Policies should be evaluated before the answer leaves the system.
Not after a transcript review, not after a complaint, and not only inside a prompt that can drift.
About Turnkeeper
turnkeeper.ai exists because AI agents are moving from suggestion engines into live conversations, support workflows, approvals, and operational decisions. Prompts alone are not a control system.
What we believe
Our mission is to make AI automation safe enough for high-volume customer operations by putting control infrastructure between the model and the moment it replies, routes, approves, blocks, or calls a tool.
That means fewer avoidable model calls, safer escalations before customers see the wrong response, and clear evidence for every decision that mattered.
Operating thesis
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Not after a transcript review, not after a complaint, and not only inside a prompt that can drift.
Human review should be a workflow state the agent understands, not a manual side conversation.
Teams should be able to inspect the proposed action, policy match, reviewer decision, and final outcome.
Why now
The opportunity is not just more automation. It is reliable automation that lowers cost without creating unmanaged risk. Adobe's 2026 report says 78% of organizations expect AI agents to handle customer support interactions within 18 months, while only 16% have deployed them org-wide.
Gartner predicts agentic AI will autonomously resolve 80% of common customer service issues by 2029, with a 30% operating cost reduction.
Grand View Research$182.9Bestimated AI agents market by 2033Grand View estimates the AI agents market grows from $10.9B in 2026 to $182.9B by 2033, with customer service and virtual assistants as the largest application segment.
What we build
turnkeeper.ai gives teams policy checks, review queues, workflow guardrails, lower model spend, and audit-ready logs for AI agent conversations.