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How Llev works

One team member. Built like a system.

You met Llev on the home page, one senior team member in your Slack. Here’s what’s under the hood: the surface you talk to, the skills and models he thinks with, the memory he reasons on, and the controls that keep him in check.

01Interface

He lives where you already work.

No new app to learn, no separate window to babysit. Llev shows up as a teammate in the tools your team already opens every day, and the same Llev is reachable everywhere at once.

SlackSlack
Microsoft TeamsMicrosoft Teams
EmailEmail
Lleverage platformLleverage platform
02Knowledge

Skills that already work, on the best models.

Llev arrives pre-loaded with battle-tested skills for real jobs, not a blank prompt. Underneath, he runs on frontier models with automatic failover, so he picks the right brain for the task and never goes dark when one provider does.

Pre-built skills

TriageReportingOutreachQAResearchReconciliation

Models, with failover

Opus 4.8Opus 4.8
GPT-5.5GPT-5.5
Gemini 3.5Gemini 3.5
03Memory

A brain that grows with you.

Llev’s memory is proprietary to Lleverage, short-term for the task in hand, long-term for everything he’s learned about your business. It’s graph-based, so he connects people, tools and decisions the way a real colleague would. Every memory is inspectable.

Llev's memory, shown as three stacked layers, knowledge, insights and observations, connected in a graph
04Control

You stay in charge.

A senior hire with real access has to be controllable. Connect the integrations you choose, watch usage and cost in real time, gate the risky actions behind human approval, and read back every move in an immutable activity log.

Activity log

  • 09:23Opened PR #4821, fix array filter
  • 09:31Created LLE-9846 in Linear
  • 10:02Refund €240, waiting for approval
  • 10:14Posted weekly report to #revenue
A true story · one Tuesday last month

Llev shipped a bug fix on his own. Nobody asked him to.

0109:14

A customer reported a bug in a Slack channel. Llev was in the channel.

0209:23

He read the message, found the bug in the codebase, fixed it and opened a pull request.

0309:31

He wrote a Linear ticket and tagged the right engineer for review.

0409:55

When the fix shipped, he messaged the customer in Slack. He added a 🙂

The ticket he wrote

LinearLinear

Find Record node: user field filter operators cannot match arrays

Issue LLE-9846 in Linear

Summary

Find Record node user field filtering is broken due to incompatible data structures between the UI operators and the stored value. Root cause, code locations and a workaround included.

Status

Triage

This is what an always-on team member actually looks like.

One becomes many

From one hire to a whole org.

Hire one Llev and he scales the way a great team does: many Llevs running between departments, humans approving on top, one shared memory growing underneath. Same system, more desks.

FINANCECUSTOMERLOGISTICSOPS
Humans

Sign-off, audit, override

Many Llevs

Running between departments

Shared memory

Denser every day

An org,
not an inbox.

Four layers. One hire.

Meet your Llev, pick a start date, and put him to work in the tools you already use.

Prefer to talk first?

Book a demo.

Ready to go? You can start free, right now, no sales call needed. Or book a walkthrough and we’ll show you Llev on your own stack.

  • A walkthrough of Llev on your own stack
  • Pick a start date that suits your team
  • Meet your team member before you commit

Get a walkthrough.

Tell us about your team, we reply within one working day.