AI that explains why revenue dropped — not just what?
Yes — that is what Adaptrix is built for. AI demand forecasting predicts what you will sell; explainable driver attribution with SHAP explains why sales dropped — price, stockouts, promotions or seasonality — and AI agents act on the answer. Self-hosted in the EU, GDPR-first, fixed annual pricing with €0 per-token.
See it work
Ask in plain language. Get an explained, cited decision.
One question routes through specialised agents — schema, SQL, forecast, root cause and governance — and lands as an auditable answer with the evidence attached.
Director· Retail workspace
DemoWhere is weekly demand heading, and should we reorder?
Director Highanswer in 9.7s
- Analysing demand
- Scanning orders + sku_sales
- Ranking the drivers of the change
- Formatting the explained answer
scan Scan sku_sales · 8.4M rowsfilter Filter the affected regionsaggregate Aggregate demand by segmentrank Rank driver contributions
A category shift plus a lapsed promo pull demand below reorder.
Why demand movednet -4.2k
- 60% of the moveCategory A demand shift-2.5k
- Promo lapse-1.2k
- Stockouts-0.5k
Adaptrix recommends
- Hold reorder on Category A one cycle
- Reinstate the lapsed promo on top SKUs
Projected · recover to 37k units within a month
GDPR · checkpoints cleanSQL + provenance attachedAudit logged
Ask a follow-up…
Illustrative scenario shown in a demo workspace — figures for demonstration, not a customer result.
Ask it about your operation
Why did weekly demand shift — and should you reorder?
A category shifts, a promo quietly lapses, a stockout compounds the dip — and suddenly the reorder decision is a guess. A dashboard shows the demand curve bending; it does not tell you which of those three moved it.
Adaptrix reads orders, SKU sales, returns and inventory together, forecasts demand with a 95% confidence interval, and attributes the shift to signed drivers that sum to the change — so the reorder call is grounded in why demand moved, not just that it did.
- See the demand shift before the reorder deadline forces the guess
- See which category, promo or stockout drove it — signed, summing to the move
- Act on a recommendation with the projected recovery attached
Scripted replay with illustrative data — this is the real answer view, not a video.
Q
9.7s·auditedWhere is weekly demand heading, and should we reorder?
- Parsing the question — metric, segments, horizon
- Linking 4 connected sources
- Forecasting with a 95% confidence interval
Weekly Demand · 12 wk actuals + forecast
forecast 34.0k units
Why it moves
A category shift plus a lapsed promo pull demand below reorder.
Forecast34.0k units95% CI · 31.8k–36.2k units next month
- −2.5kCategory A demand shift · 60% of the move
- −1.2kPromo lapse
- −0.5kStockouts
Net change= −4.2k
Adaptrix recommends
- Hold reorder on Category A one cycle
- Reinstate the lapsed promo on top SKUs
Projected · recover to 37k units within a month
TraceGDPR · checkpoints cleanSQL + provenance attachedAudit logged
$1.73T
Annual global cost of out-of-stocks and overstocks (IHL Group, 2025)
20–50%
Forecast-error reduction from AI-driven demand forecasting (McKinsey)
Up to 65%
Reduction in lost sales from product unavailability (McKinsey)
Sources: IHL Group, “Retail Inventory Crisis Persists Despite $172 Billion in Improvements”, September 2025; McKinsey & Company, “AI-driven operations forecasting in data-light environments”. Linked below.
Why don't retail dashboards explain a revenue drop?
Dashboards show that sales fell — which store, which week, which SKU. They do not show why. Root-cause analysis still means days of manual cross-referencing across POS, ERP and promotion calendars.
Out-of-Stocks and Overstocks
Inventory distortion — lost sales from stockouts plus margin locked up in overstock — remains retail's largest hidden cost (IHL Group, 2025)
$1.73T global cost per year
Customer Behavior Blindspots
Disconnected data prevents understanding of cross-channel customer journeys
67% missed opportunities
Pricing Strategy Gaps
Static pricing models fail to respond to market dynamics and competition
12% margin erosion
Why did sales drop? How explainable AI answers it, step by step
When revenue falls, Adaptrix runs the same investigation a good analyst would — automatically, on every drop, in minutes.
1
Detect the drop
Anomaly detection on daily POS and e-commerce revenue flags the decline the day it starts — not at month-end reporting.
2
Decompose it
The drop is broken down by store, channel, category and SKU to isolate where revenue was actually lost.
3
Attribute the drivers
SHAP feature attribution ranks the candidate drivers — price changes, stockouts, promotion timing, weather, seasonality — by how much each contributed.
4
Attribute the drivers
SHAP-based driver attribution tests whether a suspected driver is actually behind the drop or merely correlated with it — did the price increase cost sales, or was it a competitor's promotion?
5
Act on the answer
AI agents turn the finding into action: a reorder proposal, a price correction, a stop on a cannibalising promotion — with a human approving each step.
The result is an explanation you can defend to the board, with the evidence attached — not a dashboard screenshot.
What changes with explainable AI instead of another dashboard?
Inventory Management
Traditional
Weekly manual stock counts, Excel-based reorder points
With Adaptrix
Real-time inventory AI predictions, automated reorder optimization
Fewer stockouts, less overstock
Customer Analytics
Traditional
Quarterly reports, siloed channel data
With Adaptrix
Automated 360° customer KPIs with Full-Stack Agentic Suite. GDPR-native processing
Zero-configuration setup
Dynamic Pricing
Traditional
Monthly price reviews, manual competitor analysis
With Adaptrix
Real-time pricing with self-hosted AI (zero recurring costs)
Enterprise-scale performance
How does AI demand forecasting work in Adaptrix?
Forecasting is a portfolio, not a single model. Adaptrix benchmarks classical time-series baselines against machine-learning models per SKU and picks what actually performs on your data.
Classical baselines first
Classical statistical forecasting models set an honest baseline per SKU and season — if the simple model wins, the simple model is used.
ML where it earns its place
Gradient-boosted and ensemble models add price, promotion and calendar effects where they measurably cut forecast error. McKinsey estimates AI-driven forecasting reduces errors by 20–50%.
Connected to your ERP and POS
Native connectors for SAP, Shopify, Square and standard SQL sources keep forecasts fed with live sales and stock data — no CSV exports.
Inventory optimization, not just prediction
Forecasts feed reorder points and safety-stock recommendations, so the output is an order proposal — not another chart.
Private by architecture
The LLM layer that explains results runs on self-hosted open models on Adaptrix's own EU infrastructure — so sales and customer data never go to a third-party AI provider. GDPR is the default, not an add-on.
Where does AI demand forecasting pay off first?
Pre-built retail workflows on your ERP and POS data — Shopify, SAP, Square — with time-to-value measured in weeks.
Inventory Optimization
Predict demand patterns, optimize stock levels, reduce waste
Time to value: 3 weeks
Customer Segmentation
Identify high-value segments, personalize experiences, increase CLV
Time to value: 2 weeks
Price Optimization
Dynamic pricing based on demand, competition, and inventory
Time to value: 4 weeks
Promotion Analytics
Measure campaign effectiveness, optimize promotional spend
Time to value: 2 weeks
Store Performance
Compare locations, identify best practices, optimize operations
Time to value: 3 weeks
Time-to-value figures reflect typical mid-market deployments; results vary with data quality and scope.
“We're witnessing a fundamental transformation in how successful retailers manage inventory. The data shows a clear bifurcation emerging: retailers deploying AI and machine learning are achieving sales growth 2.3 times higher and profit growth 2.5 times higher than competitors.”
What this looks like in practice
Illustrative scenario
Mid-market grocery chain (multi-location)
Challenge:
Roughly 80 stores; weekly revenue dips nobody could explain until month-end, and reorder decisions run from spreadsheets.
Solution:
Connected POS and ERP data, enabled AI demand forecasting per store and SKU, and switched on explainable drop analysis with agent-drafted reorder proposals.
Results:
- Revenue drops explained within a day, with drivers ranked
- Fewer stockouts on top-selling SKUs
- Reorder proposals drafted automatically, approved by category managers
- One weekly forecast review instead of ad-hoc spreadsheet analysis
Illustrative scenario
Mid-market fashion e-commerce retailer
Challenge:
Frequent promotions made it impossible to tell whether a sales dip was seasonality, price, or promotion cannibalisation.
Solution:
Deployed explainable driver attribution (SHAP) on promotion and pricing history, self-hosted so customer data stayed in the EU.
Results:
- Promotion cannibalisation identified and quantified per campaign
- Price changes assessed for driver impact before rollout
- Customer data processed GDPR-first on self-hosted infrastructure
- Marketing and merchandising working from one explainable picture
Scenarios are illustrative, modelled on typical mid-market retail deployments — they are not verified customer results.
Retail-Specific Features & Integrations
POS system integration (Square, Shopify, SAP)
E-commerce platform connectors (Magento, WooCommerce, BigCommerce)
Supply chain visibility dashboards
Customer journey mapping
AI demand forecasting (statistical and ML ensembles, cross-validated)
Automated alert system for anomalies
Mobile-optimized dashboards for store managers
GDPR-compliant customer data handling
Your 8-Week Retail Transformation Journey
Weeks 1-2
Data Integration
- Connect POS systems
- Import historical data
- Set up real-time feeds
Weeks 3-4
Dashboard Creation
- Build KPI dashboards
- Configure alerts
- Mobile optimization
Weeks 5-6
AI Model Training
- Train demand forecasting
- Customer segmentation
- Price optimization
Weeks 7-8
Go Live & Optimize
- Team training
- Process integration
- Performance tuning
Where to go next
Adaptrix for retail is part of a sovereign, GDPR-first analytics platform.
Frequently asked questions
Why did my sales drop — and can AI really tell me?
Yes, if it goes beyond dashboards. Adaptrix detects the drop, decomposes it by store and SKU, ranks candidate drivers with SHAP attribution. You get a defensible explanation — price, stockout, promotion or seasonality — usually within a day, not a quarter.
What is AI demand forecasting?
AI demand forecasting predicts future sales per SKU and location by combining classical time-series models with machine learning that captures price, promotion and calendar effects. McKinsey estimates it cuts forecast errors by 20–50% and lost sales from unavailability by up to 65%.
Which retail systems does Adaptrix connect to?
Native connectors cover ERP and POS systems including SAP and Shopify, plus Square and standard SQL databases. Historical sales, stock levels and promotion calendars are ingested automatically — no CSV exports or manual data modelling.
How is customer data kept GDPR-compliant?
The whole stack — including the LLM layer — runs on self-hosted open models in Adaptrix's EU infrastructure, so personal data never goes to a third-party AI provider. Automated PII detection and GDPR checkpoints apply to every query.
Do we need a data science team to use this?
No. Model selection, backtesting and monitoring are automated, and results are explained in plain language. Your category managers approve agent proposals; a data team is helpful but not required.
What does Adaptrix cost for a retail deployment?
Pricing is a fixed annual fee with €0 per-token — costs do not scale with how many questions your team asks. See the pricing page for tiers; typical mid-market deployments go live in about eight weeks.
Ask it why.
Bring a real question from your business and watch a live answer — reasoning, sources and math included — or model what it saves you first.