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.
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.
The Swept AI Scorecard
Quality: Accuracy and factuality- Safety: Refusal hygiene and jailbreak resistance
- Privacy And Bias: PII checks and sensitive attribute tests
- Performance And Cost: p95 latency and unit cost
- Threshold Result: Pass or fail by task, with owners and next steps
Compare Agents, Models and Prompts on Equal footing
- Run the same suite across OpenAI, Anthropic, Vertex AI, Azure OpenAI, AWS Bedrock, Mistral, and OSS models like Llama.
- Toggle prompts and tools, then view deltas per KPI. You control datasets and tasks, Swept keeps runs consistent and reproducible.
Catch Issues Before Rollout
- Known failure patterns are tested, including adversarial prompts
- Sensitive attribute flips expose bias risk with clear deltas
- Privacy scans detect leakage before customers do
- Exploit sets measure resistance, with pass rates you can share
Data Prep, Done
- Import files or connect a warehouse
- Tag examples by intent and difficulty for balance
- Use stratified sampling for fair coverage
- Track changes with version history for audits
Collaboration and Handoff
- Assign owners for each suite and threshold set.
- Comment on runs, request changes, and re-test quickly.
- Export results to your notebook, repo, or ticketing system.
- Hand off the approved baseline to Supervise with one click.
50+ Integrations and Counting
