Hemera is building an autonomous AI agent designed to streamline and optimize Twitter marketing for crypto-focused businesses. The tool targets crypto founders, growth marketers, and influencers, audiences that depend on constant, engaging, and personalized interaction on Twitter to grow their communities, share project updates, and drive engagement.
The problem: generic and inconsistent tweet generation
Hemera’s existing backend relied on simple GPT prompt engineering to analyze tweet history and generate content. While functional, this approach often fails to capture user-specific nuances, such as humor, technical tone, slang, or emoji usage, leading to generic or repetitive outputs.
For crypto founders and influencers, authenticity is everything. Inconsistent tone and “AI-like” phrasing makes tweets feel artificial, breaking user trust and reducing engagement.
The challenge was clear: build an AI tweet generator capable of capturing stylistic diversity and personality even from limited historical data, while ensuring scalability for multiple user personas.
The solution: an AI tweet generator for authentic social voice
GoML proposed a 4-week Proof of Concept (PoC) to build a backend personalization engine, an AI tweet generator that learns each user’s tone, style, and intent to create authentic, voice-consistent tweets. The module would serve as the backbone for Hemera’s autonomous Twitter agent, powering future integrations with its Telegram interface.
Automated user style extraction
Extracted user bios and tweet history to identify tone markers, sentiment, structure, frequency, hashtags, and emoji patterns. These insights formed the foundation for personality modeling and tone simulation.
LLM-powered tweet generation
Leveraged Claude 3.5 on Amazon Bedrock to generate tweets and replies that mirror the user’s communication style. The system ensures balanced creativity, contextual awareness, and authenticity across all outputs.
Reusable prompt framework
Designed modular, context-aware prompts adaptable to each user’s style and evolving crypto trends. Enabled future scalability and easier fine-tuning for new audiences.
Backend integration via FastAPI
Delivered the engine as a FastAPI-based service with JSON outputs, ready for seamless integration into Hemera’s backend and future Telegram workflows.
The impact: authentic and scalable tweet personalization
By implementing an AI tweet generator, Hemera achieved a significant leap in authenticity and personalization. The PoC established the groundwork for scalable, voice-consistent social automation, bridging the gap between AI creativity and human tone.
Key outcomes:
- 80% improvement in tone consistency across generated tweets
- 70% reduction in human editing effort
- Higher engagement on test tweets during UAT phase
Lessons for other organizations
Common pitfalls to avoid
- Over-reliance on prompt engineering without stylistic modeling
- Ignoring tone variation and audience personality differences
- Building AI tweet systems without modular prompts or APIs
Advice for teams facing similar challenges
- Begin with a focused PoC to validate tone consistency
- Use LLMs like Claude 3.5 for adaptive, context-aware generation
- Prioritize modular prompt frameworks for easy iteration and scaling
Want to build your own social posts generator?
Let GoML design your personalization engine, combining Gen AI, style modeling, and modular backend frameworks to help your brand tweet with authenticity, precision, and scale.
