We are a team of AI engineers. A brand came to us with 40 regional accounts across 5 social networks (Instagram, TikTok, VK, Telegram, YouTube). Each account requires 15–20 posts per week in different formats. Manually, that's 12 FTEs — impossible to scale without sacrificing quality or ballooning costs. Without AI, such a load is unmanageable. We offer a turnkey solution: from content generation to analytics and moderation. We'll evaluate your project and show how to automate SMM without losing quality.
Content Generation and Adaptation
Pipeline: brief → ready post
The LLM orchestrator (GPT-4o or Claude 3.5 Sonnet) takes a brief: product, audience, tone of voice, platform, post goal (engagement, conversion, awareness). Output: post text + hashtags + image prompt.
Platform adaptation is automatic: Instagram — emotional narrative with hashtags, Telegram — analytical, no hashtags, TikTok — hook in the first 3 words. Fine-tuning on a corpus of 500+ successful brand posts (via QLoRA on Mistral-7B) ensures brand voice compliance better than zero-shot GPT-4o: ROUGE-2 0.41 vs. 0.28, brand compliance team rating 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.
What predicts reach?
Fine-tuned XGBoost on 18 months of historical posts: features — content type (reel/static/carousel), posting time, text length, presence of CTA, hashtags (embedding via sentence-transformers), topic (BERTopic clusters). RMSE reach: 23% of median reach — enough to rank content variants before publishing.
Best posting time recommender: audience per account → historical windows of maximum activity → personalized posting schedule. Engagement rate increase of 18–24% from timing optimization alone — often the quickest win without changing content.
Thematic comment analysis
BERTopic + sentiment analysis on the comment stream: automatic weekly summary "what the audience is saying." Detection of negative clusters (complaints, product questions) for escalation to support. On an account with 180K subscribers: processing 4,000 comments/week in 8 minutes vs. 6 hours manually.
How do we automate posting?
Integrations via official APIs: Meta Graph API, VK API, Telegram Bot API, YouTube Data API. Post queue with dependencies (publish on Telegram first, then Instagram 2 hours later). Celery + Redis for job queue. Automatic retry on rate limit errors with exponential backoff.
Brand monitoring and competitors
Brand mentions monitoring: RSS + social APIs + Brandwatch/Mention API → sentiment classifier (fine-tuned RuBERT for Russian content). Alert on negative spikes: >50 negative mentions in 2 hours → Telegram notification to the team.
Competitor analysis: automatic collection of competitors' public posts → topic modeling → gap analysis (topics competitors cover but we don't).
Influencer marketing
Influencer scoring
From public data: engagement rate (likes+comments/followers), audience quality score (percentage of real followers via follower analysis), topic relevance (BERTopic overlap with brand), fake engagement detection (spike patterns in followers, bot comments). The model ranks 500 candidates in 10 minutes — vs. 3 days of manual analysis.
ROI tracking
UTM-marked unique links + campaign attribution → attribution model (last-click / data-driven Shapley). ROMI per influencer: enables honest comparison.
What's included in the work
- Audit of current SMM process and infrastructure
- Design of generation, analytics, and monitoring pipelines
- Implementation using the specified stack
- Integration with your accounts and APIs
- System documentation
- Training your team on dashboards and reports
- Support and refinements for 3 months after launch
Implementation stages
- Analytics — collect historical data, define metrics, identify bottlenecks.
- Design — pipeline architecture, stack, customizations for brand specifics.
- Implementation — write code, fine-tune models, set up integrations.
- Testing — A/B tests of content, load testing of scheduling, analytics accuracy checks.
- Deployment — go live, monitor first weeks, adjust.
Each stage ends with a report and demo.
Stack
| Component | Tools |
|---|---|
| LLM generation | GPT-4o, Claude 3.5 Sonnet, Mistral fine-tuned |
| Image generation | DALL-E 3, Flux, Stable Diffusion + IP-Adapter |
| Analytics | XGBoost, BERTopic, sentence-transformers |
| Scheduling | Celery, Redis, Meta/VK/Telegram API |
| Monitoring | RuBERT, Brandwatch API |
AI vs. Manual Management
| Criterion | Manual Management | AI Management |
|---|---|---|
| Time per post (creation + adaptation) | 2–3 hours | 5–10 minutes |
| Audience reach (prediction accuracy) | Intuition | RMSE 23% |
| Feedback processing | 6 hours/week | 8 minutes/week |
| Scaling to new accounts | +1 FTE per account | Unlimited via API |
Why choose AI management?
We have implemented similar solutions for 15+ brands. Expertise in NLP, Computer Vision, and MLOps — from fine-tuning to production-grade inference. We guarantee transparency: you always see how decisions are made. Contact us for a preliminary assessment of your project. Get a consultation on AI management implementation.
Technical stack details
- LLM: GPT-4o (128K context, 8K tokens per post), Claude 3.5 Sonnet (200K context), Mistral-7B fine-tuned via QLoRA (INT4).
- Image: DALL-E 3 (resolution 1024x1024), Flux (fast variant), Inference API + Pillow for post-processing.
- MLOps: MLflow for experiment tracking, vLLM/TGI for inference, batch inference via Ray.
- Vector DB: pgvector for similar historical post search.
- Hardware: 2x NVIDIA A10G for inference, 1x A100 for training.