AI-based social media management system
A brand with 40 regional accounts across five social networks (Instagram, TikTok, VK, Telegram, YouTube) spends 12 FTE on content management. Each account requires 15–20 posts per week in various formats. Without AI, this work is not scalable—either quality declines or costs increase linearly.
Content generation and adaptation
Pipeline: brief → finished post
The LLM orchestrator (GPT-4o or Claude 3.5 Sonnet) accepts the following brief: product, audience, tone of voice, platform, and post goal (engagement, conversion, awareness). Output: post text + hashtags + image prompt.
Automatic platform adaptation: Instagram uses an emotional narrative with hashtags, Telegram uses an analytical approach without hashtags, and TikTok uses a hook in the first three words. Fine-tuning using a corpus of successful brand posts (500+ examples via QLoRA on Mistral-7B) ensures better brand voice consistency than zero-shot GPT-4o: ROUGE-2 0.41 vs. 0.28, brand compliance score by the team: 4.3/5 vs. 3.6/5.
Image generation pipeline
DALL-E 3 / Flux via API + post-processing: automatic brand overlay (logo, color, font) via Pillow/ImageMagick. For product images: Stable Diffusion with IP-Adapter (preserves product appearance) + ControlNet (composition control). A/B test on 12,000 impressions: AI visuals vs. designer — CTR 2.8% vs. 2.6%, statistically insignificant, but saving 120 hours/month.
Engagement analytics
What does reach predict?
Fine-tuned XGBoost on 18 months of historical posts: features—content type (reel/static/carousel), post time, text length, presence of CTA, hashtags (embedding via sentence-transformers), topic (BERTopic cluster). RMSE reach: 23% of the median reach—sufficient for ranking content variants before publication.
Best posting time recommender: account audience → historical peak activity windows → personalized posting schedule. Engagement rate increases by 18–24% thanks to timing optimization—this is often the fastest win without changing content.
Thematic analysis of comments
BERTopic + sentiment analysis on the comment stream: automatic summary of "what the audience is saying" for the week. Highlighting negative clusters (complaints, product questions) for escalation to support. For an account with 180,000 subscribers, processing 4,000 comments/week takes 8 minutes vs. 6 hours manually.
Automated publishing and monitoring
Scheduling and multi-channel publishing
Integrations via official APIs: Meta Graph API, VK API, Telegram Bot API, YouTube Data API. A publication queue with dependencies (first publish to Telegram, then to Instagram after 2 hours). Celery + Redis for the job queue. Automatic retry on rate limit errors with exponential backoff.
Brand Monitoring and Competitors
Brand mention monitoring: RSS + social API + Brandwatch/Mention API → sentiment classifier (fine-tuned RuBERT for Russian-language content). Alert for spikes in negativity: >50 negative mentions in 2 hours → Telegram notification to the team.
Competitive analysis: automatic collection of competitors' public posts → topic modeling → gap analysis (topics that competitors cover but we don't).
Influencer marketing
Influencer Scoring
Based on open data: engagement rate (likes + comments/followers), audience quality score (percentage of real followers through follower analysis), topic relevance (BERTopic topic-brand match), fake engagement detection (follower spike patterns, bot comments). The model ranks 500 candidates in 10 minutes, compared to three days of manual analysis.
ROI tracking
UTM tagging of unique links + assignment to the campaign → attribution model (last-click / data-driven Shapley). ROMI for each influencer: can be compared fairly.
Stack
| Компонент | Инструменты |
|---|---|
| LLM генерация | GPT-4o, Claude 3.5 Sonnet, Mistral fine-tuned |
| Image generation | DALL-E 3, Flux, Stable Diffusion + IP-Adapter |
| Аналитика | XGBoost, BERTopic, sentence-transformers |
| Scheduling | Celery, Redis, Meta/VK/Telegram API |
| Мониторинг | RuBERT, Brandwatch API |
Development timeline: 3–5 months for the core (generation + scheduling + basic analytics). Full toolset with influencer scoring and competitive monitoring: 5–8 months.