Give HR Teams Onboarding Memory That Scales Beyond Slack Threads
HR teams answer the same onboarding questions every hiring cycle. The knowledge lives in Slack threads, Notion pages, and the heads of three senior HR staff. MemoryLake gives HR teams a structured onboarding memory layer — so every new hire's AI assistant can answer policy, process, and benefits questions correctly.
Give HR Teams Onboarding Memory That Scales Beyond Slack Threads
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The problem: onboarding knowledge is scattered and stale
A new hire asks about parental leave. The Notion page is six months out of date. The Slack thread that updated the policy is buried. HR ends up answering manually — for the hundredth time. AI tools, when used, often pull from the stale doc and give wrong answers.
How MemoryLake solves onboarding memory for HR teams
Single source of truth for policies
Benefits, leave, expense rules, and onboarding flows live as typed Fact memory with version control.
Conflict detection blocks stale answers
When a policy updates, MemoryLake flags the contradiction. New answers retrieve the current version.
Skill memory for repeatable onboarding flows
"Day-1 onboarding sequence for an engineering hire." Defined once. Triggered for every new hire.
Provenance per fact
Every answer the AI gives links back to the policy doc and signed-off date.
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How it works for HR teams
- Connect — Import policy docs, employee handbook, benefits guides, and HR Slack threads.
- Structure — Each policy becomes a versioned fact with effective dates and owners.
- Reuse — Any AI tool — internal HRIS bot, onboarding assistant, manager copilot — retrieves the current policy.
Before vs. after: HR onboarding memory
| Without MemoryLake | With MemoryLake | |
|---|---|---|
| Same FAQ asked 50 times | HR answers manually | AI answers from memory |
| Policy update from last week | Stale doc still cached | New version retrieved |
| Audit "what policy was active in March?" | Manual archive search | Versioned memory query |
| Onboarding a senior engineer vs. an intern | Same generic flow | Role-specific skill memory |
Who this is for
People operations and HR teams in growing companies — typically 50–2,000 employees — where onboarding volume outpaces the bandwidth of senior HR staff and policy questions have legal exposure.
Try this use case on your own account
A fictional robotics company changed its parental leave and travel meal allowance this year, but its January handbook still has the old numbers. Every version of every policy is pinned as a fact stamped with its effective date, sign-off and source, and nothing is overwritten. When a new hire asks about parental leave, search returns both the old and the new version plus the outdated handbook; the stamps pick the version in force and mark the rest stale. An audit lists which version applied on any date, and on day one `actor update` moves both new hires on, so the engineer and the intern each get the first-day plan for their role.
What you will see
- Every policy version pinned as an immutable fact stamped with effective date, sign-off and source document
- Old and new versions both come back from
search; the version in force is the answer, stale versions and the outdated handbook are marked - Audit any date with
fact list: which version of every policy was in force, and which ones are scheduled - New hires moved to
stage:day-1withactor update, found withactor list --tags, each given the plan for their role, plus a local web UI
You need: a MemoryLake API key · the memorylake CLI · Python 3.9+
Run it
git clone https://github.com/memorylake-ai/memorylake-usecases.git cd memorylake-usecases/onboarding-memory export MEMORYLAKE_API_KEY=sk-… python3 demo.py # or: python3 web/server.py
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Frequently asked questions
How does versioning protect us from compliance issues?
How does versioning protect us from compliance issues?
Every policy fact is versioned with effective dates. You can answer "what was the policy on date X?" with an immutable audit trail.
Can different roles see different memory?
Can different roles see different memory?
Yes. HR-only memory stays in an HR-scoped namespace; employee-facing memory is read-only for them.
Is employee data encrypted?
Is employee data encrypted?
AES-256 end-to-end. MemoryLake staff cannot read employee personal data.