Preparing a presentation for investors or a quarterly report takes 4–8 hours of a designer's work and 2–3 hours of an analyst's work. Clients pay design studios or freelancers significant amounts for each pitch deck. We automate this process: the AI system generates the structure, slide text, selects illustrations, and assembles a ready PPTX or Google Slides in 5–15 minutes based on a brief or data set. Time savings — up to 90%, budget savings — up to 80% compared to manual work. Our experience: over 50 projects in content automation, including report and presentation generation for large companies. We guarantee quality at the level of a senior designer. Contact us to discuss your case and get a consultation.
| Stage | Manual | AI System |
|---|---|---|
| Structure and script | 2-3 hours | 30 seconds |
| Writing texts | 3-4 hours | 1-2 minutes |
| Selecting illustrations | 1-2 hours | 30 seconds |
| Slide layout | 2-3 hours | 1-2 minutes |
| Total | 8-12 hours | 5-15 minutes |
How does the AI system generate presentation structure?
We use GPT-4o with a custom system prompt that turns the brief into a detailed structure: slide types, headline-conclusions, key points, and speaker notes. Each slide = one idea. The headline is a conclusion, not a topic. The opening is a hook. The closing is a specific next step. All this is returned as JSON for programmatic assembly.
from openai import AsyncOpenAI from dataclasses import dataclass import json client = AsyncOpenAI() @dataclass class PresentationBrief: title: str purpose: str # pitch, report, educational, sales, internal audience: str # investors, clients, board, employees, students slides_count: int # желаемое количество слайдов key_messages: list[str] data_points: list[dict] = None # {"metric": "...", "value": "...", "context": "..."} company_context: str = "" duration_minutes: int = 15 style: str = "professional" # professional, minimal, bold, corporate async def generate_presentation_structure(brief: PresentationBrief) -> dict: response = await client.chat.completions.create( model="gpt-4o", messages=[{ "role": "system", "content": f"""Ты — презентационный стратег и сторителлер. Создай структуру презентации для аудитории: {brief.audience}. Цель: {brief.purpose}. Длительность: {brief.duration_minutes} мин (~{brief.duration_minutes // brief.slides_count * 60} сек/слайд). ПРИНЦИПЫ: - Один слайд = одна идея - Заголовок слайда = вывод, а не тема ("Выручка выросла на 40%" вместо "Финансовые результаты") - Открытие: крючок — не "добрый день, меня зовут..." - Закрытие: конкретный следующий шаг для аудитории Для каждого слайда: - slide_type: title, problem, data, solution, case_study, timeline, cta - headline: заголовок-вывод - key_points: 2–3 тезиса - visual_suggestion: что изобразить - speaker_notes: 2–3 предложения для спикера Верни JSON: {{slides: [...]}}""" }, { "role": "user", "content": f""" Тема: {brief.title} Ключевые сообщения: {', '.join(brief.key_messages)} Данные: {json.dumps(brief.data_points or [], ensure_ascii=False)} Контекст компании: {brief.company_context} Количество слайдов: {brief.slides_count} """ }], response_format={"type": "json_object"} ) return json.loads(response.choices[0].message.content) Why do we use GPT-4o and python-pptx?
GPT-4o provides high-quality content and flexibility: you can set style, tone, audience. Python-pptx gives full control over layout — from kerning to SVG shapes. For data slides, we generate Chart.js specifications; for illustrations, prompts for DALL-E. The AI system is 20 times faster than manual work, and the cost of generating one presentation is 5–10 times lower.python-pptx documentation
async def generate_slide_visual( slide_type: str, headline: str, data_points: list = None, style: str = "professional" ) -> str: """Возвращаем либо промпт для DALL-E, либо тип chart для Chart.js""" CHART_SLIDES = {"data", "timeline", "comparison"} if slide_type in CHART_SLIDES and data_points: # Для слайдов с данными — генерируем chart spec response = await client.chat.completions.create( model="gpt-4o", messages=[{ "role": "system", "content": "Создай Chart.js конфигурацию для визуализации данных на слайде. Верни JSON с type, data, options." }, { "role": "user", "content": f"Данные: {json.dumps(data_points, ensure_ascii=False)}\nЗаголовок слайда: {headline}" }], response_format={"type": "json_object"} ) return json.loads(response.choices[0].message.content) # Для остальных — промпт для image generation style_map = { "professional": "clean corporate illustration, flat design, blue palette", "minimal": "minimalist line art, monochrome, white background", "bold": "bold graphic design, high contrast, modern typography" } return f"{headline}, {style_map.get(style, style_map['professional'])}, presentation slide visual, 16:9" Slide assembly via python-pptx: set 16:9 size, apply theme, fill headlines and content, add speaker notes.
from pptx import Presentation from pptx.util import Inches, Pt, Emu from pptx.dml.color import RGBColor from pptx.enum.text import PP_ALIGN import io class PresentationBuilder: def __init__(self, theme: dict): self.prs = Presentation() self.prs.slide_width = Emu(9144000) # 16:9 widescreen self.prs.slide_height = Emu(5143500) self.theme = theme def add_content_slide(self, headline: str, key_points: list[str], notes: str = "") -> None: layout = self.prs.slide_layouts[1] # Title and Content slide = self.prs.slides.add_slide(layout) # Заголовок title = slide.shapes.title title.text = headline title.text_frame.paragraphs[0].font.size = Pt(28) title.text_frame.paragraphs[0].font.color.rgb = RGBColor(*self.theme["primary"]) # Контент body = slide.placeholders[1] tf = body.text_frame tf.clear() for point in key_points: p = tf.add_paragraph() p.text = point p.font.size = Pt(18) p.level = 0 # Заметки спикера if notes: notes_slide = slide.notes_slide notes_slide.notes_text_frame.text = notes def save(self) -> bytes: buf = io.BytesIO() self.prs.save(buf) return buf.getvalue() What does the full pipeline look like?
A single asynchronous function: structure → visuals (in parallel) → assembly.
async def create_presentation(brief: PresentationBrief) -> bytes: # 1. Генерируем структуру structure = await generate_presentation_structure(brief) # 2. Генерируем визуалы параллельно visual_tasks = [ generate_slide_visual(s["slide_type"], s["headline"], brief.data_points, brief.style) for s in structure["slides"] ] import asyncio visuals = await asyncio.gather(*visual_tasks) # 3. Собираем PPTX builder = PresentationBuilder(theme={"primary": (67, 97, 238)}) for slide_data, visual in zip(structure["slides"], visuals): builder.add_content_slide( headline=slide_data["headline"], key_points=slide_data["key_points"], notes=slide_data.get("speaker_notes", "") ) return builder.save() What is included in the AI generator development?
- Analysis and design: review of your business case, presentation types, templates, integrations.
- Module development: structure generation, content, visuals, PPTX/Google Slides assembly.
- Integration with data sources: BI systems (Metabase, Grafana), Notion, Confluence, API.
- Template customization: corporate style, color schemes, fonts.
- Team training: documentation, code review, usage workshop.
- Warranty support: 6 months after launch.
| Model | Generation speed | Content quality | Token cost |
|---|---|---|---|
| GPT-4o | 10–20 slides/min | High | $0.01/1K tokens |
| Claude 3.5 | 8–15 slides/min | High | $0.015/1K tokens |
| LLaMA 3 (70B) | 5–10 slides/min | Medium | $0.003/1K tokens |
Typical mistakes in presentation automation
- Data hallucinations: AI may invent numbers. Solution — include only verified data points in the context.
- Repetitive phrasing: without proper few-shot, models start copying style. Solution — diverse examples.
- Slide overload: one idea per slide, no more than three points.
- Brand neglect: check colors and fonts after generation.
Project work process
- Analytics — gather requirements, study existing presentations, choose tech stack.
- Design — pipeline architecture, data schema, API specification.
- Development — iterative implementation of modules, unit tests.
- Testing — on real data, check content quality, generation speed.
- Deployment — deploy on your infrastructure or cloud, configure CI/CD.
Timeline and scope
- MVP (PPTX generator with fixed template) — 2–3 weeks.
- Platform (Google Slides, template database, auto-scheduling, user dashboard) — 6–8 weeks.
- Customization (additional data sources, complex visualizations, RAG over corporate knowledge base) — estimated individually.
Development cost is calculated individually after an audit of your processes. Our engineers are certified in AWS and Google Cloud, with 5 years in AI solutions. Save up to $3,200 per year on presentation creation — get a consultation, tell us about your task, and we will offer the optimal solution.