Running Model Meets Reality as a service, AI Evaluation Observability

Model Meets Reality Agency Implementation, Predictive Model Delivery

Learn how to build and monetize predictive model services for clients using Model Meets Reality's versioning and grading system. This course teaches agencies to author sealed claims in MODEL.md files, publish models to a public registry, and grade predictions against real outcomes to establish credible forecasting track records that justify retainer fees.

Open the decision record for Model Meets Reality

What does running Model Meets Reality for clients commit you to?

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

Monthly tool cost
Vendor cost: $0 (open-source). Setup costs: medium complexity, weeks to value; labor for technical staff and domain expert not modeled.
Time to first value
Not published (setup complexity medium; time to value weeks)
Payback
Not modeled
Guided implementation
8 hours

Is Model Meets Reality worth running as a client service?

Model Meets Reality supports externalizing domain expertise as auditable, gradeable models with a public ledger of hits and misses. The evidence shows a structured process for grading mechanisms against real-world outcomes, but the registry is currently empty and nothing has been graded, so ROI depends on future resolutions and client adoption.

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

  • GitHub account and repository for hosting MODEL.md (key integration)
  • Ollama or LM Studio for offline model execution (key integration)
  • Access to an AI assistant capable of pasting a repo link to run models
  • Public registry access for linking the model file
  • Version control client for managing dated claims and deletion clauses

People and inputs

  • Technical staff to manage git repositories and model file formatting
  • Domain expert to author premises and dated claims with frozen criteria
  • Process for sealing claims and tracking resolution dates
  • Template for MODEL.md including premises, dated claims, and deletion clause

Included with the course

7 working documents for delivering this service.

  • Model.md Template for Client Predictionstemplate
  • Sealed Claims Checklist: Pre-Resolution Lockdownchecklist
  • Predictive Model Delivery SOPsop
  • Client Accuracy Scorecard Worksheetworksheet
  • GitHub Repo Setup Guide for Model Ownershipguide
  • Baseline Assumption Documentation Templatetemplate
  • Model Grading and Ledger Review Checklistchecklist

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