Backdrop - AI Coworkers that run your projects and operations
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AI made execution faster. The bottleneck has shifted to deciding what to build while the knowledge behind those decisions is scattered across people, tools, and AI chats.
Backdrop provides AI coworkers for projects and operations that understand your company, work with your team,and build shared company context. Across Slack, GitHub, Linear, Notion, Asana, Google Workspace and more, they synthesize customer feedback, create plans and specs, manage tickets, draft documents, and keep work moving.
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Backdrop
Hi Product Hunt! 👋 I'm Akanksha, one of the founders of Backdrop.
AI has made execution dramatically faster. But as execution gets easier, the bottleneck shifts to deciding what to build, why it matters, how to prioritize it, and keeping everyone aligned. At the same time, the knowledge behind those decisions is becoming even more fragmented. It's scattered across Slack, docs, tickets, meetings, and now private AI conversations.
Customer feedback gets trapped in someone's ChatGPT. A feature gets rebuilt because nobody remembers why it was killed. Someone joins the team and suggests an idea that was already tried six months ago. Two teammates ask AI the same question without realizing the other already did.
The problem isn't that teams lack information. It's that they can't build on what they already know. Too often, what people learn in AI stays with them instead of the company. We think AI should make companies smarter, not just individuals. That's why we built Backdrop.
Backdrop provides AI coworkers for projects and operations that understand your company, work alongside your team, and carry context across every project and decision.
We're launching with Alex, our AI coworker for projects and operations. Alex connects to Slack, GitHub, Linear, Notion, Asana, Google Workspace, and more to turn customer feedback into product plans, discussions into decisions, meetings into action items, and plans into execution.
If your team ships software, Alex can also bring in Sam, our AI engineering coworker, to implement features, review code, investigate bugs, and turn plans into shipped products.
We're incredibly excited to finally share Backdrop with the Product Hunt community. We'd genuinely love your feedback, questions, and ideas. We'll be here all day, so ask us anything. Thanks for checking us out! 🚀
Backdrop
@akanksha_backdrop Hey everyone, I'm Caitlin, CTO at Backdrop 👋
Building AI that actually works for a company is a different problem than building a chat demo. Most tools reset every session and context lives in someone’s head, a Notion page, or a private ChatGPT thread, and the next person starts from zero again.
My focus has been making Alex and Sam compound instead of reset: pull what’s already known, work across Slack, GitHub, Linear, Notion, and the tools you already use, and leave the company smarter than they found it, not just a longer chat history.
One of the hardest engineering challenges has been getting that right without making the system feel like a black box. Agents should carry real context forward, but teams still need enough visibility and control to trust what happens next. Here to answer questions and appreciate the support!
the part that's interesting to me is the cross-tool synthesis rather than just another single-tool assistant - pulling customer feedback and turning it into an actual plan across Slack/Linear/Notion is the annoying manual glue work most teams still do by hand. what's been the hardest integration to get genuinely useful vs. just surface-level read access?
Backdrop
@omri_ben_shoham1 Good question, this is honestly where most of our engineering time goes.
Out of the box integrations from agent builder tools are basically API wrappers. Create a ticket, post a message, read a doc. For some tools that's enough and we keep those light. But for the tools where the core role work happens, it's not. Real PM work means knowing which project the feedback belongs in, what your labels and states mean, who owns what, how to write a ticket someone can actually pick up. The API doesn't give you any of that, it's all in how your team uses the tool.
Then there's the part you called glue work. Feedback sits in Slack, the ticket's in Linear, the plan's in Notion, and none of them know about each other. A lot of what we built is that connective layer, plus auth living outside the agent, tight control over what tools it can touch, and skills that teach it the working path per app and per role.
Hardest ones so far have been tools like Google Workspace and Figma. A PM reviewing a design and drafting a plan is a completely different job than an engineer implementing from that same file and checking the live UI, so a generic read the file integration doesn't cut it. We had to build for each role separately.
Honestly, reading data was maybe 20 percent of the work. The rest was making the agent behave like someone who's actually used the tool before. For eg: Slack isn't just read the channel, it's pull the right signal. Linear and Notion aren't just create a page or ticket, it's write it the way your team would, etc.
Arc
This is cool, all these shared context cos are building stuff for the user but for not the org and its hard to flow context (and importantly, source of truth) across the org. Also daisy chaining tool use is pretty unique in this context.
Is orchestrator multi model?
Backdrop
@basile_senesi1 Thank you! Yeah the user vs org distinction is the key. Personal AI memory is a solved-ish problem, getting a team to one source of truth is not, and that gap is where we live. On the orchestrator, we pick the model for the job under the hood, different kinds of work run on different models. We'll expose that choice eventually, we just don't want teams thinking about model selection on day one
this is so exiting. the shared context and information, along with actions to do tasks. will definitely cut down the time our team spends on working with AI agents rather than these agents for working for them. Are Alex and Sam purpose built? Or can they be used interchangeably?
GrowMeOrganic
What's the differentiation between Backdrop and other AI team apps?
Backdrop
@iamanantgupta Good question! A few things.
The big one is you don't build our agents, they arrive finished. Most tools in this space give you a shell that you spend weeks setting up, training, and then babysitting. Alex already knows how PM and ops teams operate.
Second, they're part of the team, not attached to one person. They have their own Slack handles, their own identity. For eg: when Alex comments on a Linear ticket, the whole team sees it and it's obviously Alex, same as any other teammate.
That carries into how the whole thing works. You can DM the agents or pull them into group chats with the team, and the shared context and memory get built from the team's actual work, projects, tasks, decisions. So what an agent learns on one project carries into the next and everyone's working off the same source of truth, instead of one person's chatbot knowing things the rest of the team can't see.
Third, Backdrop is an actual workspace, not just a chat window. There's task management, full history on every task showing what happened, why, and how the agent went about it, plus an approval setting if you want sign off before work runs.
And the integrations are built around what a PM/ops/engineer actually does in those tools day to day, not the generic out of box connections you get everywhere else.
Is there a specific tool you're comparing us to? Happy to get into specifics
the "someone joins the team and suggests an idea already tried six months ago" scenario is such a specific and true pain point, that's the real cost of tribal knowledge living in people's heads instead of anywhere searchable. when Slack, Linear, and a doc genuinely disagree on the current state of something, does Backdrop surface the conflict to a human, or does it pick one source as authoritative and move on?
Backdrop
@galdayan That scenario is exactly why we built this.
When Slack, Linear, and a doc disagree on the current state of something, Backdrop doesn’t quietly pick one source as truth and move on. It pulls the relevant context, flags the mismatch, and asks clarifying questions so a human can decide. Once that decision is made, that’s what should stick as company knowledge so the next person doesn’t rediscover the same conflict six months later. If a task already points at an authoritative source (linked spec, approved plan), we treat that as the directive. But unresolved disagreement across tools is a clarifying-question moment, not something we want an agent to paper over.
Pulled feedback from our Slack threads and turned it into Linear tickets without me babysitting the process. Genuinely useful for cutting down the back-and-forth.
honestly the shared company context idea is really compelling, one thing i'd love to see is a way to mark certain decisions as final or source of truth so the ai doesn't second guess them when pulling context for new projects, basically a confidence flag for historical calls
Would love to see a quick weekly digest mode where the AI coworker summarizes what it touched across Slack, Linear, and Notion so the team can skim changes without digging thread by thread. That kind of cross-tool recap would save a lot of context switching for everyone.
This is neat. How does it decide which tasks to handle autonomously versus loop you back in?