See how Forma Health supervises their AI

Trust your AI tools because you've verified them

Most AI vendors can show you a compelling demo, but those are controlled presentations, optimized to impress you, not to match your needs. Swept monitors how your AI tools and LLMs of choice perform in the real world: with your data, your users, and under real conditions. We measure reliability rather than assuming it. Swept's methodologically sound review of how any AI system will perform on your data, your users, and under real conditions is built on your workflow and baked into the infrastructure.

Reliability and governance are cousins, and both enable safe and smart scaling.

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What We Do

Set-up with a named engineer, based on your workflows

Reliability monitoring is configured against your actual workflows: your claims, your underwriting logic, your customer interactions. Baselines are established from your data before monitoring begins, so what counts as reliable is defined by your standards, not a vendor's marketing sheet. Completely integrated into your governance program.

Customize

Accuracy thresholds, hallucination rate limits, latency baselines, safety flags, etc., are set by you and based on your teams' real workflows.

Supervise & actively monitor

Swept monitors AI performance continuously across every tool in your environment. Drift, degradation, and failure modes are identifiable before they reach customers or regulators. When an agent starts behaving even subtly differently than it did at baseline, you'll know. Alerts arrive with context: what changed, what the baseline was, and what the relevant history looks like.

We also test known failure patterns before launch: adversarial prompts, sensitive attribute flips, privacy leakage, and exploit resistance. Issues are easiest and cheapest to address earlier rather than later.

Track for governance, compliance & audit readiness

Every reliability event, performance flag, and resolution is logged. Reliability data feeds directly into your governance reporting, so when a regulator, auditor, or board member asks how your AI is performing, the answer is current and easy to access.

Remain LLM-agnostic

Swept runs the same evaluation suite across Anthropic, Gemini, Azure, AWS Bedrock, and on-prem open-source models using comparable (and customized to you) data, tasks, and thresholds. Ongoing monitoring covers every model in your environment regardless of vendor.

What You Get

  • Pre-deployment evaluation on your actual data, users, and workflows
  • Continuous performance monitoring across every AI tool in your environment
  • Baseline established from your actual data and workflows
  • Fair model comparisons: same suite, same data, same thresholds across all vendors
  • Drift and degradation detection before it affects customers or compliance
  • Configurable accuracy, hallucination, latency, and safety thresholds
  • Alerts with context when performance deviates from baseline
  • Full audit trail of reliability events, flags, and resolutions
  • Reliability data integrated into governance and compliance reporting
  • Proactive alerts when employee behavior/workflow patterns change to maintain confidence

FAQs

How do you establish a reliability baseline?
We test your AI tools against your actual data and workflows before monitoring begins. The baseline reflects your standards, not a vendor benchmark, and is saved as the reference point for all ongoing monitoring.
What counts as a reliability problem?
You define the thresholds: accuracy rates, hallucination frequency, latency limits, safety flags. Swept monitors against those thresholds and alerts your team when they're approached or breached.
What happens when a reliability issue is flagged?
Alerts route to your team with context: what triggered, what the threshold was, and what the relevant history looks like. Your team decides how to respond while Swept documents the decision.
Can you monitor third-party AI vendors we don't control?
Yes. Swept monitors vendor AI behavior through web access log parsing and vendor AI inventory. When a vendor updates their model or changes behavior in a way that affects your reliability standards, you'll know before it becomes a problem.
How does reliability monitoring connect to governance?
Every performance event and resolution is logged and feeds into your governance reporting. The compliance record exists continuously, not just at audit time.
Can I bring custom metrics?
Yes. You choose tasks, graders, and metrics, including domain-specific ones. We use your own reliability targets for accuracy, safety, latency, and cost.