Demo Day：融资路演实战

import json class PitchDeckScorer:    def __init__(self, company_name):        self.company_name = company_name        self.sections = {}        def evaluate_section(self, section_name, score, notes=""):        self.sections[section_name] = {'score': score, 'notes': notes}        def total_score(self):        if not self.sections:            return 0        total = sum(s['score'] for s in self.sections.values())        return round(total / len(self.sections), 1)        def investor_interest(self):        weights = {'problem': 1.5, 'solution': 1.5, 'market': 1.2,                   'team': 1.3, 'financials': 1.0, 'demo': 1.5,                   'traction': 1.4, 'competition': 0.8}        weighted_sum = 0        weight_total = 0        for name, data in self.sections.items():            w = weights.get(name, 1.0)            weighted_sum += data['score'] * w            weight_total += w        return round(weighted_sum / weight_total, 1) if weight_total else 0 class FundraisingAnalyzer:    def __init__(self, pre_valuation, raise_amount, interest_rate=0.08):        self.pre_valuation = pre_valuation        self.raise_amount = raise_amount        self.post_valuation = pre_valuation + raise_amount        self.investor_pct = raise_amount / self.post_valuation        self.founder_pct = 1 - self.investor_pct        self.rounds = []        def add_round(self, round_name, raise_amount, pre_valuation,                  liquidation_pref=1.0, participating=False, anti_dilution='weighted_avg'):        post_val = pre_valuation + raise_amount        new_investor_pct = raise_amount / post_val        self.rounds.append({            'round': round_name,            'raise_amount': raise_amount,            'pre_valuation': pre_valuation,            'post_valuation': post_val,            'investor_pct': round(new_investor_pct * 100, 2),            'liquidation_pref': liquidation_pref,            'participating': participating,            'anti_dilution': anti_dilution        })        def effective_ownership(self, exit_value):        founder_pct = self.founder_pct        for r in self.rounds:            founder_pct *= (1 - r['investor_pct'] / 100)        liquidation_total = sum(r['raise_amount'] * r['liquidation_pref'] for r in self.rounds)        if exit_value > liquidation_total:            remaining = exit_value - liquidation_total            founder_proceeds = remaining * founder_pct            for r in self.rounds:                if r['participating']:                    founder_proceeds -= remaining * (r['investor_pct'] / 100)        else:            founder_proceeds = 0        return {            'founder_pct': round(founder_pct * 100, 2),            'exit_value': exit_value,            'liquidation_total': round(liquidation_total, 2),            'founder_proceeds': round(max(founder_proceeds, 0), 2)        }        def compare_scenarios(self, scenarios, exit_values):        results = []        for sc in scenarios:            sc_results = []            for ev in exit_values:                sc_results.append({                    'exit_value': ev,                    'founder_proceeds': sc['analyzer'].effective_ownership(ev)['founder_proceeds']                })            results.append({'scenario': sc['name'], 'details': sc_results})        return results class QAPrepTool:    def __init__(self):        self.questions = []        def add_question(self, category, question, key_points, data_support=""):        self.questions.append({            'category': category,            'question': question,            'key_points': key_points,            'data_support': data_support        })        def prep_report(self):        categories = {}        for q in self.questions:            cat = q['category']            if cat not in categories:                categories[cat] = []            categories[cat].append(q)        return categories deck = PitchDeckScorer("AI客服创业公司")deck.evaluate_section('problem', 9, '客服人力成本高，痛点明确')deck.evaluate_section('solution', 8, 'AI多轮对话降本60%')deck.evaluate_section('market', 8, 'TAM 500亿')deck.evaluate_section('team', 7, '技术背景强，缺销售')deck.evaluate_section('financials', 7, '单位经济性待验证')deck.evaluate_section('demo', 9, '3秒回复复杂工单')deck.evaluate_section('traction', 8, '200家企业客户')deck.evaluate_section('competition', 7, '数据飞轮是壁垒') print(f"=== {deck.company_name} Pitch Deck评分 ===")print(f"各模块得分: {', '.join(f'{k}={v["score"]}' for k,v in deck.sections.items())}")print(f"综合得分: {deck.total_score()}/10")print(f"投资人兴趣指数: {deck.investor_interest()}/10") qa = QAPrepTool()qa.add_question('市场规模', 'TAM 500亿怎么算的？', 'IDC报告+自研模型', '2025年AI客服市场500亿CAGR 32%')qa.add_question('竞争壁垒', '大厂做了怎么办？', '行业语料数据飞轮', '200家客户积累独有行业数据')qa.add_question('单位经济性', 'CAC和LTV分别是多少？', 'CAC 2万LTV 12万比值6x', '回本周期4个月')qa.add_question('技术风险', '模型幻觉怎么处理？', 'RAG+人工审核双保险', '幻觉率<2%行业最低') report = qa.prep_report()print(f"\n=== Q&A准备报告 ===")for cat, qs in report.items():    print(f"\n[{cat}] ({len(qs)}个问题)")    for q in qs:        print(f"  Q: {q['question']}")        print(f"  要点: {q['key_points']} | 数据: {q['data_support']}") fa = FundraisingAnalyzer(3200, 800)fa.add_round('A轮', 2000, 12000, liquidation_pref=1.0, participating=False)fa.add_round('B轮', 5000, 40000, liquidation_pref=1.0, participating=False) print(f"\n=== 融资分析 ===")print(f"天使轮: 投前{fa.pre_valuation}万, 融资{fa.raise_amount}万")print(f"投后估值: {fa.post_valuation}万, 投资人占比: {fa.investor_pct*100:.1f}%") for ev in [5000, 10000, 30000, 80000]:    own = fa.effective_ownership(ev)    print(f"退出{ev}万: 创始人持股{own['founder_pct']}%, 创始人所得{own['founder_proceeds']}万")