From data to decided — across every category, every week.
Ariane continuously monitors your full SKU estate and converts commercial opportunities into structured Decision Cards — each with a recommended action, financial value in USD, deadline, and assigned owner. Your team approves, rejects, or escalates in under 30 seconds.
Built on Hypertrade's retail intelligence platform — deployed across 4 continents over 20 years.
Category: Women · Sub-Category: Facial Care
Channel: Website · Type: standard
Rationale
Previous promotion 36 days ago generated 10.3% uplift with ROI of 1.2 and reach of 37%
Mechanics:10% Discount
Expiry Date:April 5, 2026
Chance of Success:91%
Other Decisions for this item:1
12 decisions in queue this week▲
Decision Queue · This Week12 pending
SKUTypeValue
Ranked by financial impact · Refreshed every Monday
2.5–4.5%
Sales increase through prioritised assortment and promo decisions
60–80%
Time saved through predictive recommendations and report automation
15%
Inventory reduction through predictive recommendations and alerts
0.3–0.6%
Margin recovery through advanced analytics and decision accuracy
Hypertrade has worked with leading retailers across Southeast Asia, the Middle East and Africa — from grocery and hypermarket to health and beauty.
Early access engagements underway. Case studies available Q3 2025.
The problem your teams face every week
Thousands of SKUs. No clear next move.
Your category managers have dashboards telling them what happened. They don't have a system telling them what to do next — and what it costs to wait.
Data everywhere, decisions nowhere
Your teams have access to more data than ever — ERP, POS, shelf analytics, competitor pricing. But translating all of that into a clear "here's what to decide this week" requires hours of manual analysis that doesn't scale.
Avg. category manager: 3–4 hrs/week just synthesising data before deciding anything
Decisions made too late — or not at all
Without a structured queue, high-impact decisions get missed, deprioritised, or escalated too late. A delist that should have happened in week 8 happens in week 14 — after $200K of avoidable margin erosion.
Average delay on range decisions: 3.4 weeks past optimal timing
No one knows what a decision is worth
When a reprice, a delist, or a promo approval has no P&L impact attached, it doesn't get urgency. Competing priorities win. The result: millions in value left on the table every quarter.
Unacted opportunities across a typical category: $2M–$6M per quarter
The Complexity Ceiling
Retail complexity has outgrown human decision velocity.
Commercial teams cannot manually process modern retail complexity anymore. Ariane is not a tool to help them try harder. It is the operational layer required to manage it at scale.
What this means for a $500M retailer
Weekly decisions per category manager200+
Decisions with $ value attached~0%
Unacted margin per quarter$2–6M
0.4% margin recovery on $500M revenue$2M EBIT
SKU Proliferation
Average SKU estate grew 34% in five years. Decision volume doubled. Commercial headcount stayed flat.
Decision latency → margin leakage
Promotional Volatility
Promotional ROI declining across categories. Cost-price instability compresses every window to act. Most teams are still deciding last quarter's promotions.
Every delayed decision has a measurable cost
Dashboard Overload
More BI tools than ever. More data than ever. Category managers still spend 3–4 hours a week synthesising before they can decide anything. BI generates insight — not decisions.
Insight ≠ decision
"Ariane is not another analytics layer. It is a mathematical necessity — the operational infrastructure that makes commercial decision-making possible at modern retail scale."
Hypertrade · Ariane RDS Design Thesis
How Ariane RDS works
From data to decided. Every week.
Three steps that transform how your commercial teams operate — then a choice of how far to automate.
Step 01
Surfaces what matters
Ariane continuously analyses your full SKU estate — sales index, margin, stock cover, competitor pricing, promotional performance, shopper behaviour, and supplier performance — and identifies exactly where action is needed this week.
Sales & margin trends
Competitor price gaps
Stock cover anomalies
Cannibalisation signals
Step 02
Builds decision cards
Every opportunity becomes a structured Decision Card — with a recommended action, a financial impact, a deadline, and an owner. Your team opens the tool and knows exactly what to decide, in what order, and why.
$ impact on every card
Deadline & owner assigned
Recommended action
Escalation paths built in
Step 03
Your team decides — or Ariane does
Manual validation or agentic execution — within guardrails you define. Each approved decision generates an executable output: a pricing brief, a planogram update, a supplier negotiation brief.
Approve, reject, or escalate
Executable outputs generated
ERP integration on roadmap
Full audit trail maintained
The Decision Card
Everything you need. Nothing you don't.
Each card surfaces the right context — product details, rationale, mechanics, success probability, and guardrails — so your team can act with confidence in seconds, not hours.
Priority + financial value upfront
Medium / High / Critical priority badge with $ impact so you always know what to action first.
AI-generated rationale
Grounded in your Retail Logic — not generic AI guesswork. Every sentence maps back to data, thresholds, and causal nodes.
Guardrails baked in
View guardrails, check data, and open the Decision Centre for full context — before you approve or reject.
Approve or reject in under 30 seconds
Chance of success, expiry date, and conflict detection — everything surfaced so you never need to hunt for context.
MEDIUM PRIORITY
$28k
REPEAT PROMO
›
SKU: 00178923 · Facial Scrub – 75 g
195 stores
Category: Women · Sub-Category: Facial Care · Channel: Website
Product Type: standard
Rationale
Previous promotion for SKU 00178923 36 days ago generated 10.3% uplift and had an ROI of 1.2 with a reach of 37%
Mechanics:10% Discount
Expiry Date:April 5, 2026
Chance of Success:91%
Other Decisions for this item:1
Live decisions · This week
This is what your team opens on Monday morning.
Not a dashboard. Not a report. A queue of decisions — each one pre-analysed, valued in $, and assigned to an owner before your team sits down.
Fabric Conditioner · All Retailers · Wk 10 2025● Two decisions already overdue
Decision Queue · Fabric Care
Live
Product
Index
Margin
Action
Impact
Due
Comfort Original 1.5L
SKU 4821 · TESCO · OVERDUE 2 DAYS
78
↓ 22pts vs LY
14.2%
↓ 4pt margin
⚠ Delist?
$366K at risk
Overdue · Act now
Lenor Spring 750ml
SKU 3310 · ASDA, MORRISONS, TESCO
82
↓ 18pts vs LY
16.8%
↓ 2.1pt
↓ Reprice?
67d overstock
Overdue · Act now
Fairy Non-Bio 3L
SKU 2205 · SAINSBURY'S
112
↑ 12pts vs LY
22.4%
↑ 1.2pt
Promo ↑
+$224K upside
Due tomorrow
Comfort Intense 900ml
SKU 5512 · ALL RETAILERS
95
↓ 5pts vs LY
19.1%
↑ 0.4pt
Reprice
+$869K upside
Due Fri 7 Mar
Comfort Intense Jasmine 1.4L
SKU NEW · ALL RETAILERS · LAUNCH
—
New launch
23.5%
Forecast
+ Range
+$373K Y1
Due 21 Mar
Each of these cards lives in a Decision Centre.
A focused workspace for one type of commercial decision — pre-analysed, queued, and ready to act on.
Ariane structures commercial decisions across every domain your team manages — pre-analysed, queued, and ready to act on every Monday morning. Not dashboards to investigate. Decisions to approve.
Range
SKU listing and delisting decisions with full cannibalisation modelling and P&L impact per retailer.
Example: Delist Comfort 1.5L at Tesco. Net P&L impact: +$44K/period.
Promotions
Promotional approval and optimisation — ROI modelling, mechanic selection, and cannibalisation risk scoring.
Targeted actions for lapsed shoppers and high-value category opportunities — briefing-ready for your CRM or agency team.
Example: 12K lapsed fabric care shoppers in cluster B. Rescue campaign brief generated.
Finance
Margin floor monitoring and P&L protection — flags SKUs eroding profitability before the damage compounds.
Example: 3 SKUs below 17% margin floor. Recommended: renegotiate cost price or reprice by period 4.
Inventory
Overstock resolution, OOS prevention, and replenishment decisions with markdown cost modelling.
Example: Lenor Spring 750ml: 67-day stock cover. Reprice −12% to clear in 3 weeks.
Suppliers
Decides which Range, Promo, and Inventory actions to negotiate per supplier — so you walk into every meeting with data, not opinions.
Example: Supplier A fill rate at 84% for 6 weeks. Escalation brief generated ahead of QBR.
The weekly commercial rhythm
Every Monday, your team opens Ariane. Not a spreadsheet. Not a dashboard.
Ariane creates a structured commercial operating cadence — a weekly rhythm of decisions that replaces ad hoc analysis, escalation chaos, and missed opportunities.
Mon
Decision queue refreshed overnight
Ariane has processed the week's data. Decision cards are queued, prioritised by $ impact, and assigned to owners before anyone opens their laptop.
Tue
Category reviews replace report synthesis
Category managers work through their queues. Each card already contains the rationale, the financial impact, and the recommended action. No preparation required.
Wed
Escalations surface to leadership
High-value or cross-category decisions are escalated automatically. Leadership reviews a curated list — not an inbox of unresolved queries.
Fri
SLA report: 94% of cards actioned
Full audit trail. Decisions approved, rejected, or escalated — with timestamps, owners, and financial outcomes tracked automatically.
Accountability structure
Decision ownerAssigned before queue opens
$ impact visibleOn every card
Approval traceabilityFull audit log retained
Escalation pathBuilt into every card
SLA actioning rate94% within SLA
Your category managers stop investigating. They start deciding.
30–40% of their weekly time returned. Used for strategic work, supplier relationships, and market-building activity — not dashboard synthesis.
What this foundation enables
The Decision Framework is not just today's feature. It is what makes everything else possible.
Today
Contextual analysis
What happened, why it happened, and which node in the causal chain is responsible. Clear, grounded, auditable explanations — not fluent guesses.
Live now
2026
Prescriptive & scenario modelling
What will happen if you act — or don't. Scenario simulation across the causal chain, so your team can test decisions before approving them.
In development
Future
Agentic execution
Validated decisions sent directly to your ERP — within the guardrails you define. The Decision Framework is what makes autonomous execution safe and auditable.
On the roadmap
Who uses Ariane RDS
Decisions that used to take days. Now take seconds.
Commercial teams at leading retailers and FMCG manufacturers use Ariane to move faster, with more confidence, every week.
Before Ariane, decisions were made in weekly meetings based on whoever argued loudest. Now we have a queue. Everyone knows what's pending, what it costs to wait, and who's responsible. It changed how we operate as a commercial function.
SC
Sarah C.
Chief Commercial Officer
Global FMCG · $800M revenue
We were sitting on $2M of unacted inventory decisions every quarter. Ariane surfaced them with deadlines and P&L impact attached. In 6 weeks we'd cleared the backlog and our stock cover normalised for the first time in two years.
MR
Marc R.
VP Category Management
Regional Grocery Retailer · 195 stores
What I value most is the rationale. Every card tells you exactly why — which node triggered it, what the threshold breach was, what the downstream risk is. My team stopped arguing about the data and started making calls.
AL
Amelia L.
Head of Commercial Planning
FMCG Manufacturer · Top 10 Category
Two ways in. One decision engine.
Request a Margin Assessment. Or see the engine first.
Whether you're ready to quantify your margin leakage directly, or you want to see the system in action first — both paths lead to the same place.
Engagement-based pricing. Scoped to your SKU estate and store count.
30 minutes. Live on a real retail dataset. See exactly how Ariane surfaces, structures, and queues commercial decisions — before you commit to anything.
Both engagements run on your own data · No generic demos · No commitment required
How It Works
Decision Centres. Not dashboards.
A Decision Centre is a focused workspace for one type of commercial decision. Everything is pre-analysed and queued — your team approves, rejects, or escalates. And moves on.
5
Decision Centres
0
Dashboards to search
1
Action per decision card
What Is a Decision Centre
One focus. One queue. One output.
Instead of searching through dashboards and reports, everything is pre-analysed and queued. Each Decision Centre owns a specific commercial domain — pricing, range, promotions — and shows your team exactly which products need a decision this week.
Each decision card carries a recommended action, a $ impact, a deadline, and an owner. Your team doesn't investigate. They decide. Approve, reject, or escalate — and move to the next one.
Decision type
Pre-analysed & queued
Time to decide per card
Under 30 seconds
Output on approval
Executable document
Owner assignment
Before it reaches the queue
Escalation path
Built into every card
The Protocol
From raw data to approved action
Step 01
Data ingested
Sales, inventory, shopper, supplier, and promotional data flows in and is normalised across all stores and SKUs.
Step 02
Logic applied
The causal waterfall runs — from Inventory and Supplier up through Distribution, Range, and Promo to Sales and Margin.
Step 03
Decisions queued
Each anomaly, opportunity, or risk becomes a decision card — ranked by $ impact and routed to the right Decision Centre.
Step 04
Team acts
Approve, reject, or escalate each card. Approved decisions generate executable documents — RFAs, planograms, supplier briefs.
Pure Retail Logic
No black box. Every step is visible.
Ariane doesn't guess. Every recommendation traces back through a deterministic causal chain — from Supplier and Inventory all the way up to Margin. You can see every step, understand every signal, and follow the reasoning behind every card.
And because no two retailers are the same, the parameters that drive the logic are yours to calibrate — thresholds, cluster definitions, margin targets, ranging policies. Ariane applies your strategy at scale. Not someone else's.
Adjustable parameters · examples
Distribution threshold
≥ 60% stores
Margin floor
28% GP
Promo ROI minimum
1.4× baseline
Store cluster model
Your segmentation
Traffic decline trigger
−8% vs. LY
The Ariane Causal Chain
MARGIN
↑
SALES
Shopper behaviour
TRAFFIC
SPENDING
Commercial levers
RANGE
PROMOTION
PRICE
DISTRIBUTION
Operational enablers
INVENTORY
↑
SUPPLIER
Every decision card points to a node in this chain. You always know which lever is being pulled — and why.
What It Delivers
5 Decision Centres. 5 commercial outputs.
Each centre owns one domain. One queue. One type of action.
Finance
Top-Line Audit — Monitors Margin and Sales results to protect P&L from baseline erosion. Weekly variance report by category and store cluster.
+2.4%
margin recovery
Range
ADD / DELETE Actions — SKU-level instructions by store cluster. Optimises planograms and solves range gaps to recover lost traffic.
$366K
identified uplift
Promotion
Repeat, Fix, or Stop — Scores every promo by ROI and shopper response. Tells you which to run again, which to renegotiate, and which to kill.
−18%
wasted promo spend
Shopper
Campaign Execution — Targeted actions for lapsed shoppers and high-value category opportunities. Briefing-ready for your agency or CRM team.
3.1×
rescue rate
Supplier
Collaboration Strategy — Decides which Range, Promo, and Inventory actions to negotiate per supplier. Walk into the room with data, not opinions.
+12%
JBP realisation
The Resulting Action
No suggestions. Only decisions.
Every analysis ends with a single decision card. Your team doesn't investigate — they act.
Pre-analysed, not raw data
The card arrives with the diagnosis already done. Your team reads the instruction, not a report.
→
One action, not a menu of options
Ariane prescribes the specific move. Approve, reject with a reason, or escalate — nothing else needed.
Approval generates a document
Clicking Approve RFA produces a ready-to-send Range For Approval — a real executable document, not a screenshot.
Deep dive without leaving the queue
Need more context? The Universal Product Navigator is one click away — full history and competitive data, without losing your place.
RANGE DECISION CENTRE
CARD 7 OF 23
Range Gap Detected
DELETE SKU 4821
Chicken Tikka Masala 400g · 45 of 200 stores
Remove from cluster B stores. Re-allocate 1 facing to SKU 2205. Resolves a range gap responsible for 12% traffic loss in the Indian ready meals category since week 14.
$ Impact
$366K
Deadline
This week
Owner
S. Patel
Two ways to engage
See the engine live. Or audit your category's margin.
Both engagements run on your own category data
Why Ariane is different
AI that understands retail. Not just describes it.
Most AI tools sit on top of your data and generate fluent explanations. Ariane reasons inside a pre-built model of how retail commerce actually works — before a single word is written.
The foundation
We encoded retail logic first. Then trained the AI on it.
A Retail Knowledge Map is a formal model of how commercial variables causally relate to each other — not statistically, but structurally. It defines that Distribution causes Traffic, that Traffic causes Sales, that Supplier reliability enables Inventory. These are not inferences. They are rules.
When Ariane's LLM generates a contextual analysis, it reasons within these rules — not around them. It cannot hallucinate a causal explanation that violates retail logic, because the Retail Knowledge Map makes that structurally impossible.
Logic type
Deterministic causal
Causal nodes in the model
10 core nodes
Hallucination risk
Structurally constrained
Explanation type
Auditable & traceable
Built from
30+ yrs retail expertise
The model in practice
The causal chain — real retail examples
This is the structure Ariane reasons within. These are not hypothetical. These are the types of causal chains Ariane identifies every week — and the decisions it generates as a result.
PROMOTIONS · MARGIN LEAKAGE
Promotion uplift masking category margin decline
Promotion uplift on Brand X generated 14% volume increase — visible as a "success" in every dashboard. But the promotion caused cannibalisation in 3 adjacent SKUs, reducing category margin by 1.2pp despite the headline revenue growth. Net category P&L: −$88K.
Ariane identified the causal interaction between promotion mechanics and cross-SKU cannibalisation, and recommended withdrawal in 42 stores — saving $88K over the remaining promotion window.
Causal path
PROMOTION
↓ triggers
CANNIBALISATION
↓ reduces
MARGIN −1.2pp
RANGE · TRAFFIC RECOVERY
Distribution gap causing traffic decline across cluster
Distribution for SKU 4821 fell from 78% to 55% over 6 weeks — crossing the 60% threshold in cluster B stores. Ariane traced the distribution gap to a range deletion decision 8 weeks earlier. The resulting traffic shortfall accounted for 11pp of a 14pp sales decline in the category.
Ariane issued a Range Add decision card with a $366K financial impact, assigned to the category manager with a deadline of the current week — before the next planogram cycle closed.
Causal path
SUPPLIER FILL-RATE ↓
↓ caused
DISTRIBUTION 55%
↓ caused
TRAFFIC −11pp
↓
RANGE ADD → +$366K
PRICE · VOLUME RECOVERY
Price index above threshold driving market share loss
SKU 5512 price index reached 108 vs. competitor set — 5 points above the shopper sensitivity threshold identified in Ariane's elasticity model. Volume share had declined 5pp over 4 weeks. A 4% reprice was projected to recover the lost share within 6 weeks with a net $ uplift of +$869K.
Ariane issued a Price decision card — not a report on competitor prices, not a chart — a single recommended action, with a deadline and a projected P&L outcome attached.
Causal path
PRICE INDEX 108
↓ above threshold
SPENDING ↓ (shopper)
↓
REPRICE −4% → +$869K
Live trace · Indian Ready Meals · Week 18
0/6 steps
▶
Watch a real cascade
Press Play to trace how a supplier fill-rate drop in week 16 caused a 14% sales decline by week 18 — step by step through the causal chain.
Each node lights up as the failure propagates upward
Step 1 of 6
SUPPLIER
Fill rate drops
Loading...
Root enabler
Supplier
Service level, fill rate, lead times. The origin of most Inventory problems.
Threshold: ≥ 95% fill rate
Operational enabler
Inventory
Stock depth and availability. Enables Distribution across the store estate.
Threshold: ≥ 14 days cover
Commercial lever
Distribution
Physical availability across stores. A prerequisite for Traffic and Sales.
Threshold: ≥ 60% of estate
Commercial lever
Range
Which SKUs are listed in which clusters. Gaps directly cause Traffic loss.
Threshold: core SKUs in all clusters
Shopper behaviour
Traffic
Shoppers visiting or engaging with the category. Driven by Range and Distribution.
Trigger: −8% vs. LY
Result
Sales
Total revenue. Traffic × Spending, reflecting all lever states below.
−14% in week 18 → decision card issued
Ultimate result
Margin
Gross profit after all levers are applied. The final measure of commercial health.
Threshold: ≥ 28% GP floor
Shopper behaviour
Spending
Average basket value per shopper. Driven by Promotion and Price. Rises with effective mechanics, falls with poor execution.
Trigger: −5% avg basket vs. LY
Commercial lever
Promotion
Price mechanics and activation. Drives Spending but erodes Margin if ROI is below threshold.
Threshold: ROI ≥ 1.4× baseline
Commercial lever
Price
Base price positioning. Affects both Spending and Margin directly. Monitored vs. competitor index.
Trigger: price index > 105 vs. competitor
The difference in practice
Same LLM technology. Completely different architecture.
Both answers below come from a large language model. The difference is what the LLM is given to work with — raw data and a vague question, or a structured causal diagnosis it has been asked to express clearly.
Generic AI analytics
Question asked
"Why did Indian ready meals sales drop 14% in week 18?"
Answer generated
Sales in the Indian ready meals category declined 14% in week 18 compared to the prior week. This may be related to reduced promotional activity, seasonal trends, or changes in consumer purchasing behaviour. It is recommended to review the promotional calendar and consider running targeted offers to stimulate demand.
Ariane RDS — knowledge-grounded
Question asked
"Why did Indian ready meals sales drop 14% in week 18?"
Answer generated
Root cause: Range gap → Traffic loss. SKU 4821 (Chicken Tikka Masala 400g) was delisted from 45 cluster B stores following the Q2 ranging review. Distribution fell from 78% to 55% — below the 60% threshold defined in your model. This triggered a Traffic ↓ event in cluster B, accounting for 11 of the 14 percentage points of the sales decline. The remaining 3pp reflects a natural volume shift to SKU 2205, which is under-ranged to absorb it. No promotional factor is implicated.
The AI layer
The LLM reasons within the Retail Knowledge Map. Not around it.
The language model in Ariane is not given raw data and asked to explain it. It is given a structured representation of what the Retail Knowledge Map has already diagnosed — and asked to express that diagnosis clearly, specifically, and in context.
The result is analysis that is grounded, constrained, and auditable — not fluent guesswork. Every sentence maps back to a node, a threshold, and a dataset.
Retail Logic first, language second
The causal diagnosis happens before the LLM speaks. Language is the output layer, not the reasoning layer.
Constrained by structure
The LLM cannot propose a causal explanation that violates the retail model. Causal inversion is structurally impossible.
Every analysis is traceable
Each explanation references a specific node, a threshold breach, and the data that triggered it. No black box.
Built for retail, not general use
The model knows the difference between a range gap and a promotional void, a traffic problem and a spending problem. General AI tools do not.
What this foundation enables
The Decision Framework is not just today's feature. It is what makes everything else possible.
Today
Contextual analysis
What happened, why it happened, and which node in the causal chain is responsible. Clear, grounded, auditable explanations — not fluent guesses.
Live now
2026
Prescriptive & scenario modelling
What will happen if you act — or don't. Scenario simulation across the causal chain, so your team can test decisions before approving them.
In development
Future
Agentic execution
Validated decisions sent directly to your ERP — within the guardrails you define. The Decision Framework is what makes autonomous execution safe and auditable.
On the roadmap
Two ways to engage
See the engine. Or start the diagnostic.
The Ariane Platform
One system. From data to decided.
Ariane is not a collection of tools. It is a single commercial intelligence platform — with a decision engine at the centre, and a set of purpose-built capabilities that feed it, support it, and execute what it recommends.
How the platform works
01 — Sense
Ingest & model
Sales, pricing, stock, supplier, promo data unified in a causal Retail Knowledge Map
Forecasting · Dashboards
02 — Decide
Ariane RDS
The decision engine. Surfaces what matters, builds decision cards, routes to the right team
Decision engine
03 — Execute
Act & optimise
Approved decisions become briefs, planogram updates, supplier notes, CRM campaigns
Assortment · NBO · CRM
04 — Learn
Measure & close
Outcomes feed back into the models. Every decision improves the next recommendation
Vendor-agnostic by designAriane connects to any ERP, POS, or data source through standard APIs and file-based ingestion — no platform lock-in required.
The capabilities that surround the engine
Each capability does one job inside the platform workflow. Together they give your team everything needed to sense signals, decide confidently, and execute without friction.
Sense
Sales forecast
Forward-looking volume and revenue models at SKU × store level. Every decision card is backed by a forecast — so you know the $ impact before you approve.
"RDS uses this to put a financial value on every card before it reaches your queue."
Sample output ▾
Forecast card — SKU 4821 · Comfort Original 1.5L
4-week forecast (base)$142,300
Forecast if delisted−$366k annualised
Substitution capture (SKU 2205)+$88k estimated
Net decision value−$278k · Act this week
→ Attached to Range Decision Centre card
Sense
Retail expert dashboards
Pre-built views for range, price, promo, and supplier performance — built on retail expertise, not generic BI. Open from any decision card for full category context in one click.
"Gives category managers the context to interrogate a decision without building a report."
Sample output ▾
Pre-built view — Pricing dashboard
Fabric conditioners · Cluster B · Wk 18
Price index vs market108 (target: ≤104)
SKUs above index threshold7 of 23
Volume share trend (4wk)−2.1pp
Active reprice cards3 in queue
→ Linked to Price Decision Centre
Execute
Assortment optimisation
Cluster-level range planning against your optimal assortment model. Range decision cards arrive pre-scored — your team acts on options, not open questions.
"Powers the Range Decision Centre. Scores every listing and delist candidate."
Sample output ▾
Assortment score — Cluster A · Premium fabric softeners
Category gap identified23% — premium segment
Recommended new listingComfort Jasmine 1.4L
Forecast Y1 incremental+$373k
Cannibalisation riskLow — 4% overlap
→ Issued as a Range +SKU decision card
Execute
Next best offer
Personalised promotion recommendations built on basket data, shopper segments, and price elasticity. Feeds the Promotions Decision Centre with pre-ranked mechanic options.
"Each promo card arrives with ranked mechanics already attached — your team chooses, not guesses."
Once a promotion is approved in RDS, audience segments and campaign briefs are generated automatically — closing the loop between the commercial decision and customer-facing execution.
"Approve a promo card. The campaign brief is ready in minutes."
Sample output ▾
Auto-generated brief — Facial Scrub 10% discount
Target segmentBeauty buyers · lapsed 30d
Audience size42,300 shoppers
ChannelPush + email
Campaign windowApr 5–12, 2026
→ Ready to send to campaign platform
Ariane RDS
The decision engine is still the product.
The capabilities above support and extend it — but the platform is designed around a single idea: every commercial decision, surfaced with the right context, at the right time, with a financial value attached.
See Ariane RDS →
2.5–4.5%
avg. sales increase across categories
0.3–0.6%
avg. margin recovered per retailer per year
12 hrs
saved per category manager per week
94%
of decision cards actioned within SLA
See the full platform in your context.
We'll show you how the workflow maps to your team structure and data sources.
How Ariane works
From raw data to a decided action.
Three steps that transform how your commercial teams operate — every category, every week — with a choice of how far to automate each decision.
The workflow
Sense. Decide. Execute.
Ariane continuously monitors your full SKU estate and converts every signal into a structured decision — valued, prioritised, and ready to act on.
Step 01
Surfaces what matters
Ariane continuously analyses your full SKU estate — sales index, margin, stock cover, competitor pricing, promotional performance, shopper behaviour — and identifies exactly where action is needed this week.
Sales & margin trends
Competitor price gaps
Stock cover anomalies
Cannibalisation signals
Step 02
Builds decision cards
Every opportunity becomes a structured Decision Card — with a recommended action, a financial impact, a deadline, and an owner. Your team opens the tool and knows exactly what to decide, in what order, and why.
$ impact on every card
Deadline & owner assigned
Recommended action included
Escalation paths built in
Step 03
Your team decides — or Ariane does
Manual validation or agentic execution — within guardrails you define. Each approved decision generates an executable output: a pricing brief, a planogram update, a supplier negotiation brief.
Approve, reject, or escalate
Executable outputs generated
ERP integration on roadmap
Full audit trail maintained
Control & governance
Your team stays in control.
Ariane works within the boundaries you set. Every decision has a human owner — and the system only acts automatically where you explicitly allow it.
Manual validation
Your team reviews every card. Approve, reject, or escalate — Ariane never acts without a human decision.
Agentic execution
Within guardrails you define, Ariane acts automatically — generating and routing approved outputs without human sign-off on each card.
2.5–4.5%
Sales increase across categories
30–40%
Time saved per category manager per week
0.3–0.6%
Margin recovery per retailer per year
94%
Of decision cards actioned within SLA
Ready to see it in action?
See the engine. Or audit your margin.
Two ways to engage — both run on your own retail data.
About Hypertrade
Built by retail practitioners. Deployed in 15 markets.
Hypertrade is a retail intelligence company headquartered in Southeast Asia. We build systems that help commercial teams make better decisions — faster, with better data, and with financial accountability attached to every action.
Our thesis
The retail operating model needed to be rebuilt — not optimised.
We started with a question: why do the world's most data-rich retailers still make major commercial decisions in spreadsheets and weekly meetings? The answer was not capability — it was architecture. The systems built to support commercial teams were observation systems. They described the past. They did not prescribe the future.
Ariane RDS is the answer we built. A decision engine — not a dashboard. Grounded in the world's first causal Retail Knowledge Map, powered by a large language model, and designed to produce one thing: a structured queue of commercial decisions with financial value attached to every card.
Markets deployed15+
Retail categories covered40+
Decision cards processed weekly10,000+
Years of retail domain expertise encoded30+
Headquartered in Southeast Asia. Built for modern retail markets.
We understand the complexity of retail in high-SKU, high-volume, multi-format markets across Southeast Asia, the Middle East, and beyond. Ariane is not a Western retail tool retrofitted for emerging markets — it was designed for them.
Resources & Documentation
Reference material for every stakeholder.
Technical documentation, executive briefings, and methodology papers for commercial, technical, and executive audiences.
Downloads
Three documents. Three audiences.
Technical Paper · PDF
Methodology
The Causal Retail Knowledge Map: A Technical Whitepaper
How Ariane encodes retail causality — from supplier fill-rate to margin — and why structural grounding eliminates hallucination in commercial AI systems.
Ariane Margin Assessment: Diagnostic Scope & ROI Framework
What the 12-week margin recovery audit covers, what you receive, and how to calculate indicative EBIT impact before committing to the engagement.
Scope & deliverablesROI calculatorBoard-ready format
Audience: CEO · CFO · CCORequest →
Architecture Doc · PDF
Enterprise Security
Enterprise Data Handling & Security Protocols
Tenant isolation, encryption at rest and in transit, RBAC, audit log architecture, human-in-the-loop governance, and data residency options — for IT, Legal, and procurement review.
EncryptionRBAC & audit logsGovernance
Audience: CTO · IT · ProcurementRequest →
All documents are available on request — sent within one business day. No automated download gates. You'll receive the document directly from our team.
Ready to go deeper?
See the engine. Then audit your margin.
For Retail CEOs
Your margin is leaving. Ariane shows you where.
Every week, your category teams make hundreds of decisions on range, price, and promotions — without a financial value attached to each one. Ariane quantifies what every decision is worth, and what it costs to delay it.
Ariane Margin Assessment — indicative output
Margin at risk (Ranging)$1.2M
Margin at risk (Promotions)$840k
Combined recoverable margin$2.04M
Assessment feeUSD 10,000
Credited in full if you proceed✓ Credited
The cost of the status quo
Slow decisions are expensive decisions.
The margin isn't disappearing because your team is incapable. It's disappearing because the system isn't built to put a financial value on every decision before it's made.
Without Ariane
Ranging decisions made in weekly meetings — no financial value attached before the room decides
Promotions assessed after the fact — ROI calculated when the window has already closed
High-impact decisions buried in a category manager's inbox alongside low-impact ones
No audit trail — when margin erodes, it's hard to know which decision caused it
With Ariane
Every decision surfaces with a $ value, a deadline, and a recommended action — before the meeting
Promotions are scored before they run — your team approves the mechanic with ROI already calculated
Decision queue ranked by financial impact — your team always works on what matters most
Full audit trail — every decision, every outcome, every week. Board-ready reporting built in
What changes
Three outcomes. Measurable from week one.
Ariane doesn't require a transformation programme. It runs on your existing data and your existing team — and produces measurable outcomes within the first quarter.
Margin recovered
Ariane identifies margin leaking through slow or missed decisions on ranging and promotions — and quantifies it before your team acts.
0.3–0.6% of revenue recovered per year
Speed of execution
Decisions that used to take 7–10 days from signal to action are approved in under 30 seconds — with the financial value already calculated.
94% of decision cards actioned within SLA
Sales growth
Faster, better-informed ranging and promotional decisions compound over time — driving measurable sales uplift across the categories where Ariane runs.
2.5–4.5% sales increase across categories
The Ariane Margin Assessment
An executive commercial diagnostic. Not a software trial.
We run a structured 12-week engagement on one category of your data — mapping your margin leakage, surfacing your decision backlog, and delivering a quantified, board-ready commercial recovery report. The $10,000 engagement fee is not a trial barrier. It is a diagnostic investment.
No commitment. We'll tell you on the call whether the engagement is right for your business — before the fee is raised.
USD 10,000 · Credited in full if you proceed · Report yours either way
For Chief Merchandising Officers
Every decision, valued before you make it.
Ariane surfaces your ranging, pricing, and promotional decisions as structured cards — each one with a financial impact, a recommended action, and a deadline. Your team decides. Ariane makes sure nothing falls through the cracks.
Live decision queue — Fabric Conditioners · Wk 18
🔴 DELIST · SKU 4821 Comfort 1.5L$366k
🟡 REPRICE · SKU 5512 Comfort 900ml+$869k
🟢 +RANGE · Comfort Jasmine 1.4L+$373k
Total queue value this week+$876k net
The decision centres
Built for every merchandising decision.
Ariane runs six Decision Centres simultaneously — each monitoring a different commercial dimension of your category estate, generating cards only when action is needed.
Range
ADD / DELETE instructions by store cluster. Identifies range gaps, over-ranging, and cannibalisation — each with a net P&L impact attached.
Example: Delist SKU · net +$44k/period
Promotions
Pre-scored promotional mechanics before the campaign runs. ROI calculated, audience segments generated, mechanic ranked — your team approves, not guesses.
Example: Repeat promo approved · ROI 1.2 · +$224k
Pricing
Price index monitoring across your full estate. Flags SKUs above competitive threshold with elasticity-modelled reprice recommendations and volume recovery estimates.
Example: −4% reprice · +5pp volume share recovery
Replenishment
Stock cover anomalies by SKU and store cluster. Identifies over-stock, near-OOS, and supplier delivery gaps — with recommended order adjustments and financial exposure.
15% inventory reduction on average
Supplier
Monitors fill rate, lead time compliance, and promotional execution by supplier. Flags performance gaps and generates supplier brief templates for trading conversations.
Automated brief generated on each card
Finance
Top-line P&L audit — monitors margin and sales results by category and store cluster. Flags baseline erosion before it compounds and generates weekly variance reports.
0.3–0.6% margin recovery per year
The Report Builder
250+ KPIs. Your data. Your reports.
Report Builder gives every authorised user the ability to build, schedule, and share exactly the commercial view they need — across the full Sense → Decide → Execute → Learn workflow.
Sense-stage KPIs
Category baseline, price index, stock cover, supplier fill rate, promo calendar visibility — everything your team needs to understand the landscape before a decision is made.
Decide-stage KPIs
Decision queue volume, approval rate, average card value, time-to-decision by Decision Centre — governance metrics your commercial director and CFO can track weekly.
Outcome KPIs
Decision P&L, promo ROI actuals vs forecast, range coverage delta, forecast accuracy — every decision tracked to its commercial outcome. Board-ready reporting built in.
2.5–4.5%
Sales increase across categories
0.3–0.6%
Margin recovered per year
30–40%
Time saved per category manager weekly
250+
KPIs available in Report Builder
See it on your categories
See the engine live. Or go straight to the diagnostic.
30 minutes on your real data, or a 12-week margin recovery audit — both run on your category, your SKUs.
For CTOs
LLM-powered. Logic-grounded. Vendor-agnostic.
Ariane is not a black-box AI tool. It is a large language model grounded in a causal Retail Knowledge Map — meaning the LLM reasons within a structured retail knowledge graph, not from raw unstructured data. Here is exactly how it works.
Technical architecture — at a glance
AI layerLLM + Retail Knowledge Map
Data ingestionAPI + file-based
ERP / POS compatibilityVendor-agnostic
DeploymentCloud / hybrid
Data refreshWeekly (live during engagement)
Platform architecture
Sense → Decide → Execute → Learn.
Four stages. One continuous loop. Ariane RDS sits at the Decide stage — the LLM engine that generates structured decision cards from the causal diagnosis produced upstream.
01 — Sense
Data ingestion & modelling
EPOS, ERP, supplier, pricing, and loyalty data normalised across all stores and SKUs into the Retail Knowledge Map
Forecasting · Dashboards
02 — Decide
Ariane RDS
LLM grounded in the Retail Knowledge Map generates decision cards — each with a causal diagnosis, recommended action, and financial value
Decision engine
03 — Execute
Action & integration
Approved decisions generate executable outputs — briefs, planogram updates, CRM campaign briefs — routed to downstream systems
Assortment · NBO · CRM
04 — Learn
Outcome feedback
Decision outcomes feed back into forecast models. Every actioned card creates a record that improves the next recommendation cycle
Report Builder · 250+ KPIs
The AI layer
What is rules-based. What is ML. What is LLM.
Ariane uses all three — but in the right places. The architecture is designed so the LLM never hallucinates commercial decisions, because it is never given open-ended questions against raw data.
Rules-based layer
Threshold detection, anomaly flagging, and priority scoring are deterministic — defined by your team and the Retail Knowledge Map. No ML involved. Fully auditable and configurable.
Triggers: price index, stock cover, distribution thresholds
ML layer
Sales forecasting, price elasticity modelling, substitution estimation, and promotional uplift prediction. Trained on retail-specific patterns, updated weekly from your own data.
Powers: financial value on every decision card
LLM layer
The LLM receives a structured causal diagnosis from the Retail Knowledge Map — not raw data. It generates the rationale, recommended action, and escalation reasoning on each card. Grounded output, not open-ended generation.
Ariane is architected to pass enterprise security review — with the governance, auditability, and access controls that IT, Legal, and Compliance teams require before deployment.
RBAC & Role Permissions
Role-based access controls define who can view, approve, escalate, or override decisions. Permissions are configurable per Decision Centre, category, and team.
Category manager · Commercial director · Executive · Read-only
Audit Logs & Approval Traceability
Every decision card carries a complete audit trail — who reviewed it, what action was taken, when, and with what financial outcome. Stored in your environment.
Timestamped · Owner-attributed · Exportable
Human-in-the-Loop Governance
Ariane never executes autonomously without explicit permission. All agentic execution requires pre-defined guardrails — category-level, value-threshold, and action-type controls.
Guardrail configuration required before agentic mode
Explainability & Hallucination Mitigation
Every decision card rationale references a specific causal node, threshold breach, and dataset. The LLM cannot generate explanations that contradict the Retail Knowledge Map — structural grounding eliminates hallucination.
No black box — every output is traceable
Tenant Isolation & Encryption
Data is processed in isolated tenant environments. All data in transit and at rest is encrypted. No cross-tenant data sharing. Data residency options available on request.
AES-256 encryption · TLS 1.2+ in transit
Deployment Flexibility
Cloud-hosted SaaS with private deployment options. Weekly data refresh cadence during the Margin Assessment. Cadence is configurable for full deployment.
SaaS · Private cloud · On-premise roadmap
Integration Ecosystem · Named integrations. No proprietary middleware.
ERP
SAP S/4HANA
Stock, orders, supplier data, cost price via standard API
ERP
Oracle Retail
Merchandising, planning, and inventory data extraction
ERP
Microsoft Dynamics
D365 Finance & Supply Chain integration
Data Platform
Snowflake
Direct data share — no export required
Data Platform
Databricks
Lakehouse integration for large-scale retail data
Cloud
AWS · Azure
Hosted deployment or bring-your-own-cloud options
POS / EPOS
Any point-of-sale
Standard extract formats: CSV, JSON, SFTP
Price Intelligence
Any price feed
Competitor pricing via API or scheduled file delivery
No lock-in. No proprietary middleware.Ariane connects through standard REST APIs and scheduled file-based ingestion. Your data team connects once — the platform handles the rest. Data residency options available on request.
Data Governance · What Ariane touches. What stays yours.
What Ariane reads
EPOS transaction data — aggregated at SKU × store × week level
ERP stock and order data — normalised against the Retail Knowledge Map
Competitor price feeds — external signals fed into the causal model
Historical promotional performance — for uplift and ROI modelling
What stays in your environment
Raw PII and individual shopper transaction records — never processed by Ariane
Supplier contract terms and commercially sensitive pricing agreements
Decision outputs — RFAs, planogram briefs, supplier notes — generated and stored in your environment
Audit trail — full decision history stored in your systems, not ours
3-layer
AI architecture: rules · ML · LLM
Any
ERP, POS, or data source — vendor-agnostic
Zero
Raw PII processed by Ariane
RBAC
Role-based access · Full audit log · Explainable outputs
Implementation Map
Three steps. Twelve weeks. Live on your data.
The path from contract to first decision card is designed to remove every integration barrier. No transformation project, no lengthy data preparation — connect, calibrate, and go live.
Step 1 · Weeks 1–2
Secure Data Connection
Your data team connects via standard REST API or SFTP. Ariane ingests, normalises, and validates your ERP, EPOS, and competitor pricing feeds. Tenant-isolated environment provisioned from day one.
SAP / Oracle / Dynamics compatible
Snowflake data share supported
Encryption at rest and in transit from day 1
Step 2 · Weeks 3–4
Retail Knowledge Mapping
The Retail Knowledge Map is calibrated to your specific category, store cluster structure, and operational thresholds. Guardrails, margin floors, and decision parameters are configured with your commercial team.
Category-specific threshold calibration
RBAC and team permissions configured
Audit log and escalation paths defined
Step 3 · Week 5 onward
Decision Queue Go-Live
Decision cards refresh weekly. Your team approves, rejects, or escalates — with outcomes tracked from the first session. Week 12 delivers the board-ready margin recovery report.
Weekly decision queue refreshed overnight
Full audit trail and SLA tracking live
Board report delivered at week 12
No transformation project required. Ariane connects to what you already have. The Margin Assessment is designed to remove every integration barrier before you commit to full deployment. Architecture documentation and security questionnaire response within 5 business days.
Technical questions welcome
Talk to our technical team.
Architecture, integration requirements, governance, and data handling — no sales pitch, just direct answers from the team that built the system.
Architecture documentation available on request · Security questionnaire response within 5 business days
Careers at Hypertrade
Join our Retail Analytics Revolution.
Transform the shopping experience for millions with Hypertrade's retail analytics technology.
Your work flows directly into pricing, promo, range, and supply decisions across 4 continents — outcomes measured in margin, not impressions.
Modern data stack
Big data, real engineering.
Cloud-native (AWS/GCP), BigQuery, streaming ETL, retail-scale APIs, CI/CD by default. Optimization and cost savings are first-class problems, not afterthoughts.
Growth & mentorship
Architects, not ticket-closers.
Senior engineers mentor through architecture decisions, code reviews, and process improvements. Career paths into technical leadership are explicit, not implied.
Where you'll fit
Teams hiring now.
Backend & Cloud
Backend Engineer · BigQuery / ETL
Design and operate the data pipelines, APIs, and services that power Decision Cards at retail scale. AWS/GCP, BigQuery, Node/Go.
Remote-friendly · Mid & Senior
Data Platform
Data Engineer · Retail Analytics
Build the causal retail knowledge map: ingestion, modeling, performance, and data security across multi-tenant retail estates.
Hybrid · Mid & Senior
DevOps & SRE
DevOps Engineer · CI/CD & Cost
Own CI/CD, observability, cost optimization, and reliability of the platform. Strong AWS/GCP and infra-as-code background.
Remote-friendly · Senior
Frontend
Frontend Engineer · Decision UX
Craft the Decision Card experience used by retail commercial teams every day. React/TS, design-system rigour, accessibility-first.