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.
Industry AI Solutions: Healthcare, Finance, Retail, Manufacturing
We encounter the same pain points: a general text model doesn’t distinguish medical nomenclature, and a standard object detector confuses “weld seam scratch” with “casing scratch.” Each time these are different defects with different consequences. To avoid this, we build industry-specific solutions on top of general methods, but with deep domain knowledge — from regulatory requirements to data specifics. Over 5 years, we have completed 80+ projects in fintech, healthcare, retail, and manufacturing, and none were without adaptation to a specific business case.
Healthcare: Regulatory Maze and Data Governance
Medical AI differs not in technical algorithms but in a compliance-first approach. Depending on the country of application, the model may be a Class II or III medical device requiring clinical trials (FDA, CE MDR, GOST R). We ensure compliance with these standards at the architecture stage — fixing them post-factum is 10× more expensive.
Medical imaging. Detection on X‑rays, CT, MRI is a mature area. Models on ResNet, EfficientNet, SegFormer achieve AUC 0.94–0.97 on standard tasks (pneumonia on CXR, polyps on colonoscopy). Key issue is generalization: a model trained on data from one scanner manufacturer degrades on another due to differences in preprocessing and artifacts. Solution: domain adaptation via MONAI (Medical Open Network for AI) from NVIDIA, which includes DICOM loading, 3D augmentation, and confidence calibration. TotalSegmentator — for automatic segmentation of 117 structures on CT, production‑ready, Apache 2.0 license.
Clinical NLP. Extracting structured information from clinical records: diagnoses (ICD‑10/11), prescriptions, dates, indicators. medspaCy, scispaCy, MedCAT — specialized NLP libraries with ontologies (SNOMED‑CT, UMLS). Fine‑tuning BioBERT or ClinicalBERT on our data yields F1 0.85–0.92 on NER tasks versus F1 0.65–0.72 for general BERT. We verified this on a project with a regional oncology center — cancer stage extraction accuracy increased by 23%.
Clinical decision support. LLM assistants for clinical decision support are a regulatory gray area. We use an RAG system on top of clinical guidelines (UpToDate, local protocols) with explicit citation for each statement. The model does not diagnose but helps find relevant protocols. Stack: LlamaIndex + pgvector + pubmedbert-base-embeddings + Llama Guard for safety. Data in DICOM/HL7 FHIR, on‑premise deployment mandatory.
Deliverables in a Healthcare Project
- Data audit and regulatory mapping (FDA/CE/GOST)
- Architecture selection based on medical device type
- Model development and validation (AUC, sensitivity, specificity)
- Integration with PACS/EHR (HL7 FHIR)
- Preparation of documentation for CE marking (if required)
- Staff training on model usage
Finance: How to Ensure Interpretability of a Scoring Model under Basel IV?
The financial sector is one of the most mature in applying ML, but regulation is maximal. Every model affecting credit decisions falls under Basel IV, EU AI Act, GDPR Article 22. We deliver AI solutions for fintech that satisfy these requirements — in a project for a top‑10 bank we deployed a scoring model where each record required SHAP explanations.
Credit scoring. Gradient boosting (LightGBM, XGBoost) dominates. Neural networks yield +0.5–2% AUC but lose interpretability. Standard: LightGBM + SHAP to explain each decision. Fairness checking is mandatory: Fairlearn or aif360 for auditing disparate impact on protected attributes (age, gender). The default class is 1–5% — with an imbalance of 1:30, a model with 97% accuracy may have recall 0.2. Solution: focal loss, class_weight='balanced', SMOTE + careful validation. In one fintech scoring project, the model reduced credit losses by $2.1 million annually.
Algorithmic trading and risk management. LSTM and Transformer for price forecasting are popular but unstable in production due to non‑stationarity of financial series. A more robust approach: ML for signal generation (classification: up/down over horizon N) with traditional portfolio optimization on top. Backtesting via Zipline‑Reloaded, vectorbt, QuantLib. Proper backtesting is critical — look‑ahead bias kills results. We guarantee a clean experiment: all data at signal time is available in real time.
AML (Anti‑Money Laundering). Graph Neural Networks for analyzing transaction networks is an actively developing area. PyG, DGL for GNN. Task: detect suspicious patterns in transaction graphs (layering, structuring). Recall is more critical than precision — better 10 false alarms than miss one money laundering. In a project for a large payment service, we increased recall by 18% without increasing false positive rate.
Deliverables in a Financial Project
- Data audit and regulatory requirements (Basel, EU AI Act)
- Model selection and explainability (SHAP, LIME)
- Fairness check and bias mitigation
- Integration with core banking / trading systems
- Documentation and compliance reporting
- Model drift monitoring and retraining
Retail and e‑commerce: Recommendation Systems and Demand Forecasting
Recommendation systems. Current architectural standard: two‑tower model for retrieval + ranking with cross‑features. TensorFlow Recommenders or Merlin from NVIDIA for GPU‑accelerated feature processing. For small catalogs (<100k items), LightFM is sufficient. A common mistake is training on implicit feedback without accounting for position bias. Solution: IPW (Inverse Propensity Weighting) or randomized logging on a portion of traffic. Development time for a basic recommendation system is 4–8 weeks, including A/B test.
Demand forecasting and inventory optimization. Hierarchical forecasting: SKU → category → store → region. HierarchicalForecast from Nixtla automatically reconciles forecasts across levels. TFT or N‑HiTS for base forecast, gradient boosting for adjustment on exogenous factors (promotions, weather, events). One retail project led to a 15% reduction in stock‑outs due to precise promotion calibration.
Visual search and size compatibility. CLIP embeddings for image search — deploy in 2–3 weeks: clip‑ViT‑B‑32 or clip‑ViT‑L‑14, Faiss or Qdrant index, REST API. For size recommendation — specific models on return data and reviews with fit indication.
Deliverables in a Retail Project
- Analysis of transactions, products, customers data
- Architecture selection (collaborative / content‑based / hybrid)
- Development and evaluation (NDCG, recall@k, MRR)
- A/B test and business impact monitoring
- Versioning and model retraining support
Manufacturing: Quality Inspection and Predictive Maintenance
Quality control and defect detection. CV models for product inspection are one of the most mature industry tasks. YOLOv10 for defect detection, SegFormer for segmentation. Specifics: class imbalance (defects are rare), high recall requirement (missing a defect is worse than false alarm). Typical dataset: 500–2000 defect images + 500–1000 normal. Few‑shot learning via DINO or SAM 2 works with 50–100 annotated examples. We gained experience on an electronics production line — recall 0.95 at FPR 0.03. A predictive maintenance deployment saved a manufacturing client $500,000 per year in unplanned downtime.
Predictive maintenance. Vibration sensors, current sensors, thermocouples → feature extraction → anomaly or mode classification. Models: LSTM‑AE for unsupervised, LightGBM for supervised (if failure history is available). Integration with SCADA/OPC‑UA via opcua-asyncio or MQTT. Key metric: False Negative Rate — a missed pre‑failure is more costly than a false alarm. Threshold tuned to business cost of each error type. Timeline: 3 to 6 months to production.
Digital twin and simulation. Surrogate models — ML models replacing expensive physical simulation. If a CFD simulation takes 6 hours and a surrogate (trained on 10,000 simulations) takes 0.01 seconds, that's 2,000,000× speedup for optimization. SALib for sensitivity analysis, botorch for Bayesian optimization on top of surrogate.
Deliverables in a Manufacturing Project
- Sensor / image data audit
- Model selection for task (CV / time series / vibro)
- Pipeline development (ETL, feature engineering, training)
- Deployment on Edge / on‑premise
- Model monitoring and retraining
General Principles of Industry AI
Regardless of industry, there are patterns that work everywhere. Data matters more than architecture. In healthcare, 1000 quality labeled images are better than 100,000 poor ones. In manufacturing, 200 real defect examples are more valuable than 10,000 synthetic ones. Compliance‑first design — regulatory requirements are easier to embed into architecture from the start than to add later. Logging, explainability, versioning from day one. Domain expert on the team — an ML engineer without domain knowledge does slowly and error‑prone what an ML engineer plus a doctor/financier/technologist does quickly and correctly.
We guarantee certification to customer requirements (ISO 13485, SOC 2, GDPR) and provide full model documentation (model card, datasheet, compliance report). Our experience: 10,000+ engineering hours and 80+ projects.
Work Process for an Industry AI Solution
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Domain immersion (2–3 days) — interviews with experts, studying regulatory requirements, auditing available data.
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MVP design (1–2 weeks) — stack and architecture selection, feasibility assessment.
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Development and validation (from 4 weeks to 6 months depending on industry) — model training, testing, compliance.
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Integration and deployment (1–4 weeks) — on‑premise / cloud / edge, documentation, staff training.
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Support and monitoring — model drift, retraining, SLA.
Estimated timelines:
| Type of Solution |
Minimum Time |
Full Cycle with Compliance |
| Retail recommendation |
4–8 weeks |
3–6 months |
| Credit scoring |
6–12 weeks |
6–12 months |
| Medical imaging |
12–24 weeks |
12–24 months (with CE) |
| Predictive maintenance |
8–16 weeks |
3–6 months |
Cost is calculated individually for each project. Get a consultation — we will evaluate your dataset, regulatory map, and business goals.
Why Choose Our Industry AI Solutions?
-
80+ completed projects in fintech, healthcare, retail, and manufacturing.
- 5 years on the market — proven experience with compliance and deployment.
- Quality guarantee: we ensure target metrics (AUC, recall, latency p99) and provide full documentation.
- Licensed technologies: PyTorch, MONAI, LightGBM, Qdrant — we use open‑source with commercially safe licenses.
- Flexibility: we work as a contractor or as an extension of your team.
Contact us for a free data audit and consultation. Request a proposal with a detailed work plan. We will discuss your task and prepare a commercial proposal.
