LinkedIn Data, Delivered

Public surfaces only: company pages, public job postings, public posts and their engagement counts, and profile fields a member has chosen to make publicly visible. We do not access content behind a login, we do not touch private profiles, connections, or messages, and we do not attempt to infer anything a member has not published. During scoping we confirm in writing exactly which fields you can rely on, because the public surface is narrower than most people assume.

We collect only publicly available pages — the surface a logged-out visitor can see — and we do not circumvent authentication or access controls. Because professional data can constitute personal data under GDPR, we treat it as such: data is stored in your chosen region, retention windows are configurable, deletion propagates through the pipeline when content is removed at source, and our DPA is available before you sign. We will not build a use case that depends on deanonymising individuals.

Yes, and this is the most common configuration. You supply an account list — by company page, domain, or name — and we monitor those organisations continuously, pushing job postings, page changes, and public posts as they appear. Most customers wire this straight into their CRM as trigger events rather than consuming it as a dataset.

Cadence is yours to set. Account monitoring for trigger events typically runs daily, which is fast enough that a job posting reaches your CRM within a day of going live. Broader market mapping usually runs weekly, since the aggregate picture does not move fast enough to justify more.

Into the systems you already run. Normalised records land as JSON, CSV, or Parquet in S3, GCS, or Azure Blob, or straight into Snowflake or BigQuery, on whatever cadence you set. Webhooks push matches to your endpoints as they are detected, which is how most customers wire alerting. A solutions engineer sets the schema, cadence, and destination with you during onboarding.

Keeping the collector working is our responsibility. Extractors are monitored continuously, and when the platform changes its structure we patch upstream while the schema we deliver to you stays fixed. If a change causes a coverage gap we tell you which window was affected rather than quietly returning fewer records.

Pricing is scoped per engagement — the number of tracked terms, handles, or communities, the refresh cadence, and the delivery destinations involved. Historical backfill is quoted separately. Every engagement starts with a free sample run against your own brand terms, so you can judge the data before committing.