Add a safety check before agent output reaches users.
Use Brainiall as a production guardrails layer for AI agents, copilots and autonomous workflows that generate user-facing text.
Understand the first production step in seconds.
A short walkthrough reduces the leap from evaluation to action: start with a real workflow sample, read clear signals, then route content into the queue your team already operates.
See the decision path before creating a key.
Run a safe sample through the Brainiall API surface. The server keeps the demo credential private while you inspect the language, sentiment, safety and PII signals that shape the workflow.
- Check generated text before it becomes a user-facing answer.
- Same public surface leads to quickstart, playground and packages.
/v1/nlp/pii + safety checks// Run the sample to see live routing signals.What usually blocks teams
- Agent output can create brand, privacy or abuse risk in seconds.
- Teams need a simple policy layer without rebuilding the agent stack.
- Enterprise buyers ask for observability, routing and procurement-ready controls.
How to test first value
- Call Brainiall before showing agent output.
- Classify risk, language, PII and escalation signals.
- Block, rewrite, route or log based on your product policy.
From sample to production review without a vendor maze.
Start with public samples, move to authenticated playground calls, then use status, usage and billing screens to decide whether the workflow belongs in Starter, Growth Package or Enterprise Guardrails.
Run the first evaluation before a full rollout.
Start with a small anonymized sample, measure routing quality and latency, then choose self-serve, Growth Package or Enterprise Guardrails based on production volume.