Jarvis: How an AI Assistant in Slack Turns Chat Chaos into Ready Tasks in Trello

Where Important Thoughts Vanish During Team Chats

Every team shares the exact same problem. Information constantly scatters all over the place. Someone writes something in a public channel, someone else replies in DMs, and critical decisions get completely buried in the daily flood of Slack messages. Finding those loose ends later is nearly impossible. That is exactly why we at Skylex built Jarvis, a kind of invisible ai-assistant powered by artificial intelligence.

The bot simply sits in chats, listens, structures conversations, and generates ready-made suggestions for your task manager. Right now, the system works in a combination of Slack, Trello, and OpenAI. However, this logic can easily be extended to any other company services, such as setting up automatic ticket creation in Jira, syncing leads with HubSpot, or gathering a technical knowledge base in Notion.

How Jarvis Interacts with the Company Database

This assistant became truly useful because we tightly connected it to our internal database. It is no longer just a toy for sending out notifications.

When developers discuss technical matters in Slack, Jarvis automatically checks project data in our database. In practice, this ensures a continuous and fast exchange of information between teams.

Everyone sees the actual status of tasks, nobody bothers colleagues with redundant questions, and managers no longer need to piece reports together every evening to fill out tables manually.

How Exactly AI Breaks Conversations into Pieces

The main thing Jarvis does is convert a heap of regular messages into clean, structured data. The bot does not just grab every single word. It knows how to group chat messages by context based on two simple rules.

First, the analysis and processing of context do not happen automatically every 30 minutes, but strictly upon a direct request from a person in the chat (right now, our developer Danylo is finalizing and implementing this change into the system logic). This helps avoid mixing up morning stand-ups with evening bug reviews, allowing the assistant to be called exactly when the discussion is finished and the result needs to be recorded.

Second, the bot clearly reads thread structures. Any reply inside a thread automatically links to the main post, meaning technical details never drop out of the general context of the issue.

Code Logic and the Magic of Prompt Engineering

When the bot gathers a piece of chat history, it starts matching the retrieved context against the current task list. The logic is structured as follows: first, Jarvis imports and syncs active project cards from Trello, and then it begins working directly with our internal database.

A massive advantage of this approach is that current tasks in the database can come from entirely different origins. For instance, the system easily processes and compares tasks that a manager added manually into the database, even if they are not synced with Trello boards at all. Having gathered this full array of information (page IDs, descriptions, and current statuses), the script prepares the final data package to send to OpenAI.

The real secret lies in the prompt. We wrote a very strict instruction for the AI that forces it to think like a rigorous technical analyst. No creative freedom or hallucinations are allowed: there is a clear business logic backed by specific examples.

The bot simply compares the text from the chat with the technical description of tasks in the database. Of course, every such API request costs money, so we optimized the data volume and selected a model, specifically GPT-4o, to keep it cost-effective.
 

What a Manager Actually Sees in Slack

After analyzing the conversation, Jarvis displays neat, interactive cards of three types in Slack. If a developer mentions that a feature is ready, the bot offers a one-click option to move the task to Done status.

If important details slip into the chat, like a Figma link or a bug description, the AI suggests attaching it as a comment to the corresponding Trello card.

And if the team simply had a long discussion, the bot creates a short summary of the conversation so others do not have to read through hundreds of messages and waste time.

Each suggestion includes buttons with direct links to the relevant thread and the Trello card, plus a short explanation of why the AI made that decision.

A manager only needs to click one button, and everything updates via the API automatically. If the bot makes a mistake somewhere, there is an "Ignore" button: you click it, and the assistant will no longer disturb you regarding that specific context.

Conclusion

Right now, the internet is flooded with generic information and theoretical discussions about the capabilities of artificial intelligence. However, we at Skylex decided not to limit ourselves to theory, so we just took action and developed a real digital tool that handles its tasks daily and shows excellent practical results.

With such a tool, your Slack turns into a smart ecosystem. Not a single useful thought from your developers gets lost in chats, and every management decision finds its place in the project automatically, completely freeing management from routine. Furthermore, we see a clear perspective for integrating Jarvis directly into client communication chats: environments that always generate a large volume of operational tasks and where an AI assistant will help instantly record agreements in real time.