Where signals become decisions.
LEV is not a chatbot. It is a bounded-choice decision engine that operates across every persona view, surfacing the right insight at the right moment with the right next action. Sub-30-second outputs. Full source citation. Every action becomes a new signal.
How LEV delivers insight.
LEV operates across three surfaces, each calibrated to a different decision moment in the user's workflow.
Onboarding
First-run guidance.
When a new user logs in, LEV walks them through their persona view, surfaces the most relevant signals first, and sets up their first decision flow. Day one is productive.
Insight
Inline at the top of every page.
A contextual insight grounded in actual data, followed by 2-3 bounded choices. The user does not write a prompt. They make a decision. Output in under 30 seconds with confidence score and source tools cited.
Navigation
Sequential decision flows.
For complex decisions (1:1 prep, retention package, OKR coaching), LEV runs multi-step flows. Each step is a bounded choice. Each choice narrows the recommendation. Every action becomes a new signal that improves future recommendations.
See LEV in action.
Example: a manager opening LEV before a 1:1. LEV reads the signal data, identifies the decision context, and surfaces the right framing before the conversation starts.
30-45 minutes to prep a 1:1 manually, pulling data from Jira, Slack, and HR tools separately.
Under 30 seconds with LEV, with confidence score and source tools cited.
Jordan Lee's 1:1 is overdue. Activity down 42%.
Pattern detected: Skills gap, not motivation.
Approach: Support, not review.
Open with skills-gap framing. Avoid performance review language. Identify one tool for upskilling support. Follow up in 2 weeks.
Why bounded-choice, not free-form chat.
Free-form AI chat puts the cognitive load on the user. The user has to know what to ask, how to ask it, and how to evaluate the answer. That works for general-purpose AI. It does not work for executives making time-sensitive workforce decisions.
- User must know what to ask and how to evaluate the answer
- Output quality depends entirely on prompt quality
- No signal feedback loop. Each session starts from scratch.
- LEV identifies the decision context from signal data
- Presents 2-3 specific options grounded in the signals
- Every action becomes a new signal. The system gets smarter.
This is the question we built LEV to answer. Most workforce platforms force users to write prompts, navigate dashboards, or interpret raw data. LEV inverts that: the system reads the data and surfaces bounded choices, so the user makes the decision rather than crafting the question.
Insight velocity at organizational scale.
LEV runs across six persona surfaces simultaneously. Every layer benefits, not just the executive layer.
Morning Brief
90-second daily intelligence and decision queue. LEV surfaces the highest-priority actions across all six signal families before the first meeting.
Retention Playbook
Attrition risk flagged 60-90 days early. LEV generates a bounded-choice retention action set before the employee is lost.
ROI Decision Flow
AI tool ROI surfaced with confidence scoring. LEV generates the defensible spend recommendation, not just the dashboard.
Pipeline Health
Rep performance patterns surfaced before the quarter closes. LEV identifies coaching vs replacement decisions with signal support.
1:1 Prep
30-second coaching brief with pattern detection and recommended approach. No manual data pulling from Jira, Slack, and HR systems.
Growth Companion
Employees see what LEV sees about them. Signal by signal. Growth recommendations. AI fluency coaching. Opt-out available by right.
Every decision feeds the signal substrate.
Signals arrive from connected tools
Behavioral data from Slack, GitHub, Jira, Salesforce, and AI tools flows into the six signal families continuously.
LEV reads signal context and identifies the decision
LEV scores the signals, identifies the relevant decision context for the persona, and prepares 2-3 bounded choices grounded in data.
User makes a bounded choice
No prompt engineering. No blank canvas. The user reviews the options and makes a decision. LEV executes or generates the next step.
The decision becomes a new signal
Every bounded-choice decision flows back into the measurement substrate. Future recommendations are more accurate. The system compounds.
LEV sits at every layer of the intelligence funnel. It is the decision surface for every persona view, not a standalone module.
See the full platform →Activity, Quality, Delivery, Revenue, OKR Alignment, AI Impact. Every LEV output cites the source signals and confidence score.
See the signal families →LEV applies AI Impact signal data to give CFOs and CHROs defensible ROI answers, not just adoption dashboards.
See AI Impact → See LEV in the
live demo.
Explore the live demo with sample data. See exactly how LEV turns workforce signals into 30-second decisions for the CHRO, CFO, manager, and CEO at once.
Walkthrough required. Email tomer@levos.ai for guided demo.
CEO 
CFO 
CHRO 
Manager 
Employee 
AI Ops 