Your commercial data should wait for the morning review become action overnight.
Skilled AI agents turn overnight performance data into ranked issues, proposed actions, and owner follow-up.
If AI can do it, why are you still working on it?
You have dashboards, reports, and copilots.
AI can now produce serious business deliverables.
Yet someone still has to do the work: read what changed, judge what matters, and prepare what needs action.
A performance summary is not enough.
Generic AI can read a dashboard and see that margin is down.
It does not know what to check, who owns the action, or what was tried last week.
So we build around your own work: the sources, checks, owner rules, and history BI does not hold.
What if the review started with actions already underway?
Not a dashboard to inspect. Not a summary to read.
The numbers changed overnight: sales, margin, availability, promo performance, store execution.
The issues are clear and the proposed actions are already with the owners.
An AI agent for the commercial follow-up.
One commercial performance folder: packs, exports, owner map, calendar, and prior actions.
Past work becomes a repeatable skill: source checks, exception logic, owners, outputs, and history.
No integration is needed to prove whether we can cut the time from data to action.
--- name: commercial-performance-action-loop description: Turn the weekly performance pack into owner-ready retail actions with source traceability. --- # Commercial Performance Action Loop ## Operating Mode Action support only. Name owner, question, deadline, and evidence. ## Inputs bi_performance_export_w24.xlsx store_cluster_margin.csv promo_availability.csv owner-map.md ## Run Rules Rank exceptions by commercial impact. Map each exception to an owner. Carry forward unresolved prior actions. ## Output Action queue: owner, question, next step, due date. Evidence notes: every claim tied to source.
Commercial performance action loop
We prove ourselves.
Generating actions is not the proof.
Nor is beating generic AI on what a good review means for you.
The proof is whether operating managers use it each morning to act sooner, follow through, and improve the numbers.
One review earns the next.
With more runs, the business builds a working standard.
Each run records its own performance and the impact it helped create.
Management sees what AI is doing, what it saves, and whether commercial response is getting faster.
For retail teams where commercial follow-ups are still manual.
Pick one recurring commercial review: weekly performance, margin leakage, promo availability, or store underperformance.
In two weeks, we test whether the work behind it can be ready before the review starts.
Fixed first scope. Existing exports. Your owners. Your rhythm.
Start with one reviewEclipsai builds managed AI workflows for recurring commercial work: repeatable skills that turn existing data into owner-ready actions, with evidence, checks, and history.
Questions before you bring one performance pack
Practical answers for teams testing one fixed proof.
What do we get after the first proof?
You get a working action workflow for one recurring review: the exception logic, owner map, drafted follow-up questions, action queue, source notes, run records, and output history needed to run the loop again.
What kind of work is this for?
Reviews where the number is only useful if someone acts: weekly performance, margin leakage, availability, promo performance, store underperformance, category reviews, labour mismatch, or stock exceptions.
What is not a good fit?
It is not a BI replacement, dashboard build, data cleaning project, or generic alerting tool. It is also not a good first fit if the review has no repeat pattern, no usable exports, or no person who can act on the question.
How does Eclipsai build the workflow?
We start with one review: its inputs, owners, thresholds, prior actions, meeting rhythm, and quality bar. The work becomes a workflow standard: required files, commercial rules, action gates, source checks, owner questions, and a run ledger.
Does the workflow make the decision?
No. The workflow prepares the action queue and follow-up material. The owner remains responsible for judgment, escalation, and final decisions.
How do we know it worked?
Before the proof starts, we agree what better follow-up means. Typical measures are time from metric noticed to owner asked, answer received, action agreed, action recorded, and status checked in the next cycle.
What do you need from us?
One recurring performance pack or commercial review, the normal source exports, a metric dictionary if available, an owner map, the last action log, and access to the people who know what good follow-up sounds like.
Where does the workflow run?
The first proof can run from a controlled folder using exported files. Read-only BI and Teams or email integration can come later if the proof earns it.
What does the first proof cost?
The first proof is fixed-scope and priced after we understand the review, data, owners, and delivery environment. It covers one recurring action workflow. Integrations, broader rollout, stakeholder training, and ongoing maintenance are separate.
What happens if the proof does not work?
If the workflow does not meet the agreed quality standard for the agreed review, we keep improving within that scope or refund the proof fee. If refunded, the workflow is not used.
What happens after the first proof?
If the workflow proves value, you can run it again, maintain it internally, ask Eclipsai to maintain it, or extend the same action-loop approach to another recurring review.
Bring one performance pack
Send a short note with the weekly performance pack or recurring retail review you want to test.
Include the next review date if you already know it.