The proposal sounds reasonable.
People agree to do things in Slack. Someone has to copy those things into Jira. Put an LLM between the two, and surely you can remove the copying.
You already have developers. You already pay for an AI API. Why add another subscription?
So, imagine you build it.
You send a message: “Maya, please update the vendor checklist by Friday.”
A ticket appears. Correct title. Correct owner. Correct deadline.
Beautiful. You show the team. Someone posts a fire emoji. You add the bot to a real channel.
Then someone says “actually”
The next request is slightly less cooperative.
“Can we update the vendor review, check that access exception with IT, and attach the latest report?”
Three tasks. Fine. You adjust the prompt.
Then a reply arrives:
“Actually, IT already has a ticket. Daniel’s taking the review. The report is in last week’s thread.”
Now the bot needs to understand which request changed, find an existing ticket, resolve who Daniel is in the tracker, and locate the right attachment.
You could ask everyone to use a strict message format. But that introduces a small problem: the point was to remove work, not teach the entire company how to speak to your bot.
So you add conversation history. Then tracker searches. Then more instructions.
You are no longer just moving text between applications. You are deciding what a conversation means and what should happen because of it.
There are now tickets about the ticket bot
Next, you need to handle what happens when the same event arrives again. Slack’s Events API can retry failed deliveries, so your application needs to avoid turning a repeated event into repeated work. Slack Developer Docs
In your imagined rollout, that discovery arrives as a message from a colleague:
“Why did it create this twice?”
You fix that.
Then someone asks why a task never appeared. Was the request missed? Did the tracker reject it? Did the connection stop working?
You add logs, failure alerts, and a way to reprocess messages. Before changing the prompt again, you assemble test conversations to check whether fixing one mistake creates another.
All sensible work. All necessary for the tool you now want people to depend on.
Also, none of it was in the original sentence: “We’ll just connect Slack to Jira.”
The worst thing happens: people like it
Another department wants access.
They use a different tracker. Their requests have different owners, different project names, and different ideas about what “urgent” means.
Someone wants meeting follow-ups included. Someone else asks about screenshots. A manager wants to know which channels the bot can read.
In this version of the project, adoption is going well. Your small automation now has users, access decisions, support requests, and a backlog.
Your main product also has a backlog.
The same people are working on both.
That is the uncomfortable part of the build-versus-buy decision. The internal tool does not have to fail to become expensive. It can become expensive because it succeeds.
At least the tokens are cheap
Return to the original calculation: a message goes in, a task comes out, and you pay for an API call.
That is not the workflow you just built.
Strukt’s own implementation illustrates the difference. Its build-vs-buy breakdown describes a multi-step process involving context, tracker searches, task creation, and updates. Processing the follow-ups also consumes resources, not just creating the initial ticket. Strukt.ai
You can optimize your version. Test cheaper models. Reduce unnecessary context. Improve validation. Work out which steps need more capable models and which do not.
But put that work in the budget, too.
A smaller API bill is useful. A smaller API bill that requires an ongoing engineering project is a different calculation.
Count the subscription you avoid, certainly. Then count the building, testing, corrections, maintenance, and other projects those people could have delivered.
You could also let this be someone else’s product
This is the work Strukt.ai is built around.
Strukt connects Slack and Telegram with trackers including Jira, Linear, Trello, and Notion. It can separate several requests into individual tasks, identify owners from mentions or commitments, and carry relevant files and context into the work. Strukt.ai
That gives you an existing product to test against your conversations, rather than a list of capabilities to build before testing can begin.
Strukt also describes using lower-cost models within its task-processing workflow and spreading the development cost across customers. You are buying the implementation and its ongoing operation, not simply resold access to an LLM. Strukt.ai
The pricing starts with one active channel free. Pro includes three channels for $79 per month, and Teams includes 10 channels for $299 per month, billed monthly. There are no per-seat or token-based fees. Additional channels and optional add-ons can increase the subscription cost. Strukt.ai
You still approve access, check outputs, and decide whether the workflow meets your needs. Buying does not remove those responsibilities.
It removes the need to develop and maintain the underlying product yourself.
What were you trying to get done?
There are good reasons to build. Perhaps your workflow depends on proprietary logic or requirements an existing product cannot meet.
But “we could build this” is not, by itself, a reason to own it.
Before opening a new repository, try the actual workflow in one approved channel. Check what gets captured, what needs correcting, and how much manual work remains. Compare that with the cost of keeping an equivalent internal system running, not just getting its first ticket to appear.
The original goal was to spend less time managing tasks. Not to become the team that maintains the task bot.