What it is
Technographics is data about the technology a company runs: its CRM and ATS, cloud provider (AWS, GCP, Azure), programming languages and frameworks, data platform, and the rest of its dev and marketing stack. Where firmographics describes a company by industry, headcount, funding stage and geography, technographics describes it by tooling. For a consultant working a tech-adjacent desk, the stack matters almost as much as the org chart, because it points at which specialists a company will need next.
The data comes from a company's public footprint: GitHub repository activity, job description language, engineering blog posts, vendor case studies, and technology signatures visible on the company website. How rich the profile gets depends on how visible that footprint is. A company with an active GitHub and a maintained engineering blog yields far more than a quiet one, and the strongest profiles combine several sources rather than relying on any single one.
A stack change is a hiring need that has not been advertised yet.
Why it matters
Technographics does two jobs. As a filter, it narrows your ICP to companies running technology relevant to your specialism, a desk that places cloud infrastructure engineers only wants accounts with an AWS- or GCP-heavy stack, not every company in a headcount band. As a signal, it flags the moment that stack changes, because a move to Kubernetes or a new data platform usually means a company will need specialists to run it, often before the role is ever advertised. The two uses reinforce each other: the tighter the stack filter, the more precisely a change in that stack can be trusted as a real signal rather than background noise.
Without technographics a consultant either casts too wide, reaching accounts whose stack has nothing to do with their specialism, or waits for the job post and finds a competitor already in. Technographics closes that gap on both ends: tighter targeting up front, and an earlier window to act when the stack moves.
How boilr handles it
Your AI sales employee reads the same public footprint, GitHub repos, engineering blogs, job description language, vendor mentions, and builds a technographic profile for every company that matches your ICP. You can set stack filters directly in your ICP, so a desk specialising in a particular cloud platform or language never sees an account running technology outside that specialism. The profile sits alongside the rest of the account's intelligence in the Company Brain, so it is not a one-off lookup but knowledge that stays current and shared across the desk.
When that profile shifts, a new cloud provider adopted, container orchestration introduced, a new data platform in place, boilr picks it up as a tech migration, one of the buying-signal types the agent tracks around the clock. It identifies the engineering lead behind the change, verifies their contact, and drafts a task that opens with the specific technology rather than a generic pitch. You review and send. The profiling and the watching are the employee's job.
