Our data
Good go-to-market decisions need data you can trust. Sumble's comes from what companies actually do, not self-reported profiles or stale lists, and every data point connects to the others so you can filter, segment, and aggregate your way to an answer without writing SQL or learning which table to query.
It isn't pulled from a decades-old legacy database, invented by an LLM, or passed through from another vendor. It's built from organization websites, job posts, and people profiles that are always linked and referenced, then modeled so you can query it flexibly.
Primary sources
Sumble's organization data is rooted in two primary sources: job posts and people profiles. From these we derive organization metadata, tech stack, the teams a company is building, and how all of it trends over time. They're the root of the data tree, and you'll see them throughout the app in slideovers that show the underlying job post or profile.
Derived data
Headcount
Location
Sourced from GeoNames, licensed under CC BY 4.0
Industry
Parent-Subsidiary relationships
Attributes
B2B/B2C
IT Services
Digital Native
Teams and org structure
Technologies
Projects
Job functions: people & job posts
Job levels: people & job posts
Data freshness
Sumble's data stays current through automated pipelines that run throughout the day.
Job posts
Multiple times daily. New postings are ingested from aggregators and careers pages throughout the day.
People profiles
Daily. Professional profiles are refreshed and matched to organizations.
Organization data
Daily. Firmographic data, industry classifications, parent/subsidiary relationships, and attributes are recomputed every night.
Technologies & teams
Daily. Technologies and team structures are re-extracted from job posts using ML models.
Trends
Daily. Historical trends for technologies, job functions, and headcount are recomputed with each pipeline run, drawing on years of historical data.
Intent signals
Daily. New signals are generated after each data pipeline run.
Coverage
Sumble tracks a broad cross-section of the global job market: hundreds of thousands of organizations, millions of job postings and people profiles, and thousands of tracked technologies across categories like programming languages, cloud platforms, databases, and frameworks. Team structures are extracted for covered companies.
Coverage is deepest for technology companies and companies with active hiring, since job posts are a primary data source.
Data quality
Raw job posts and profiles are unstructured text. Sumble turns them into structured, queryable data through several processing stages:
Entity extraction: Custom NER (Named Entity Recognition) models identify technologies, teams, projects, and other entities mentioned in job descriptions.
Relation classification: A second model classifies the relationship between entities, for example whether a technology is actively used or only mentioned.
Entity linking: Extracted entities are normalized and linked to canonical records, so "k8s", "Kubernetes", and "K8S" all resolve to the same technology.
Deduplication: Job posts and organizations are deduplicated across sources to prevent double-counting.
Hierarchy construction: Teams are organized into tree structures, job functions into a standardized hierarchy, and parent/subsidiary relationships are maintained for organizations.
This pipeline runs daily, reprocessing new data and updating existing records.
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