CaptivateIQ Launches AI Agents for Incentive and Sales Planning

CaptivateIQ, a sales performance management platform provider, today launched Rev Planning Agent and Comp Builder Agent as part of the broader CaptivateIQ Agents portfolio to provide compensation, revenue, and finance teams with artificial intelligence to build, adapt, and scale incentive compensation and sales strategy.

"Compensation and sales planning processes are ripe for automation, but leaders' biggest source of AI hesitation is trust," said Conway Teng, co-founder and CEO of CaptivateIQ, in a statement "That's because [large language models] are probabilistic, and anything involving revenue and paychecks needs to be exact. Nobody is going to accept a commission check that's probably right. You need the right number and a clear record of how you got it."

Working across live sales planning models in CaptivateIQ, the Rev Planning Agent ensures sales strategy can change as needed. Mid-year changes to quota, capacity, and territories move from a project to an afternoon task as users can ask questions in plain language and get answers from their actual plan, review the changes the agent proposes, and be notified of potential improvements before they think to ask. For example, users can do the following:

• Ask how a quota was calculated or what differs across scenarios, and the agent traces the answer through the full chain of inputs.
• Describe desired changes, and the agent suggests ways to improve the model, creates quota policies, worksheets, and calculation logic, and then updates the existing logic.
• Design and rebalance territories by describing a goal or territory rules, and the agent builds the logic, assigns and balances accounts, and helps compare versions before anything is committed.

The agent also simulates scenarios against live data to proactively flag revenue target gaps, capacity risk, unbalanced territories, and unassigned accounts.

The Comp Builder Agent works from the plan logic and data in CaptivateIQ to help compensation administrators understand, debug, create, and update plan logic. Users can describe what they need in plain language or upload requirements and get plan-specific explanations and reviewable changes. For example, users can do the following:

• Ask where a payout came from and get an explanation traced back to the source, even for plans they did not build themselves.
• Ask the agent to debug any calculation, and it will find the root cause, write the fix, and apply it once approved.
• Describe a new rule or upload requirements to build net-new plans, or start from last year's plan and let the agent carry the logic forward with the changes applied consistently across plans and periods.

Built around trust, both agents work from an organization's own data, logic, and rules, and are designed so teams stay in control of every change and can explain every number. The admin must preview changes before they are applied, ensuring humans remain in the loop at all times. Every calculation runs through the CaptivateIQ platform rather than being generated by the LLM, so results are deterministic, traceable, and audit-ready by default.

"The agent is only half the story. The model underneath matters just as much," Teng added. "Many legacy tools run on proprietary languages or models that only a services team understands. An agent might be able to work with that, but if your comp team can't follow what it did, you're back to calling services to check its work. We've invested as much in our modeling platform as in the agents themselves, and our customers are now building, updating, explaining, and debugging plans and payouts in a fraction of the time."