Your platform already has the answers, but do you know what to do with them when they appear?

Most planning systems still rely on documentation, training sessions or internal experts to explain what users are seeing. So instead of acting, teams slow down to interpret. A planner stops to investigate a KPI shift or needs to explain which demand signals drove a change in forecast or a low-stock alert. When teams hesitate or take time to better understand why things changed, work gets delayed, not because the system is unclear, but because the meaning isn’t immediately accessible where the work is happening.

Invent.ai’s advisory agents remove that delay by bringing knowledge directly into the platform, helping users get answers faster. They operate inside the interface and provide screen page-level, context-aware guidance based on the user's current view, the workflow they are in and the decision in front of them. Instead of switching context to find answers, users get explanations while they’re working on the task.

Guidance that lives inside the workflow

Advisory agents are built to sit inside the platform experience itself. They read the context of the screen in real time, understanding what data is being shown, what workflow is active and what the user is trying to do.

These factors shape the response: the same question is answered differently depending on where the user is in the system. The result is a platform that explains itself while being used, not after they look for documentation.

Not one assistant, but a set of focused agents

The Advisory capability isn’t a single chatbot layered onto the platform. It’s a coordinated set of agents designed to support how retail planning actually works.

Domain knowledge agents focus on the logic behind core planning areas such as replenishment, allocation, transfers, markdowns, promotions or MFP. They explain what the system is doing and how to interpret outputs in context.

Advisory agents focus on usability, guiding users through screens, filters, configuration options and how to apply them, as well as available actions such as running scenarios or exporting results.

From searching for answers to getting them in context

Today, when something is unclear, users typically leave the platform. They check documentation, ask a colleague or escalate to support. That interruption slows down execution. Advisory agents remove that step.

They explain KPIs, parameters, charts and outputs directly on the interface. Advisory agents also make available actions visible in the moment, whether that is filtering a view, running a scenario, adjusting constraints or exporting results.

It’s the right information, at the right time, inside the workflow.

Questions answered where decisions happen

Users no longer need to conduct documentation searches. They can ask questions (NLP) directly and receive context-aware answers within the workflow.

They can ask things like how transfer logic handles closed stores, what drives markdown optimization, what filters apply on a current view or what actions are available in a workflow.

Because the answers are grounded in the current context, they reflect the exact view and decision in front of the user, not a generic explanation that may or may not apply. This reduces back-and-forth interpretation and keeps users focused on execution.

Faster onboarding without dependency

Retail teams change constantly: new users join, responsibilities shift and knowledge needs to move across functions quickly.

In most environments, that creates reliance on documentation, training cycles or a small group of experts. Advisory agents reduce that dependency by embedding knowledge directly into the workflow, enabling users to self-service without relying on documentation or support teams. New users understand the system as they use it, accelerating onboarding and cross-team knowledge transfer, while teams stay aligned. Because everyone receives the same contextual explanations and support, teams are no longer the default channel for repeated questions. This leads to a more consistent way of working, without slowing down execution.

Where advisory agents sit in the Remi system

Advisory agents are part of the broader agent hierarchy coordinated by Remi, the orchestration layer across invent.ai.

Insight and monitoring agents understand what is changing in the business. Reasoning agents explain why it’s happening. Analytics agents uncover patterns and drivers. Advisory agents focus on how to operate and act within the platform itself.

Remi connects these coordinated agent families so that signals, explanations and guidance stay aligned to the same context.

Closing the gap between understanding and execution with invent.ai

When knowledge is embedded in the workflow, users stop switching between trying to understand and acting. Documentation becomes part of the interface, guidance becomes immediate and workflows become easier to follow without external support.

In retail environments where timing drives outcomes, that difference matters. Invent.ai advisory agents ensure that understanding is not something users have to search for. It’s already present inside the platform, at the moment it’s needed.

Interested in seeing how invent.ai embeds guidance directly into planning execution? Get in touch with our team.