As someone who’s spent years working as a backend engineer, I’m no stranger to the chaotic rhythms of modern workflows. My career has been one Slack message, one Jira ticket, and one task board at a time, toggling between tools like a juggler with too many balls in the air. Slack became my central hub, a digital office, but I found myself constantly bouncing out of it to other platforms to answer questions, analyze data, or perform mundane tasks. This context-switching was draining—what seemed like small interruptions quickly added up, eroding my focus and reducing productivity.
That’s where the idea for GptPanda was born. Instead of going to different platforms for answers, insights, and assistance, what if AI could simply live in Slack itself? What if I didn’t have to break my flow to access the insights I needed?
The Problem: Context-Switching Kills Productivity
In today’s fast-paced digital landscape, efficiency is everything. Teams, especially in high-intensity fields like engineering, marketing, and customer support, can’t afford to lose precious minutes jumping between applications. I knew there had to be a better way to use AI directly in Slack. And as I started exploring, it became clear that the solution could change the way teams worked together, streamlining processes, improving focus, and reducing friction.
With so many of us already depending on ChatGPT for everything from coding support to research summaries, the potential for an integrated Slack tool became undeniable. The goal was to create an AI assistant that would feel like a natural part of every conversation, not just another disconnected app.
Taking the Leap: Joining the GptPanda Team
Around this time, I was invited to join a team working on an AI-powered Slack app—GptPanda. It was exactly the kind of solution I had imagined, and I couldn’t pass up the chance to help build it. The vision was simple: bring ChatGPT into Slack at scale, seamlessly blending AI assistance with Slack’s natural workflow.
GptPanda would allow users to perform essential AI tasks—answering questions, summarizing conversations, brainstorming ideas, and organizing information—all without leaving Slack. For me, joining the GptPanda team wasn’t just about building a tool; it was about redefining workplace productivity.
Building GptPanda: The Power of Seamless Integration
One of the first things we tackled was making sure GptPanda was easy to use for teams of all sizes. With the freemium model, anyone could access the essential features at no cost,
giving users a chance to experience how embedded AI could transform their workflows. However, we also wanted to provide more advanced capabilities for power users, so we created a premium version at just $1 per user per month. This upgrade unlocks features like contextual memory and unlimited requests, which are key for teams that want to dive deeper with AI.
But the true magic lies in the contextual memory system we developed. Here’s a snippet of the code that powers this feature:
```python
async def join_all_public_channels(bot_token: str) -> None:
headers = {"Authorization": f"Bearer {bot_token}"}
async with httpx.AsyncClient() as client:
response = await client.get(
"https://slack.com/api/conversations.list",
headers=headers,
params={"types": "public_channel"},
)
result = response.json()
channels = result.get("channels", [])
tasks = []
for channel in channels:
async with async_session_maker() as session:
task = asyncio.create_task(
join_slack_channel(session, channel["id"], channel["context_team_id"], bot_token),
)
tasks.append(task)
tasks.append(
asyncio.create_task(
send_welcome_message(
channels_id=await get_team_members_from_workspace(bot_token, channels[0]["context_team_id"]),
bot_token=bot_token,
message_blocks=settings.private_welcome_message,
),
),
)
await asyncio.gather(*tasks)
```
This contextual memory system helps GptPanda track multiple conversation threads across Slack, ensuring accurate and contextually aware responses. It allows GptPanda to remember ongoing discussions and add value without needing constant re-prompting.
What GptPanda Does Best: Core Features That Transform Workflows
So, what does GptPanda actually do? Our focus is on the essential functions that every team needs but might not even realize could be streamlined by AI:
1. Instant Answers: With GptPanda, team members can get instant answers to their questions without breaking their workflow. It’s especially useful for support teams dealing with repetitive inquiries, as they can simply summon GptPanda to provide consistent, quick responses.
2. Contextual Memory: The contextual memory feature remembers the context of ongoing discussions, so team members don’t have to keep explaining the same details. This is especially powerful for brainstorming sessions or project discussions that span days or even weeks.
3. Brainstorming and Idea Generation: GptPanda is a surprisingly effective brainstorming partner. Teams can throw ideas at it, ask for alternative approaches, and even generate content drafts. It’s like having an unbiased assistant who’s always ready to pitch in.
4. Affordable and Scalable: With its freemium model, GptPanda is accessible for startups and small teams who want to try AI without a major financial commitment. For larger teams, the premium version offers advanced features that scale with their needs.
Each of these features has been built with the user in mind, aiming to make workflows smoother and collaboration more efficient. GptPanda isn’t just a Slack add-on; it’s a way to fundamentally change how teams work, helping them to be more productive and focused.
Why Slack and AI Are a Match Made in Heaven
The integration of AI within Slack isn’t just a cool idea—it’s where I see the future of workplace productivity headed. By eliminating context-switching, teams can reduce the small inefficiencies that add up throughout the day. Slack is already where work happens for many teams, so embedding AI directly into it means teams can get the assistance they need without jumping between platforms.
With GptPanda, we’ve seen teams save hours every week. Instead of trawling through endless message threads, they can access summaries and insights directly in Slack. Rather than consulting external apps for brainstorming or data analysis, they can tap GptPanda to provide creative ideas and insights within seconds. It’s all about streamlining the experience.
What’s Next for GptPanda
Building GptPanda has been an exciting journey, but we’re only scratching the surface of what’s possible with AI in Slack. We’re working on even more features that will make team collaboration more intuitive and productive. From advanced analytics to even deeper integrations with project management tools, the roadmap ahead is packed with exciting updates.
Reflecting on this journey, I can’t help but feel grateful for the opportunity to work on something that genuinely improves how teams collaborate. GptPanda is more than just an app; it’s a glimpse into the future of work, where AI helps teams focus on the things that truly matter.
Final Thoughts
If you’re in Slack all day like I am, you know the toll of constant context-switching. GptPanda was designed to change that, to bring AI directly into the place where work happens. For teams, it’s about reducing friction, saving time, and making sure every conversation is productive. And for me, it’s about creating a tool that doesn’t just respond to requests but actively supports and empowers teams to do their best work.
With AI like GptPanda, the future of work is here. It’s seamless, intuitive, and constantly evolving. We’re just getting started, and I can’t wait to see how GptPanda transforms workplaces around the world.
Written by Sarah Chen
