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.

Generative AI Development: From Prompt to Production API

We often receive a task "generate a product image" — on the surface it seems simple. But behind this lies a choice between dozens of models, configuring the inference pipeline, manually solving consistency issues, integrating into the product backend, and answering why the model generates hands with six fingers in staging but not in production. Let's break down the directions we work with.

Image Generation: From Prompt to Production API

The current landscape includes FLUX.1 [dev/schnell/pro] from Black Forest Labs and Stable Diffusion 3.5. FLUX.1 [schnell] takes 4 steps instead of 20–50 for SDXL — 5–12 times faster — while maintaining higher quality. On an A100 80GB — 1.2–1.8 s per 1024×1024 image at batch_size=4.

A typical deployment issue: FLUX.1 [dev] requires 24+ GB VRAM in fp16. On A10G 24GB it fits tightly; at batch_size>1 — OOM. Solution: torch_dtype=torch.bfloat16 + enable_model_cpu_offload() from diffusers, or quantization via bitsandbytes to NF4 — minimal quality drop, memory consumption drops to 12–14 GB.

ControlNet and IP-Adapter are key tools for production tasks where controllability is needed. ControlNet with Canny/Depth/Pose maps provides structural control. IP-Adapter (especially IP-Adapter-FaceID) allows transferring character identity to generations — this is the foundation for personalized content. More about ControlNet can be found on Wikipedia.

Case study: e-commerce photography. A retailer with 8000 SKUs needed lifestyle photos for each product. Pipeline: product segmentation (Segment Anything Model 2) → background removal → inpainting with FLUX.1 [dev] using product image as IP-Adapter reference → upscale via RealESRGAN_x4plus. The generation cost is negligible compared to professional photography, providing huge savings. Throughput — 200 images/hour on 2× A100. Our extensive experience from 30+ projects ensures we select the optimal model for your task — an evaluation can be obtained upfront.

Why Is Model Selection Only Half the Battle?

Fine-tuning for a Specific Style or Character

Dreambooth and LoRA are the standard for adapting to a specific visual style or object. LoRA trains in 2–4 hours on 20–30 reference images on a single A100. Rank 16–32 is usually sufficient for style; rank 64+ is needed for precise face reproduction.

A common mistake: training LoRA too long — the model overfits to references, losing the ability to vary. Sign: at cfg_scale=7, all images look like copy-paste of references. Solved by early stopping (usually 1500–2000 steps for 20 images) and prior_preservation_loss.

For deeper customization — full fine-tuning via diffusers + accelerate with FSDP on multiple GPUs. But that already takes 40–80 hours of training and requires a truly large dataset (1000+ images).

Comparison of Image Generation Approaches

Video Generation: Which Models Are Best?

Open-weight video models still lag behind commercial ones in stability and length. Wan2.1 is the best choice for self-hosting: 14B parameters, runs on 2× A100, delivers acceptable quality for short clips.

The main pain of video generation is temporal consistency: the character changes clothing color at the third second, objects "drift." Partial solution — generation with motion_bucket_id and noise_aug_strength in Stable Video Diffusion, or using I2V (image-to-video) instead of pure text-to-video. As noted in VideoPoet research, consistency is achieved by training on long sequences.

AnimateDiff remains a working tool for short loops and motion effects on top of SD/FLUX. Not Sora, but deployable locally and predictable.

Music and Audio Generation

AudioCraft from Meta (MusicGen + AudioGen) is a production-ready stack for music generation. musicgen-large (3.3B) generates 30 s of music in ~8 s on A100. Control via text prompt and melody conditioning — you can specify a melody by humming.

Stable Audio Open from Stability AI is an alternative with length up to 47 s, better structural control (intro/verse/chorus). Deployment is similar: diffusers + FastAPI.

For voice-over and dubbing — ElevenLabs API or self-hosted XTTS v2 (see Speech AI service). For sound design and foley — AudioGen.

3D Generation: Current Practical State

3D generation has not yet reached the same maturity as 2D. But for specific tasks, tools are already working:

TripoSG and Shap-E — text/image-to-3D. Shap-E from OpenAI generates simple 3D meshes in seconds, but geometry is rough. TripoSG gives more detailed results but requires post-processing (remeshing, UV unwrapping).

Wonder3D and Zero123++ — 3D reconstruction from a single image. They work by generating multi-views (6–8 views) and then 3D reconstruction via NeuS or instant-ngp.

Gaussian Splatting (3DGS) — not generation, but reconstruction from a series of photos/videos. For product cards and real estate it's already production: 50–200 photos → 3DGS model in 15–30 min on RTX 4090 → interactive 3D viewer in browser.

What Infrastructure Is Needed for Generative AI Deployment?

Critical for generative models:

• Task queue — Celery + Redis or Ray Serve. Synchronous HTTP for image generation is unacceptable with >5 concurrent requests.
• Caching — similar prompts yield similar results. Semantic cache via embeddings (faiss + sentence-transformers) can reduce GPU load by 20–40%.
• Quality monitoring — CLIP score for text-image alignment, FID for evaluating generation distribution. Integrate into MLflow or Weights & Biases.
• Storage — generated images immediately to S3/MinIO, not on the inference server disk.

What's Included in the Deliverables

We take the project turnkey — from model selection to deployment and monitoring. The result includes:

• Model (or API integration) with performance benchmarks (latency p99, throughput).
• Pipeline documentation (prompt engineering guide, model card, dependency versions).
• Integration with your backend (REST/gRPC, queues).
• Configured monitoring (dashboards, alerts for quality drift).
• Training workshop for the team (2–4 hours).
• Warranty support for 3 months after launch — as part of our quality certificate.

We have completed 30+ projects in generative AI — this gives us the right to guarantee results.

How Is the Generative AI Development Process Structured?

1. Analysis (1–2 days): audit of current architecture, clarification of use case, selection of models and success metrics. We evaluate the project free of charge.
2. Proof of Concept (1–3 weeks): quick prototype on your data — to see real quality, not blog demos.
3. Design (1–2 weeks): pipeline architecture, infrastructure (GPU cluster/API), A/B testing plan.
4. Implementation and fine-tuning (4–12 weeks): development, LoRA/full fine-tuning, integration with queue and cache.
5. Testing (1–2 weeks): load tests, metric validation, edge-case verification (negative scenarios).
6. Deployment and monitoring (1–2 weeks): production deployment, monitoring setup, documentation.

Timeline Estimates

Integration of a ready API (DALL·E 3, Midjourney API, Stability API) — 1–2 weeks. Self-hosted pipeline with fine-tuning — 6–12 weeks. Full platform with UI, queues and monitoring — 3–6 months. The specific cost is calculated individually after analyzing your scenario.

Contact us — order a consultation, and we will select the optimal architecture for your project. Get a preliminary cost and timeline estimate for free.