SOLUTION BY INDUSTRY
燃料およびコンビニエンス・リテイリングの成長を促進
ロケーションインテリジェンスの力を活用して、ネットワークを最適化し、顧客を理解し、競合他社に差をつけましょう。
THE PROBLEM AGENTS ARE SOLVING
High-stakes decisions.
Fragmented data.
Gas station and convenience store operators make decisions on new sites, forecourt optimization, product mix, and competitive response using fragmented data and slow research cycles. The operators who win in the next decade will have AI agents that reason over physical world data in real time.
WHAT xMAP PROVIDES
Every layer your agent needs.
Structured, agent-ready data across every dimension that matters — composable, versioned, and built for tool calls.
poi
Every gas station, C-store, QSR, and competitor location — structured with category, brand, hours, and attributes.
car_traffic
Vehicle flow counts on adjacent roads, peak hour patterns, direction of travel — at segment level, updated hourly.
gps_mobility
Where do customers come from? Dwell time, trade area catchment, origin-destination patterns from mobile signals.
parcel_data
Ownership history, lot size, zoning classification, current lease rates — all addressable by parcel ID.
demographics
Household income, vehicle ownership rates, commuter patterns, daytime population at block-group level.
competition
Branded competitor locations, catchment overlap, fuel price indexing — continuously monitored and updated.
AGENT USE CASES
What your agents can do.
01
- Flag parcels on roads with >12,000 AADT
- Model 3-minute drive-time catchment
- Surface sites with no competitor within 800m
- Rank by vehicle ownership and income tier
02
Network Optimization
Continuously monitor your estate against live traffic and mobility data, flagging underperformers and cannibalization risk.
- Detect new competitor openings in trade areas
- Flag volume decline before it hits P&L
- Model remodel ROI by site
- Prioritize estate rationalization decisions
03
Forecourt Intelligence
Cross-reference transaction data with daytime population and commuter flow to optimize pricing, product mix, and staffing.
- Identify peak commuter windows by site
- Optimize in-store product mix by demographic
- Time fuel pricing to traffic patterns
- Staff scheduling anchored to mobility data
HOW THE AGENT WORKS
A site evaluation in 90 minutes.
Watch the agent reason over a candidate parcel step by step, with a full data trace and decision at the end.
AGENT REASONING TRACE · 314 Bleecker St
parcel.query
C2-3 zoning · lot 3,200 sqft · 2 ownership transfers · ~$185/sqft/yr
✓traffic.flow
14,800 AADT · peak 7–9am and 5–7pm · eastbound dominant
✓mobility.catchment
3-min drive area · 28,400 daytime pop · 0.74 vehicle ownership index
✓poi.competition
2 branded competitors within 800m · nearest 620m · different brand tier
⚠demographics.profile
Median HH $94k · age 28–44 dominant · 68% commuter profile
✓agent.decision
PROCEED · confidence 0.81 · recommend lease negotiation
PROCEEDHOW IT INTEGRATES
Works with your stack.
Five integration paths. Same data, same schemas, same response shapes regardless of how you connect.
MCP
MCP Server
Claude and any MCP-compatible agent queries xMap tools natively. Zero integration code. First query in under 60 seconds.
REST
REST + GraphQL
Drop into your existing data pipeline. Same method names, same response shapes across all layers. p95 latency under 200ms.
SQL
SQL / Warehouse
Versioned snapshots in Snowflake, BigQuery, and Databricks. Query directly against your own transaction data.
SDK
Python SDK
Five lines to your first site score. Full async support for batch processing thousands of parcels across your entire estate.
TOP-10 US FUEL RETAIL OPERATOR
14d → 90min screening4,200+ parcels/wk+38% deal pipeline0.81 R²
“xMap is the only data layer we found that actually ships with agent-ready schemas. We had a working prototype in a day and a production system in a week.”
— CTO, Top-10 US Fuel Retail Operator
Ready to build your site selection agent?
First query in under 60 seconds. No integration code required.
連絡を取る
あなたの目標やプロジェクトの規模が何であれ、私たちはそれを処理します。
100% ご満足いただけるよう努めます。
sales@xmap.ai
+1 (415) 800-3938
アメリカ合衆国デラウェア州ウィルミントンノースキングストリート800番地
東京都港区虎ノ門一丁目17番1号
「私たちは、中東のビジネスニーズに合わせた質の高いデータを提供することに重点を置いています。レストラン、ホテル、ジムのいずれであっても、地理データを使用して業務上の意思決定を強化できます。」
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