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Ground its answers

Keep answers grounded as knowledge grows

Let Yekar.AI adapt retrieval automatically as the selected knowledge grows.

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Give the agent relevant evidence without loading the whole knowledge library into every turn.

What this changes for your team.

Yekar.AI adapts retrieval to the size of the selected knowledge. Small collections can enter the turn directly, larger ones are split and ranked, and very large collections also give the agent a knowledge-search tool for mid-turn lookups. The thresholds are platform-defined; authors do not have to tune retrieval modes.

How it works in practice.

  1. 01

    Select the organization knowledge the agent is allowed to use.

  2. 02

    Yekar.AI fits small collections directly or ranks the most relevant sections when the collection is larger.

  3. 03

    For very large collections, the agent can search the remaining permitted material during the turn.

What you can plan around.

The behaviour you can design against, stated concretely.

The selected knowledge boundary is stored with the agent; platform-defined corpus thresholds choose the retrieval shape.

Ranked retrieval records which chunks were selected for the model context.

Knowledge-search calls appear in the execution record when the large-corpus tool is exposed.

Complete the operating path

View all capabilities
Ground its answersLink answers back to their sources

Let readers move from an agent's response back to the organization material that informed it.

Ground its answersGround agents in maintained sources

Reuse one governed body of company material across the agents that need it.

Build the agentBuild agents with AI

Turn a working brief into a reviewable agent draft without starting from a blank setup.

Bring one real process

See how Yekar.AI fits the way you work.

Start with a job your team already owns, plus the tools and decisions around it.

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