InboxSage
Emails Done Right — Fast, Smart, Yours.
Created on 28th May 2025
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InboxSage
Emails Done Right — Fast, Smart, Yours.
The problem InboxSage solves
💼 The Problem InboxSage Solves
Email Overload Is Real
In today’s digital world, professionals receive hundreds of emails daily — newsletters, meeting threads, client updates, promotions, and more. Important messages often get buried, missed, or delayed. Managing this flood of communication becomes a time-consuming, error-prone task.
✅ What InboxSage Helps You Do
✨ 1. Cut Through the Noise
InboxSage filters your inbox for Important mails. Whether it’s client emails, project updates, or follow-ups, it surfaces what matters most — automatically.
📄 2. Summarize
Long email lists InboxSage instantly provides concise summaries so you don’t have to scroll through every message. Stay informed in seconds, not minutes.
🧠 3. Reply Smarter, Faster
With AI-generated, context-aware reply suggestions, InboxSage helps you respond faster — while maintaining your tone, professionalism, and intent.
⚙️ 4. Customize to Fit Your Workflow
Using simple prompts, you define what’s “important.” InboxSage adapts to your needs — not the other way around.
🚀 Who Is It For?
- Busy professionals juggling multiple projects
- Executives managing high-volume communication
- Customer support teams needing fast, relevant responses
- Freelancers and consultants who want more control over their client emails
- Anyone aiming for inbox zero — without the burnout
Challenges I ran into
🐛 Challenges I Ran Into
📌 Challenge: Accurate Email Prioritization with User Prompts
One of the biggest hurdles in building InboxSage was creating a system that could reliably prioritize emails based on user-defined prompts. Initially, the AI often misunderstood context or applied filters too broadly — causing irrelevant emails to be flagged as "important" or missing critical ones.
🛠️ Solution: Iterative Prompt Tuning
To solve this, I fine-tuned the prompt-processing logic and added support for more nuanced conditions (e.g., sender, keyword, sentiment, thread history). This helped the system better understand user intent and provide more accurate prioritization.
Additionally, I introduced prompt presets for common roles like "Project Manager" or "Sales Rep" to give new users a head start and improve the first-time experience.
⚙️ Challenge: Choosing the Right LLM and Temperature Settings
Another technical hurdle was selecting the right language model and tuning the temperature for different tasks like summarization and reply generation. Higher temperatures made replies too creative and inconsistent, while lower settings made them too robotic.
✅ Solution: Task-Specific Temperature Control
I ended up using task-specific temperature settings — for example:
- Lower temperature (0.3–0.5) for summarizing and prioritizing (for consistency and accuracy)
- Slightly higher temperature (0.6–0.7) for generating human-like replies (to sound more natural)
This approach helped strike the right balance between reliability and tone personalization.
💡 Takeaway
User-defined prompts and AI-generated content are powerful tools — but they require careful calibration of both model behavior and user experience. Choosing the right model settings was as crucial as the logic behind the app.
Technologies used
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