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.
Our mission
Keep AI agents useful without letting them move unchecked.
turnkeeper.aiturnkeeper.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
Every agent action should have a policy boundary, a review path, and an audit trail.
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
Control has to happen in the narrow window before an agent takes action.
Risky turns need a queue, owner, transcript, and decision.
Human review should be a workflow state the agent understands, not a manual side conversation.
Every exception should become durable operating evidence.
Teams should be able to inspect the proposed action, policy match, reviewer decision, and final outcome.
Why now
The agent rollout is coming faster than the control layer.
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.
Gartner80%common service issues autonomously resolved by 2029
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 makes us different
We are not trying to be the agent, the tracer, or the safety library.
Those categories are useful and will coexist. Turnkeeper is the operational control plane that sits around live agent decisions: it turns policies into workflow gates, human review, API decisions, and audit-ready evidence.
Agent observability
LangSmith, Langfuse
These tools focus on traces, monitoring, evaluations, prompt management, latency, cost, and debugging LLM applications.
Turnkeeper is placed before the action: it decides whether an agent may reply, route, approve, block, or move into review.
Guardrail frameworks
NVIDIA NeMo Guardrails
Guardrail systems intercept model inputs and outputs, apply safety checks, and block, alter, or validate policy-violating content.
Turnkeeper treats guardrails as workflow infrastructure: policies connect to review owners, audit events, decisions, and downstream operations.
Customer AI agents
Intercom Fin
Customer-service agents are built to resolve conversations, automate support, hand off when needed, and report service performance.
Turnkeeper is vendor-neutral control infrastructure for teams building or deploying agents across sensitive customer workflows.
What we build
Control before action, evidence after every decision.
turnkeeper.ai gives teams policy checks, review queues, workflow guardrails, lower model spend, and audit-ready logs for AI agent conversations.