Targeting by tool stack: finding companies that ran on Jira and Slack

Some sourcing briefs start with a sector. Others start with a tool. A lab building an agent that works inside Jira and Slack does not just need company data; it needs company data from teams that actually lived in those two tools, for years, with the links between them intact.

Targeting by tool stack is one of the more precise ways to build a list. It is also one of the easier ones to get wrong if you treat a technographic signal as proof.

Agents are increasingly built to operate inside specific software. An agent that triages tickets, summarizes threads or drafts status updates has to understand the conventions of the tools it works in: how Jira issues move through statuses, how Slack threads branch off a channel, how people reference one from the other.

Data from a different stack is not useless, but it teaches a different set of habits. A team that ran on a lightweight task board and email behaves differently from one that ran on Jira workflows and Slack channels. When a lab's environments and evaluations are built around a particular stack, matching it at the sourcing stage avoids a lot of filtering later.

That is why tool stack is one of the core fields in a sourcing brief, alongside sector, headcount, years of history, regions and whether the company is operating or winding down.

Each tool on its own is common. The pairing is what makes the data useful. Teams that used both heavily tend to leave a dense web of cross-references:

• Jira issue keys pasted into Slack messages, so a conversation points at a specific ticket
• Integration bots posting ticket creations, transitions and comments into project channels
• Slack thread links pasted back into Jira comments to record where a decision was made
• Standups, incident channels and release channels organized around Jira projects or sprints

That web is what lets a single piece of work be followed from first mention to resolution. A ticket shows the formal record. The channel shows the discussion that shaped it. When a code host such as GitHub or GitLab and a wiki such as Confluence are also in the stack, the trajectory can extend from request to spec to pull request to deploy.

Anonymization keeps those links usable. Every person, company and account becomes a consistent token across every tool, so the engineer who commented on the ticket is the same token in the Slack thread about it.

We build target lists from company and technographic data. Technographic data infers which tools a company uses from public signals, such as job postings that list required tools, integration footprints and other indicators of a company's stack. Combined with firmographic filters for sector, headcount and region, it narrows a broad market down to companies that plausibly fit.

It is a starting point, not a verdict. A technographic signal can tell you a company used Jira at some point. It usually cannot tell you:

• How long the tool was used, or whether the company migrated off it
• Whether one team used it or the whole company did
• How much history is still retained and accessible
• Whether most conversation happened in public channels or in direct messages
• Whether the company still has admin access to export its own data

Retention deserves special attention. Workspace plans and admin settings can limit how much history is kept or visible, and a company that downgraded or let a subscription lapse may have far less than its age suggests. Two companies with identical technographic profiles can have very different archives.

That is where the phone comes in. Martal Group has spent 17 years in B2B outbound, with more than 200 onshore sales reps across the US, Canada, the EU and LATAM. Our reps call the people who can sign, and part of that conversation is confirming that the stack on paper matches the stack in practice. More on that channel in cold calling as a data-sourcing channel.

The questions are practical. How many years was the team on Jira and Slack? Was there a migration in between, and is the older history still there? Did engineering reference ticket keys in commits and chat, or was the tracker updated after the fact? Were most channels public? Who holds admin rights today?

A company that clears those questions moves to evaluation. The evaluation is read-only and takes the seller under an hour, and it measures what is actually present rather than what the signals suggested. The seller sets scope and exclusions, which are annexed to the contract before any data moves.

Tool-stack targeting works best when the brief is specific about what matters most. "Jira and Slack, at least three years, 50 to 500 people, software and fintech, North America and Europe" gives us something concrete to search on and something concrete to confirm on the call. If a code host or wiki is a hard requirement too, it belongs in the brief from the start.

From there, the list gets built, reps start calling, and data from companies that sign is evaluated, anonymized and delivered on a rolling basis. Each delivered dataset comes with consistent tokens across tools, credentials and secrets removed, and HR, payroll, medical and privileged folders excluded by default. Every dataset is licensed for training and evaluation only.

If your team needs data from a particular stack, send us a sourcing brief.