Running Google Research as a service, Research Tools

Google Research Agency Implementation, Geospatial AI for Location Services

Learn how to integrate Google Research's open-source models and ME-POIs framework into client projects that require location intelligence. This course teaches agencies how to deliver geospatial AI features like busyness prediction and opening hour detection without building models from scratch, reducing project timelines and increasing margins on location-based services.

Open the decision record for Google Research

What does running Google Research for clients commit you to?

Published figures for this service. Blank fields are not published.

Monthly tool cost
Vendor cost basis is open-source (not published as a paid tier). Your investment is engineering labor across weeks of onboarding. Not modeled, no vendor pricing tiers exist to derive a monetary setup cost.
Time to first value
Weeks (setup_complexity: high; time_to_value: weeks)
Payback
Not modeled
Guided implementation
8 hours

Is Google Research worth running as a client service?

The evidence supports a viable managed service for agencies with in-house data science teams: Google Research's ME-POIs framework produces documented prediction gains when mobility data is added to place text. What remains unknown is client willingness to pay, delivery-hour economics, and any recurring vendor cost, since pricing is open-source and not published. No white-label or reseller program exists, so the Google Research brand must remain visible in delivery.

An agency-fit judgement for reselling this service. It is separate from the tool description on the decision record.

Before you start

What has to be in place before the first client engagement.

Tools and subscriptions

  • Access to Google Research ME-POIs framework and open-source code
  • Gemini API access for text-model integration
  • TrajGPT integration capability for mobility-pattern modeling
  • Client-supplied text metadata of places (POIs, addresses, categories, descriptions)
  • Client-supplied aggregated and anonymized mobility data (arrival times, stay durations, surrounding movement patterns)

People and inputs

  • Data science team capable of implementing self-supervised learning and spatial multiscale visit propagation
  • Engineering capacity for a high-complexity setup requiring weeks of onboarding
  • Geospatial AI domain expertise to interpret place embeddings and prediction outputs
  • Client data-sharing agreements for anonymized mobility data

Included with the course

6 working documents for delivering this service.

  • ME-POIs Integration Checklist for Location Serviceschecklist
  • Geospatial AI Project Scope Templatetemplate
  • Open-Source Model Evaluation Worksheetworksheet
  • Mobility Data Preparation SOPsop
  • Place Embeddings Delivery Guideguide
  • Gemini Integration Setup for Location Predictionssop

Listed by name. These documents are not yet published as individual downloads.