// use cases

What people build
on top of us.

We're a pipeline, not a product. The interesting work happens downstream — in warehouses, in legal review rooms, in research notebooks. Four patterns we see most often.

// 01 · DATA TEAMS

Pipe Slack into your warehouse.

Treat Slack like any other source. Stream every message into Snowflake or BigQuery, model it in dbt, build dashboards on how your org communicates.

+18 dashboardsavg per data team

terminal — slackscraper

$ slackscraper push --to snowflake://acme.us-east-1/RAW/SLACK

output

WITH thread_volume AS (
  SELECT channel, DATE_TRUNC('week', ts) wk,
         COUNT(DISTINCT thread_ts) threads
  FROM raw.slack_messages
  GROUP BY 1, 2
) SELECT * FROM thread_volume;

// 02 · COMPLIANCE

eDiscovery that takes minutes, not weeks.

Defensible legal holds across every channel. Time-bound search. Audit trails for FINRA, GDPR, HIPAA. Hand your counsel a CSV instead of a paid Slack Enterprise Grid SKU.

4h → 4minaverage eDiscovery turnaround

terminal — slackscraper

$ slackscraper search --legal-hold --case 2026-014 \
    --since 2024-01-01 --users "@maya.chen" \
    --format csv > exhibit-a.csv

output

✓ 12,847 messages matched
✓ chain-of-custody signed: 2026-05-23T14:22Z
✓ exported to s3://acme-legal/cases/2026-014/

// 03 · ARCHIVE

A backup you can actually read.

Slack's built-in "export" gives you a tarball of JSON. We give you something a human (or grep) can open. Search across your workspace's entire history in milliseconds.

8.2B msgsarchived to date

terminal — slackscraper

$ slackscraper archive --to ~/slack-archive/
  ./eng-platform/
  ./product-launch-q3/
  ./incidents/
  ./design-crit/

output

$ rg "rollback plan" ~/slack-archive/
  eng-platform/2026-05-14.md:14:
  do we have a rollback plan written down?

// 04 · RESEARCH

For people studying how teams talk.

Organizational researchers, sociologists, internal comms teams — anyone who needs structured, longitudinal data about how your company actually communicates.

128 paperscite our dataset format

terminal — slackscraper

$ slackscraper sync --include-reactions \
    --include-thread-graph --format parquet

output

df = pd.read_parquet("slack.parquet")
df.groupby(['channel', 'week']) \
  .agg({'thread_depth': 'mean',
        'reaction_count': 'sum'})

// in their words

Three teams,
three problems solved.

“

We tried to build this internally for nine months. Slackscraper had it streaming to Snowflake by Friday.

K

Kavi Mathur

Head of Data · Northstar

“

Our compliance team used to dread Slack export requests. Now they finish them between coffees.

S

Sara Ozdemir

GC · Meridian

“

I use it as a personal grep over five years of company history. Best ten dollars I spend each month.

D

Devon Liu

Eng Lead · independent

Have a use case we missed?

We probably haven't. But we want to hear about it.

$ email hello@slackscraper.io →

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What people buildon top of us.