AI Presentation Generator: From Brief to PPTX in 5 Minutes

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.

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.

Project work process

1. Analytics — gather requirements, study existing presentations, choose tech stack.
2. Design — pipeline architecture, data schema, API specification.
3. Development — iterative implementation of modules, unit tests.
4. Testing — on real data, check content quality, generation speed.
5. 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.