How to get your Lever data ready for agentic AI

Lever holds your recruiting pipeline: candidates, applications, interviews, feedback, and offers. Getting that data ready for agentic AI means giving AI agents a centralized, cleansed, and governed version of it, so they answer questions like "which stages lose our best candidates, and which postings fill fastest?" in seconds rather than days. Getting your Lever data AI agent-ready means centralizing it in a warehouse or data lake where AI agents query your full hiring history, join it with other data sources, and surface answers on demand. Fivetran + dbt Labs delivers the data foundation for agentic AI — Fivetran moves your Lever data reliably, and dbt transforms it into trusted, AI-ready tables.

Why Lever data is critical for agentic AI

Talent leaders decide where to invest recruiter time, which postings to fund, and how fast to move. Lever records every step, but inside the tool each view is a snapshot. It does not show how pipeline health connects to hiring cost, team growth, or new-hire performance.

Recruiting ops teams pull exports for weekly pipeline reviews, so leaders see last week's story. And the volume of notes, interview feedback, and stage changes across hundreds of openings exceeds what any recruiter reviews.

Agents need infrastructure built for agents, not just analytics: current, connected recruiting data in one place.

What agentic AI does with Lever data

Pinpoint where candidates drop off. A recruiting leader asks which stage loses the most qualified candidates. The agent reads stage history across every opportunity and shows the leak.

Compare source quality. A head of talent asks which sources produce offers that candidates accept. The agent ties sources to outcomes, and the team reallocates budget.

Tighten interview feedback. A hiring manager asks how long feedback takes and how consistent it is. The agent reviews interview and feedback records across panels.

Forecast offer volume. A finance partner asks how many offers will land next month. The agent projects from the live pipeline.

How Fivetran gets your Lever data ready for agentic AI

Lever data is hard to use directly for agent workloads. It sits in a single recruiting system, changes all day, and spreads across candidates, opportunities, postings, and notes.

Fivetran moves your candidates, applications, interviews, feedback, offers, notes, postings, and users into your warehouse or data lake. Candidate, interview, offer, and note data syncs incrementally, so only changes move. Reference data, such as postings, stages, and users, refreshes daily so updates and deletions land correctly. Fivetran also keeps stage history for candidates and opportunities, which lets agents analyze how people move through your pipeline over time. You block sensitive columns before they leave Lever.

dbt Labs completes the stack. dbt transforms raw Lever data into clean, trusted, AI-ready tables, with modeling, testing, and documentation that give agents consistent definitions for terms like "time to offer." A Quickstart data model gives you a fast starting point for Lever. Fivetran moves the data, and dbt Labs makes it usable. For an open, lake-based setup, use the Fivetran Managed Data Lake.

What your Lever data unlocks for your team

With Lever data in a central warehouse, AI agents unlock capabilities your team cannot reach from inside Lever.

• Funnel visibility — Recruiters see exactly where candidates stall or leave.
• Source and posting performance — Talent leaders fund what produces accepted offers.
• Interview and feedback quality — Hiring managers calibrate panels and speed up decisions.
• Offer forecasting — Finance plans hiring cost from the live pipeline.
• Cross-functional analysis — Recruiting data joins HR, finance, and performance data.

FAQ

What does it mean for Lever data to be AI agent-ready?

It means your recruiting data, from candidates through offers, sits in one governed warehouse or data lake. AI agents query a complete, current hiring record and combine it with HR, finance, and performance data.

What can my team actually do with AI agents and Lever data?

Your team asks hiring questions in plain language and gets answers in seconds. Examples include drop-off points by stage, source quality, and offer forecasts.

Is Lever data ready for AI agents out of the box?

No. Centralize, model, and govern Lever data before an agent queries it reliably.

Do we need a data engineering team to set this up?

No. Fivetran handles the movement, and the Quickstart data model gives analysts a fast path to analysis-ready tables.

How does Fivetran get Lever data ready for AI agents?

Fivetran moves your Lever data reliably into your warehouse or data lake and keeps it current. dbt Labs transforms and governs it into clean, AI-ready tables using its full modeling and testing capabilities, and the Quickstart model speeds up the first build. One company delivers the complete stack.

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