Pricing intelligence
Market-reading engines for real estate (EstateEdge live) and retail price-compare. Feature engineering over live listings, normalisation, benchmarking, and served insight for pricing teams.
Real estateRetailLive listings
Data & AI · Applied & Industry AI
AI where the
P&L moves.
Industry-tuned AI products — pricing intelligence (EstateEdge, retail price-compare) and logistics AI for routing, fleet and driver performance.
Product live
What we do
Applied AI is where Phoenix ships industry-tuned models against real revenue and cost levers. Two shipped families: pricing intelligence (real estate and retail) and logistics AI (routing, fleet economics, driver performance).
EstateEdge is the live example — an AWS-native product that reads the market, benchmarks pricing, and gives developers and brokers a decision surface backed by real data. Same engine, different verticals for the pipeline.
Inside the practice
What's in scope.
Logistics AI
Routing optimisation, fleet-economics modelling, and driver-performance scoring. Same technology base — customer-specific tuning and integration.
RoutingFleetDriver perf
Forecasting & anomaly
Demand forecasting, revenue leakage detection, and anomaly monitoring on SAP transactional data — anchored to a business KPI, not just a chart.
ForecastAnomalySAP data
Product tuning per customer
Applied-AI products are deployed as tuned instances per customer — same core engine, customer-specific data ingestion, KPIs and interfaces.
Per-customer tuningAWS-native
How Phoenix delivers
The approach.
01
Anchor
Pick the KPI the AI has to move — margin, on-time delivery, close rate — not just accuracy.
02
Feed
Data feeds established, historicals loaded, cold-start behaviour defined.
03
Ship
Model in production against real users; decision surface delivered where the work happens.
04
Own
Retraining cadence, drift monitoring and KPI tracking under managed services.
What you get
Named deliverables.
Every engagement lands specific artefacts — not slides.
Model deployed to production against a named KPI
Decision surface (dashboard or in-app) for the operating team
Data pipeline sized and reliable — sub-daily cadence where needed
Retraining schedule, drift monitoring and evaluation harness
SAP integration where the KPI reads from SAP data
Optional: managed-services runbook to operate the model long-term
More data & AI services
The wider practice.
FAQ
Frequently asked
What's actually shipping today under applied AI?
EstateEdge — an AWS-native real-estate pricing product, live and used by developers and brokers across the region. A retail price-compare engine on the same technology base. Logistics AI for routing, fleet economics and driver-performance scoring is in production and on-boarding customers.
How is EstateEdge sold?
Deployed as a tuned instance per customer — same core engine, customer-specific data ingestion and dashboards. Sold as a subscription against a named business KPI (pricing win-rate, listing velocity, margin), not as a licence.
Can you build a bespoke applied-AI product for our vertical?
Yes — that's the pattern. The core engine (data ingestion, feature engineering, model registry, decision surface) is reused, and the vertical is customised on top. Timeline depends on data availability; new-vertical builds typically ship a first production model in 10–14 weeks.
Do you need our SAP data to build applied AI for us?
Only if the KPI reads from it. Pricing intelligence engines run off market data; logistics AI runs off telemetry and route data. Where SAP is the source of truth (forecasting, revenue leakage, procurement anomaly), SAP integration is standard because it's already the day-job.
How is the model managed once it's live?
Under managed services: retraining cadence, drift monitoring, evaluation harness against the anchor KPI, and periodic architecture review. Delivered by the same team that built the model — not a hand-off.
Talk to us
The earliest conversations
are usually the most useful.
Whether you're scoping an SAP move to cloud, restarting a stalled programme, or just trying to figure out where data and AI fit — start with a conversation.