Video briefing
Model selection for AI consultancies: measure your client's tasks, not the leaderboard
Public leaderboards cannot choose a model for your client, and a one-shot demo repeats their mistake. We are piloting a service that runs your client's real tasks through candidate models with repeated trials and error bars, then breaks ties on cost. 2:05
Public leaderboards cannot choose a model for your client, and a one-shot demo repeats their mistake. We are piloting a service that runs your client's real tasks through candidate models with repeated trials and error bars, then breaks ties on cost.
Transcript
What the video says
Model selection for AI consultancies: measure your client's tasks, not the leaderboard. From TerraNet Technologies. Quick thing first: if this is useful, like the video and subscribe. Every AI engagement asks which model a workflow should run on. Clients rarely know, and recommendations are often settled by existing software bundles, vendor partnerships, or blog post tier lists. When teams run a pilot, it is usually a few prompts judged by eye. It sounds confident, but it is rarely measured.
Public rankings offer little clarity. Of seventy-seven benchmarks in our catalogue, only two publish run-to-run confidence intervals. On DeepSWE, only three of twenty-seven adjacent rank steps separate at ninety-five percent confidence. Claude Sonnet 5 costs two hundred sixty-three times as much per task as DeepSeek-V4-Flash, yet their intervals overlap completely.
Testing on your client's own work is the right principle, but running a small set of tasks once separates nothing. Evaluating real tasks measures the right problem, but precision requires repeated trials, explicit error bars, and treating differences within the margin of error as true ties rather than ranked steps.
TerraNet is piloting a measurement service. You provide one workflow, twenty to fifty real examples with known outcomes, and three to five candidate model configurations. We run repeated trials per model until intervals settle, evaluate tasks side by side, and break statistical ties using cost per task and measured latency.
The deliverable is a concise decision memo built for your statement of work, explaining what won, what tied, and why. The process focuses on checkable outcomes like classification and routing. Data is kept in secure private storage and deleted automatically thirty days after memo delivery.
If your consultancy selects models for clients and wants to prove performance on real workloads rather than relying on leaderboard noise, submit a workflow to join the pilot.
Produced by TerraNet Technologies from the cited evidence behind the written article. Facts can change after the recorded date.