Public relations and corporate communications professionals manage complex, high-stakes messaging across multiple stakeholders: media, investors, employees, regulators, and the public. Chinese LLMs like DeepSeek V4, GLM-4, and Qwen3 can accelerate PR workflows by drafting releases, monitoring sentiment, analyzing media coverage, and preparing crisis responses, all while maintaining brand voice and messaging consistency. TokenEase's unified API provides communications teams with cost-effective access to these powerful models.
Transform raw announcements, executive quotes, and data points into polished, publication-ready press releases.
import requests
def generate_press_release(announcement_details, company_profile, target_media, key_messages):
response = requests.post(
"https://tokenease.io/v1/chat/completions",
headers={"Authorization": "Bearer YOUR_TOKENEASE_API_KEY"},
json={
"model": "deepseek-v4",
"messages": [
{"role": "system", "content": "You are a senior PR professional. Write press releases that are newsworthy, factual, and aligned with journalistic standards. Include strong quotes and clear angles."},
{"role": "user", "content": f"Company: {company_profile}\nTarget media: {target_media}\nKey messages: {key_messages}\nAnnouncement:\n{announcement_details}\n\nWrite press release."}
]
}
)
return response.json()["choices"][0]["message"]["content"]
announcement = """
Company: TechFlow Inc.
News: Series C funding of $125M led by Sequoia Capital
Previous rounds: $15M Series A (2023), $45M Series B (2024)
Use of funds: International expansion, AI product development, hiring 200 engineers
Current valuation: $850M
CEO quote: "This funding validates our vision of democratizing enterprise AI"
"""
profile = "B2B SaaS company, 350 employees, HQ San Francisco, customers include 3 Fortune 500 companies"
media = "TechCrunch, Forbes, WSJ Tech, Reuters"
messages = "Market validation, growth trajectory, enterprise AI leadership, job creation"
release = generate_press_release(announcement, profile, media, messages)
Rapidly draft crisis responses that balance transparency, legal protection, and stakeholder reassurance.
def draft_crisis_response(crisis_description, stakeholder_groups, legal_constraints, company_position):
response = requests.post(
"https://tokenease.io/v1/chat/completions",
headers={"Authorization": "Bearer YOUR_TOKENEASE_API_KEY"},
json={
"model": "qwen3-235b",
"messages": [
{"role": "system", "content": "Draft crisis communications. Balance empathy, accountability, and legal caution. Prepare versions for different stakeholders with appropriate tone and detail levels."},
{"role": "user", "content": f"Position: {company_position}\nLegal: {legal_constraints}\nCrisis: {crisis_description}\nStakeholders: {stakeholder_groups}\n\nDraft responses."}
]
}
)
return response.json()["choices"][0]["message"]["content"]
crisis = "Data breach affecting 2.3M customer records including names, emails, and partial credit card numbers. Discovered Aug 18. Law enforcement notified. No evidence of misuse yet."
stakeholders = "Customers, regulators, media, employees, investors"
legal = "Cannot admit liability. Must note ongoing investigation. Include regulatory notification timeline."
position = "Taking full responsibility for remediation. Enhancing security measures. Offering identity protection."
draft = draft_crisis_response(crisis, stakeholders, legal, position)
Analyze media clips and coverage summaries to track sentiment, identify key narratives, and measure message penetration.
def analyze_media_coverage(coverage_samples, campaign_messages, competitor_mentions):
response = requests.post(
"https://tokenease.io/v1/chat/completions",
headers={"Authorization": "Bearer YOUR_TOKENEASE_API_KEY"},
json={
"model": "glm-4-plus",
"messages": [
{"role": "system", "content": "Analyze media coverage for PR campaigns. Assess tone, message penetration, narrative control, and compare against competitor coverage. Identify gaps and opportunities."},
{"role": "user", "content": f"Competitors: {competitor_mentions}\nCampaign messages: {campaign_messages}\nCoverage:\n{coverage_samples}\n\nAnalyze coverage."}
]
}
)
return response.json()["choices"][0]["message"]["content"]
samples = """
TechCrunch: "TechFlow raises $125M, but faces growing competition from established players"
Forbes: "Enterprise AI heating up: TechFlow's funding signals market maturation"
WSJ: "Investors bullish on B2B AI despite regulatory concerns over data privacy"
Reuters: "TechFlow CEO: 'We are building the future of work' amid AI talent wars"
"""
messages = "Enterprise AI leader, rapid growth, international expansion, responsible AI"
competitors = "Mentioned alongside OpenAI, Anthropic, and Cohere in coverage"
analysis = analyze_media_coverage(samples, messages, competitors)
Adapt core messages for different stakeholder groups while maintaining consistent brand voice and key facts.
def personalize_for_stakeholder(core_message, stakeholder_type, relationship_history, communication_goal):
response = requests.post(
"https://tokenease.io/v1/chat/completions",
headers={"Authorization": "Bearer YOUR_TOKENEASE_API_KEY"},
json={
"model": "kimi-k2",
"messages": [
{"role": "system", "content": "Adapt corporate communications for specific stakeholder groups. Maintain accuracy while adjusting tone, depth, and framing for audience relevance."},
{"role": "user", "content": f"Goal: {communication_goal}\nHistory: {relationship_history}\nStakeholder: {stakeholder_type}\nMessage:\n{core_message}\n\nAdapt communication."}
]
}
)
return response.json()["choices"][0]["message"]["content"]
core = "Company is restructuring operations to focus on core products, resulting in 15% workforce reduction (200 positions). Cost savings will fund R&D and market expansion."
history = "Investor since Series A, previously expressed concern about burn rate"
goal = "Maintain confidence while being transparent about challenges"
adapted = personalize_for_stakeholder(core, "early-stage investor", history, goal)
Monitor social conversations for emerging issues and draft rapid, appropriate responses to contain negative sentiment.
def monitor_and_respond(social_mentions, brand_voice, severity_assessment, response_guidelines):
response = requests.post(
"https://tokenease.io/v1/chat/completions",
headers={"Authorization": "Bearer YOUR_TOKENEASE_API_KEY"},
json={
"model": "deepseek-v4",
"messages": [
{"role": "system", "content": "Monitor social media issues and draft responses. Assess escalation risk, recommend response timing, and write replies that de-escalate while protecting brand reputation."},
{"role": "user", "content": f"Guidelines: {response_guidelines}\nSeverity: {severity_assessment}\nVoice: {brand_voice}\nMentions:\n{social_mentions}\n\nAssess and draft responses."}
]
}
)
return response.json()["choices"][0]["message"]["content"]
mentions = """
@angrycustomer: "Your support team hung up on me after 45 minutes. Worst service ever. #boycott"
@techblogger: "Hearing multiple reports of TechFlow's new feature causing data sync issues"
@industryanalyst: "Interesting that TechFlow's competitors haven't had similar security incidents"
@employeeanon: "Morale is tanking after the layoff announcement. Leadership is out of touch."
"""
severity = "Medium-high: Support issue + product bug + employee sentiment. Potential for viral spread."
guidelines = "Respond within 2 hours to complaints. Acknowledge issues without admitting fault. Move detailed discussions to DMs."
monitor = monitor_and_respond(mentions, "helpful, accountable, human", severity, guidelines)
Draft keynote speeches, earnings call scripts, and internal communications that align with corporate messaging and audience expectations.
def write_executive_speech(speech_brief, audience_profile, key_themes, speaking_style):
response = requests.post(
"https://tokenease.io/v1/chat/completions",
headers={"Authorization": "Bearer YOUR_TOKENEASE_API_KEY"},
json={
"model": "qwen3-235b",
"messages": [
{"role": "system", "content": f"Write executive speeches in the style of {speaking_style}. Balance substance with storytelling. Include audience-appropriate anecdotes and clear calls to action."},
{"role": "user", "content": f"Style: {speaking_style}\nThemes: {key_themes}\nAudience: {audience_profile}\nBrief:\n{speech_brief}\n\nWrite speech."}
]
}
)
return response.json()["choices"][0]["message"]["content"]
brief = "All-hands meeting announcing Q3 results, celebrating product milestone, addressing restructuring concerns, rallying team for Q4"
audience = "350 employees, mixed reactions to recent layoffs, high-performing engineering team, sales team under pressure"
themes = "Resilience, innovation focus, customer impact, team appreciation, clear direction forward"
style = "Authentic, data-driven when needed, emotional at key moments, avoids corporate jargon"
speech = write_executive_speech(brief, audience, themes, style)
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