One live decision layer across sales, supply chain, operations and finance, run by agents. Four functions stop working from four versions of the truth, and month-end reporting gives way to same-day decisions.
Sales, supply chain, operations and finance each reconcile their own numbers, so every review starts with an argument about the data.
Stock-outs at outlet and depot level surface in the monthly pack, after the lost sales have already happened.
Promotions and rebates are approved on volume ambition, rarely measured on incremental margin afterwards.
Slow movers sit in one region while fast movers run out in another, and nobody sees both at once.
Each lever runs on the same live data spine, so sales, supply and finance act on one number.
Senses demand shifts by SKU, channel and region, predicts stock-outs at depot and outlet level, and proposes transfers before sales are lost.
Links every promotion, rebate and listing fee to incremental volume and margin, and flags spend that is not paying back.
Shows where cash sits in slow-moving stock by region, and where fast movers are short, so stock and cash move together.
A walkthrough of the agents at work: what they see, what they decide, and where people stay in control.
AITHENTIC demo · YouTube
Every agent follows the same five steps, so behaviour is predictable and every step leaves evidence.
Secondary sales, orders, stock by depot, promotions calendar and receivables, refreshed daily on the Microsoft Fabric data spine.
Compares demand signals to stock cover and trade commitments; separates true demand change from noise and pipeline fill.
Ranks actions by value at risk: which SKUs to rebalance, which promotions to stop, which accounts to prioritise.
Raises alerts, drafts transfer and replenishment proposals, and publishes the daily control tower briefing.
Measures the outcome of every accepted or rejected recommendation and recalibrates forecasts and thresholds.
No single agent both proposes and approves. Each one has a narrow job, its own tools and its own permissions.
Reads secondary sales, orders and seasonality daily and flags demand shifts by SKU, channel and region.
Watches stock cover across depots and routes, predicts stock-outs and proposes transfers or replenishment.
Links every promotion and rebate to incremental volume and margin, and scores return on trade spend.
Publishes a same-day briefing for sales, supply and finance leads with the three decisions that matter today.
Speed comes from autonomy. Trust comes from the three things around it. This is how each is designed into Sales Control Tower.
Agents monitor and alert continuously without being asked; anything that moves money or stock is proposed, not executed, until the business widens the band.
Every KPI has one definition and one owner, agreed with sales, supply and finance before build and enforced in the semantic model.
Each recommendation links to the data slice, the rule and the forecast version behind it, so a regional manager can see exactly why a SKU was flagged.
Thresholds, value limits and escalation owners are set per region and per channel, and can be tightened in minutes.
Bands are agreed with the business before go-live and widened only on evidence from the pilot.
| Action | Autonomy | Accountable owner |
|---|---|---|
| Detect stock-out risk and alert the owner | Acts autonomously | Supply planner |
| Publish the daily control tower briefing | Acts autonomously | Commercial director |
| Propose depot-to-depot stock transfer | Recommends | Supply planner |
| Pause an under-performing promotion | Human approves | Head of trade marketing |
| Change list price or discount structure | Assists | Commercial director |
Success metrics are agreed before build and signed off with the client after the pilot.
| Metric | Target | How we measure |
|---|---|---|
| Out-of-stock rate at outlet and depot | Down vs baseline | Stock-out days per SKU, same period prior year |
| Lost sales | Down vs baseline | Unfulfilled order lines valued at list price |
| Return on trade spend | Up vs baseline | Incremental margin per unit of trade spend |
| Cash in slow-moving inventory | Down vs baseline | Stock value above target cover by region |
Pilot results are company-reported and engagement-specific. Figures and names in decision records are illustrative.
Agentic process reengineering, one workflow at a time.
Map how the workflow really runs today, with owners, data lineage and the tacit rules.
Agree the business outcome, the baseline and the success metrics before build.
Redesign for agent-first execution: agents, tools, rules and autonomy bands.
Pilot inside your controls, train owners and move bands only on evidence.
Measure against the baseline and report the result the business signed off.
One AI-augmented plan that brings demand, capacity and financial scenarios together, so sales, operations and finance commit to the same numbers and see the impact of every trade-off on margin and cash.
View use case Order to cashAR & Credit Control AgentOrder to cash on SAP Business One, 24/7: credit checks, credit analysis, invoicing and statements handled by agents.
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View use caseBook a working session with our team in Dubai, Riyadh or the UK. We will map the workflow, agree the metric and show the agent running on data like yours.