Set The Bar For AI, See Who Clears It

Run role-aware tests on your data, define acceptance thresholds, compare options fairly, publish a scorecard leaders can sign.

Trusted by teams building AI help desks, copilots, and agents

Demos Do Not Predict Production

Reviewers need evidence on your tasks and data, not generic benchmarks. Evaluations in Swept AI turn choices into clear decisions: what to ship, what to fix, and what to avoid.

What You Get With Swept AI

Overview

Purpose, scope, models, prompts, datasets, environments, date ranges.

Methods

Tasks, graders, metrics, thresholds, sample sizes, baselines.

Controls

Data handling, privacy, change management, incident response, responsible AI notes.

Ownership

System owners, reviewers, escalation contacts, version history.

Results

Accuracy, hallucination rate, safety flags, bias indicators, latency and cost, pass or fail against thresholds.

How Swept AI Evaluations Work

Connect Data and Pick a Template

Start with a quick sample, then bring your own datasets. Use templates for help desk, copilot, agentic workflows, and question answering.

Define Acceptance Thresholds

Choose metrics and targets per role and task, for example accuracy 92 percent, hallucination rate under 1 percent, average latency under 600 ms.

Run Suites and Review the Scorecard

See side-by-side results by model, prompt, and configuration. Identify wins, regressions, and tradeoffs.

Decide and Save the Baseline

Approve a model and prompt set for handoff. Save the evaluation as a baseline for production supervision.

50+ integrations and counting

FAQs

How much data do I need?
Can I bring custom metrics?
What counts as a pass?
How do I compare multiple agents fairly?