Find what native AI support agents will break before launch
PrivateFlow stress-tests Zendesk, Salesforce, HubSpot, and internal AI actions against risky tickets and policies, then gates unsafe writes before production rollout.
Designed for regulated teams in banking, insurance, and support. Architected for failure discovery, data residency, human review, and audit transparency.
Product headquartered in Zurich; data residency depends on deployment mode.
Built to stress-test native AI. Pauses before risky actions.
Where PrivateFlow earns its weight
Four things native AI agents must prove before production.
Same support volume, four readiness checks: control risky actions, keep quality stable, stay portable across systems, and keep spend predictable.
Sensitive replies go out without anyone reviewing them.
Every reply runs through 26 policy checks, and a human is asked to approve whenever the AI isn't confident enough.
Show proof points
- Personal data, off-policy, and unsafe-content checks on every answer.
- Audit log that flags edits, so you can prove what was sent and why.
- A human review is triggered automatically on low-confidence answers.
Every reply runs through 26 policy checks, and a human is asked to approve whenever the AI isn't confident enough.
Today:Sensitive replies go out without anyone reviewing them.
Show proof points
- Personal data, off-policy, and unsafe-content checks on every answer.
- Audit log that flags edits, so you can prove what was sent and why.
- A human review is triggered automatically on low-confidence answers.
Prompts drift, nobody notices until customers complain.
New versions are tested on a small slice of traffic first, and the system rolls back automatically if quality drops.
Show proof points
- Each new prompt or model is tried on a small slice of real traffic before it goes live.
- If the new version performs worse, it rolls back on its own - no pager needed.
- Replay any past run to see exactly what the AI saw, said, and decided.
New versions are tested on a small slice of traffic first, and the system rolls back automatically if quality drops.
Today:Prompts drift, nobody notices until customers complain.
Show proof points
- Each new prompt or model is tried on a small slice of real traffic before it goes live.
- If the new version performs worse, it rolls back on its own - no pager needed.
- Replay any past run to see exactly what the AI saw, said, and decided.
Locked to one model, one helpdesk, one cloud.
92+ models across 29+ providers, with smart routing and the same governance applied everywhere.
Show proof points
- Run it as managed SaaS, host it yourself, or keep it air-gapped - same setup either way.
- Bring your own provider keys, or swap providers later without rewriting flows.
- 166 ready-made templates and 11+ building blocks to start from.
92+ models across 29+ providers, with smart routing and the same governance applied everywhere.
Today:Locked to one model, one helpdesk, one cloud.
Show proof points
- Run it as managed SaaS, host it yourself, or keep it air-gapped - same setup either way.
- Bring your own provider keys, or swap providers later without rewriting flows.
- 166 ready-made templates and 11+ building blocks to start from.
The token bill is a surprise at month end.
A run stops on its own if it goes over your budget, and routing picks the cheapest model that still meets your quality bar.
Show proof points
- Set a budget per flow - the run stops mid-way before it overspends.
- Matching answers are reused from cache, so you don't pay for the same question twice.
- Per-team usage limits keep one team from eating the whole month's budget.
A run stops on its own if it goes over your budget, and routing picks the cheapest model that still meets your quality bar.
Today:The token bill is a surprise at month end.
Show proof points
- Set a budget per flow - the run stops mid-way before it overspends.
- Matching answers are reused from cache, so you don't pay for the same question twice.
- Per-team usage limits keep one team from eating the whole month's budget.
The same controls apply whether you self-host, run in the EU, or stay air-gapped - your data stays in your environment by default.
Native AI agents are easy to launch, but hard to trust at scale.
Zendesk, Salesforce, and HubSpot can move support work fast. The missing layer is independent testing, approval ownership, and audit evidence before agents reply, update CRM, trigger billing, or escalate.
Illustrative scenario drawn from common CX rollouts.
Other protected flows:SLA breaches . Routing to the wrong queue . Fragmented context
26 checkpoints that stop replies before they ship.
Every stop is logged. Auditors, compliance, and your team all see the same record.
| Time | Actor | Action | Verdict |
|---|
[Download sample audit export ->]Illustrative synthetic JSON for one refund run
Illustrative flow. Controls here are designed to support GDPR, Swiss nDSG, and EU AI Act requirements. For full certification status, see footer.
From customer message to approved action
Watch one ticket pass through guardrails, policy, and human review. No cuts.
Run CX AI from ticket to resolution
Deployment choice, approval gates, audit trails, data boundaries. The parts your security team asks about first.
EU-hosted
Managed PrivateFlow in EU data centers. You keep the tenant keys.
- Tenant data stays in the EU region
- Managed upgrades and SLAs
- BYOK models so providers process only what you explicitly route to them