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AI & Agentic Workflows

Where could AI actually pay off for us?

Most AI demos look great and fail quietly in production. This is the other half of the work: evaluating an agent like any other intervention. Picture a support agent that drafts replies to customers. Tune the guardrails below and watch what they do to accuracy, hallucinations, and the ship decision. Runs entirely in your browser on illustrative data.

Set the guardrails

Auto-send only when the agent's confidence is at least50%

Below the bar, the answer is escalated to a human instead of sent automatically. Raise it and the agent sends less, but what it does send is righter.

Verification pass before sending

A second step that checks the draft against the source before it goes out, the single biggest lever on hallucinations. Costs a little more per answer.

What happens to 1,000 incoming questions

0 answered correctly 0 answered wrong 0 sent to a human

Ship decision

Is the agent calibrated? (stated confidence vs. real accuracy)
Raw agent Perfect calibration
Accuracy of auto-sent answers
Share handled automatically
Hallucination rate (auto-sent)
Est. cost / 1k answers
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Illustrative simulation, not a benchmark of any specific model. A real engagement evaluates your agent on your tasks, your data, and your risk tolerance.

Work together: AI & agentic workflows

Build the workflow, then prove it works before it touches customers.

What you get

  • A task-grounded eval harness for your agent, offline and online
  • A calibration and reliability report: where it's overconfident, where it fails
  • Guardrails and a human-in-the-loop policy tuned to your risk tolerance
  • A clear ship / no-ship call, backed by numbers instead of vibes

How it works

  1. Free 30-minute call
  2. Define what "good" means for your agent and assemble a test suite
  3. Evaluate accuracy, grounding, calibration, cost, and latency
  4. Ship with guardrails, or iterate, with the evidence in hand
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