Set The Bar; See Who Clears It
Swept's Evaluation offering ensures that you choose the AI agent(s) that are a fit for your requirements by running role-aware tests on your data, defining acceptance thresholds, and producing a scorecard for consideration.
What We Do
Connect Data and Pick a Template
We start with a sample of your own data for us to use in the evaluation. Templates to choose from include help desk, copilot, agentic workflows, and question answering, or bring your own task definitions.
Define Acceptance Thresholds
We use your thresholds to set targets by role and task: accuracy, hallucination rate, latency, cost. Swept keeps the runs consistent and reproducible so that our comparisons remain meaningful.
Run Suites and Review the Scorecard
Our results are visible side-by-side across models, prompts, and configurations, with wins, regressions, and tradeoffs all visible in one place.
Decide and Save the Baseline
Once you approve a model and prompt set, we save the evaluation as a baseline for production supervision. If you're interested in our Supervision offering, we can connect it to your baseline in one click.
Connect Data and Pick a Template
We start with a sample of your own data for us to use in the evaluation. Templates to choose from include help desk, copilot, agentic workflows, and question answering, or bring your own task definitions.
Define Acceptance Thresholds
We use your thresholds to set targets by role and task: accuracy, hallucination rate, latency, cost. Swept keeps the runs consistent and reproducible so that our comparisons remain meaningful.
Run Suites and Review the Scorecard
Our results are visible side-by-side across models, prompts, and configurations, with wins, regressions, and tradeoffs all visible in one place.
Decide and Save the Baseline
Once you approve a model and prompt set, we save the evaluation as a baseline for production supervision. If you're interested in our Supervision offering, we can connect it to your baseline in one click.
What You Get
The Swept Scorecard
A structured record covering quality, safety, privacy, bias, performance, and cost, complete with a clear pass or fail against your thresholds.
Fair Model Comparisons
We run the same suite across OpenAI, Anthropic, Vertex AI, Azure OpenAI, AWS Bedrock, Mistral, and open-source models. That suite? Same data. Same tasks. Same thresholds. The comparison is only meaningful if the conditions are identical.
Pre-Rollout Issue Detection
We test known failure patterns before launch; examples include adversarial prompts, sensitive attribute flips, privacy leakage, and exploit resistance. It's cheaper to find a problem at this stage than in production.
Data Preparation, Built In
We can prepare to any degree you need, from import files or connect a warehouse to tagging examples by intent and difficulty to using stratified sampling for coverage that holds up to scrutiny. We provide a version history for audits.
Collaboration and Hand off
As part of our Implementation Support, we assign owners to each suite. We export comments on runs, requested changes, and retests to your notebook, repo, or ticketing system. When you're ready, we hand off the approved baseline.