Autonomous Marketing Agents

How it works.

The system, step by step — each stage is an agent or service with a clear job and a hand-off to the next.

01 · Strategic Intent

The 'Director' Agent analyzes market trends, competitor data, and your KPIs to formulate a campaign strategy.

02 · Content Generation

Specialized 'Creator' Agents write copy, generate Midjourney assets, and slice video for specific platforms (LinkedIn vs TikTok).

03 · Execution & Posting

The 'Publisher' Agent interfaces with APIs to schedule and post content at algorithmically optimal times.

04 · Recursive Optimization

The 'Analyst' Agent reviews performance metrics and updates the Director's instructions for the next cycle.

Built for production, owned by you.

Proven frameworks, not thin wrappers. Open any capability for the technique, the stack and an implementation sketch.

Persona-Based Copywriting

LLMs fine-tuned on your specific brand voice and high-converting copywriting frameworks (AIDA, PAS).

Brand voice fine-tuning — Technique: Low-Rank Adaptation (LoRA) on Llama-3-70B

We do not rely on generic system prompts. We perform Supervised Fine-Tuning (SFT) on your historical high-performing content to bake your brand's lexicon, tone, and sentence structure directly into the model weights. We utilize RAG (Retrieval-Augmented Generation) to inject dynamic context (current offers, news) at inference time.

Stack: HuggingFace PEFT, Llama-3-70B, Pinecone (Context), LangSmith (Eval)

Visual asset generation

Automated generation of on-brand imagery using Stable Diffusion pipelines with ControlNet for consistency.

Consistent image generation pipeline — Technique: Stable Diffusion XL + ControlNet + IP-Adapter

Generating random AI art is easy; generating consistent brand assets is engineering. We utilize IP-Adapter to clone your product's visual identity and ControlNet to enforce rigid composition rules. This allows us to place your physical product into infinite synthetic environments without hallucinating details.

Stack: ComfyUI, SDXL Turbo, ControlNet Canny, IP-Adapter

Trend Jacking

Agents monitor Twitter/X trends and news APIs to autonomously draft relevant reactive content.

Real-time trend monitoring — Technique: Event-Driven Architecture + Semantic Similarity

Our 'Watcher' agents scrape X (Twitter) Trends, Google News, and Reddit API streams every 5 minutes. We use semantic embedding comparisons to identify trends that statistically overlap with your brand's content pillars. If a match is found (Cosine Similarity > 0.85), a draft is generated and pushed to Slack for one-click approval.

Stack: Apify, OpenAI Embeddings-v3, Slack Webhooks, Celery Workers

Comment & Engagement

Sentiment-aware bots that reply to comments to boost algorithmic engagement scores.

Engagement automation — Technique: Sentiment Analysis + RLHF Alignment

Social algorithms reward velocity of conversation. Our agents monitor your posts and reply to comments within 60 seconds. We use a multi-step chain: 1. Classify sentiment (Fan/Troll/Question). 2. Retrieve FAQ context if needed. 3. Generate a witty, on-brand reply that encourages a follow-up response, creating an infinite engagement loop.

Stack: LinkedIn API, GPT-4-Turbo, Sentiment Analysis, Rate Limiter

Typical stack: LangChain, AutoGPT, Stable Diffusion, OpenAI API, Pinecone (Memory)

From experiment to production.

We scope it on your stack, ship a measured first system, and run it against a baseline you can read.