PlatformAI · FlagshipAI Stories: HR insights in plain English.Next moduleAnalytics & Reporting
Stories · Plain English · Every Monday · Built in Lagos

Your people data, read to you over coffee.

Every Monday, one honest paragraph: what your team actually did last week, who's trending up, who's quietly disengaged, and what's worth a conversation today. No dashboards. No pivot tables. No "I'll get back to you."

1 paragraph
What replaced the 14-tab HR dashboard
14 days
Earlier warning on flight risk, on average
92%
Of execs read the Monday story within an hour of it landing
Before and after

The executive update, before and after Stories.

How you learn what's going on, todayBefore

  • 14-tab dashboard that nobody opens on Monday
  • A quarterly "temperature check" with HR, usually two weeks late
  • You hear about Ifeoma's resignation on the day she sends it
  • The comp gap surfaces in an exit interview, not a planning meeting
  • Board deck built by hand, the Sunday before the board meeting
Time from signal to action6 to 12 weeks

How you learn with StoriesAfter

  • One paragraph, every Monday, in your inbox and Slack
  • Flight-risk flags with context, not a score, a sentence
  • Every number traceable to a source record, one click away
  • Ask "why is attrition up in growth?", get a sourced answer in seconds
  • Board-ready memo generated from the same data, one click
Time from signal to actionSame day
How Stories work on Careersome

Five steps from a million events to one Monday paragraph.

Every event your team generates becomes a signal.

01 / 05

A check-in logged, a pulse dropped, a leave approved, a hire made, Stories listens to every module on the platform, in real time, without you building a pipeline.

1:1 notes
412 this week
PERF
Pulse responses
169 · 92%
ENG
Leave approvals
38
TIME
Hires closed
6
HIRE
Comp reviews
21
PAY

Workforce answers in plain language.

Ask what's happening across hiring, performance, leave, and engagement, get a short answer with sources, not another dashboard to decode.

Natural-language questions

Query turnover, team load, or review status in plain English, useful for managers who don't live in BI tools.

Sourced answers

Responses point back to modules and records, so you can verify, not just believe a paragraph.

Risk and trend signals

Surface flight risk, goal drift, and workload strain early, before they show up in attrition reports.

Manager and exec views

Role-appropriate summaries, team health for line managers, portfolio views for leadership.

Weekly digest

A concise read on what changed, fewer status meetings that only repeat numbers.

Connected to the full platform

Stories read the same data as recruitment, performance, and analytics, one fabric, not a bolt-on AI.

Where Stories land

In the tools your team already opens.

Stories are delivered where you read, email, Slack, WhatsApp, or viewed in-app. Schedule them daily, weekly, or ask on demand.

Sl
SlackChannel or DM delivery
W
WhatsAppExecutive digest
@
EmailMonday inbox
M
Microsoft TeamsChannel summaries
N
NotionWeekly page sync
G
Google DocsBoard-ready export
Pd
PDFOne-click board deck
Cs
Careersome appNative in-product view
iOS
MobileRead on the commute
🔔
AlertsUrgent signals, push
Api
API / webhooksWire Stories anywhere
+
ZapierCustom destinations
Questions

Before you book a demo, the things people ask us.

How is a Story different from a dashboard?

A dashboard shows every number at once and leaves the interpretation to you. A Story is a short paragraph that names what's changing, why it matters, and what to do, drafted by the AI, editable by you, sourced to the underlying records.

Who can read which Story?

Stories are scoped by role. Executives see org-wide signals. Managers see their own team. Employees see their own Story, the same honest read their manager has, phrased for them. Admins control audience and cadence.

Can I edit a Story before it goes out?

Yes. Every Monday draft is held for review by a designated editor (usually the Head of People) before being sent. You can rewrite, add context, or suppress items, the underlying sources stay cited.

What if the AI gets something wrong?

Every claim is cited to source records. If a number looks off, you click through to the evidence and either correct the record or flag it, the model learns from both. Confidence scores are visible on every figure.

Is my data used to train shared models?

No. Your models are your models, scoped to your workspace. Nothing is trained on other customers, and sensitive signals never leave your region of residency.

Read your own Monday, before you sign up.

Bring a month of your HR data, we'll run it through Careersome and hand you the Story your Monday would have started with. No commitment, no slide deck, just the paragraph.