Baseline
Measure current volume, time, cost, failure, service, risk and revenue at the workflow level.
P31 · Implementation blueprint · Mappls Address Intelligence
Last-Mile Delivery & Commerce Location Stack
Ambiguous addresses, poor geocoding, inaccurate ETA and inefficient rider routing increase cost and failed deliveries.
01 · Target operating outcome
Define the change before choosing the technology
What does one percentage point of failed or delayed deliveries cost across your current order volume?
Higher first-attempt delivery; lower distance/order and support calls; better ETA promise and courier productivity.
Target
Match rate and first-attempt delivery improve; km/order and ETA error reduce; production unit economics accepted.
02 · Solution architecture
One connected flow from source data to operational action
Address cleansing/geocoding, Mappls Pin, autosuggest, serviceability, dispatch, route optimisation, ETA, driver navigation and proof-of-delivery location. Mappls connects proprietary map and location intelligence with address cleansing/geocoding, mappls pin, autosuggest, serviceability, dispatch, route optimisation, eta, driver navigation and proof-of-delivery location. We start with one measurable workflow, prove the operating impact, and scale through reusable data, APIs and SaaS rather than a one-off implementation.
Data foundation
Map, customer, asset, sensor and enterprise data
Intelligence services
Search, routing, GeoAI, rules and spatial models
Role workflows
Batch validation · Mappls Pin · Serviceability · Exception queue
Decision and outcome
Higher first-attempt delivery; lower distance/order and support calls; better ETA promise and courier productivity.
03 · Users and RBAC
Give every stakeholder the view and actions they actually need
Sponsor / owner
Head Logistics Product
Sponsor / owner
Last Mile
Manager / analyst
Customer Experience
Manager / analyst
COO
Manager / analyst
Chief Product Officer
Operator / specialist
Maps/API Engineering
Operator / specialist
Dispatch
Operator / specialist
Data Science
Operator / specialist
Operations analyst
Operator / specialist
Risk reviewer
The shared Mappls identity is separated from application membership. A user can be an administrator in one app, an analyst in another and a viewer elsewhere.
04 · Data and integration
Connect the minimum information needed to make the workflow real
Location foundation
Map, road, address, place, imagery, boundary and terrain layers appropriate to the decision.
Enterprise context
Customer, order, asset, vehicle, incident, task, sensor or transaction records from systems of record.
Integration pattern
APIs, SDKs, secure batch, streaming events, webhooks, files or on-premise integration depending on architecture.
Security and control
Least-privilege RBAC, audit events, encrypted transport, tenant separation and cloud, private or offline deployment.
Recommended Mappls building blocks
Geocoding & Reverse Geocoding
Address-to-coordinate and coordinate-to-address
Product detailsMappls Pin / Digital Address
Compact digital location identity and address workflows
Product detailsPOI, Address & Building Data
Places, building-level addresses and location identity
Product detailsSearch, Autosuggest & Places
Local search, POIs, autosuggest and place details
Product details05 · Proof of value
Use a bounded implementation to answer the scale decision
Representative pin-code cohort from checkout address through geocode, batching, route and delivery verification.
- Confirm data, geography, users and workflow: Where do failures occur: checkout, geocode, dispatch, navigation or proof? What are distance/order and promised-vs-actual ETA?
- Configure Mappls Address Intelligence for the selected role set
- Run baseline and intervention scenarios with real stakeholder reviews
- Evaluate agreed criteria: Match rate and first-attempt delivery improve; km/order and ETA error reduce; production unit economics accepted.
- Package production scope and commercial model: Per-transaction/API tiers + optimisation SaaS + enterprise support.
06 · Rollout and operating model
Move from proof to governed adoption
Week 0–2
Discover
Baseline, data, stakeholders, security and architecture
Week 3–6
Configure
Data connection, role workflows, rules and dashboards
Week 7–10
Prove
Live users, measurement, change support and decision review
Production
Scale
Phased geographies, integrations, service model and continuous improvement