Running autodidakt as a service, AI Evaluation Observability

autodidakt Agency Implementation, Selling AI Solver Benchmarking to Research Teams

Learn how to position autodidakt's leaderboard rankings and case-coverage scoring as a research validation service for agencies serving AI labs and scientific computing groups. This course covers packaging benchmark results into client reports, automating recurring evaluations across model versions, and building retainer workflows around solver performance optimization.

Open the decision record for autodidakt

What does running autodidakt for clients commit you to?

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

Monthly tool cost
Not published, autodidakt has no published pricing tiers, so the only modeled cost is internal engineering and compute, which the agency must supply.
Time to first value
Not published
Payback
Not modeled
Guided implementation
8 hours

Is autodidakt worth running as a client service?

autodidakt is a specialized AI-solver research benchmark with public leaderboards, not a commercial agency platform, and it publishes no pricing tiers, integrations, or multi-client features. The evidence supports only a managed evaluation service for scientific computing and AI research teams; client price, labor cost, usage cost, and expected volume are all unknown, so ROI cannot be modeled.

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

  • AI model and harness configurations to evaluate (autodidakt, Codex, Claude Code, best-of-16)
  • Sparse linear system test matrices from FLASH magnetic-diffusion solves
  • SuiteSparse Matrix Collection test cases
  • GMRES(50) + BoomerAMG reference solver build
  • C compiler toolchain for generated solver code

People and inputs

  • Numerical methods engineering time for solver build and validation
  • Compute capacity for repeated benchmark runs across eight matrix sizes
  • Process for versioning model/harness grids per client
  • Analyst time to interpret speedup and case-solve leaderboards

Included with the course

6 working documents for delivering this service.

  • Benchmark Delivery Checklist for AI Research Clientschecklist
  • Speedup Leaderboard Report Templatetemplate
  • Monthly Solver Performance Monitoring SOPsop
  • Case Coverage Analysis Worksheetworksheet
  • AI Model Harness Configuration Comparison Guideguide
  • Research Client Onboarding Checklistchecklist

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