx : start_idx + 15]
    return poisson_sample(random.choice(pool))