AI Execution Control

AI interprets.
Meliqor resolves.
Systems execute.

Meliqor is the control layer between AI intent and real-world execution. It resolves target and scope, applies policy, and decides what an agent is actually allowed to do—before a system changes.

Apply for a design partnershipSee how it works
Built for teams moving consequential AI agents from pilot to production.
meliqor. wordmark
Refund order #48291
Target verified · within policy
ALLOW
Delete customer workspace
Ambiguous ownership · irreversible
REVIEW
Export restricted account data
Policy violation · scope exceeded
DENY
Pre-executionControl before side effects
DeterministicReplayable decisions
Model-agnosticWorks across agent stacks
Your boundaryCloud, BYOC or on-prem
Why Meliqor exists

We started by trying to invent a language for AI. We found a bigger problem.

At first, the goal was simple: create a language that could express intent to AI more precisely. But as we pushed deeper, one thing became obvious—better interpretation does not automatically create safer execution.

“The breakthrough was realizing that AI does not only need better language. It needs a reliable layer that turns ambiguous intent into controlled action.”

That insight changed the project. The question was no longer only “What did the user mean?” It became “What exactly is this agent about to change, is it within scope, and should the system allow it?” Meliqor grew from that shift—from language and interpretation to resolution, policy and execution control.

1 · LanguageWe explored how AI could understand intent with less ambiguity.
2 · ResolutionWe discovered that target and scope had to be resolved before action.
3 · ControlWe added deterministic rules for consequential decisions.
4 · MeliqorA control layer for AI systems that can act in the real world.
The execution gap

Guardrails protect the model. Meliqor protects the action.

Once AI can call tools, modify records, send messages, approve transactions or trigger workflows, the real risk is no longer only what the model says. It is what the system does next.

01 · RESOLVE

Know exactly what the agent is acting on.

Resolve target, scope and relevant system state before execution—not after the fact.

02 · ENFORCE

Turn policy into execution-time decisions.

Apply invariants and controls at the moment an AI agent attempts a consequential action.

03 · PROVE

Make every decision reproducible and auditable.

Capture the evidence behind every allow, correction, review or denial.

How it works

A control plane for consequential actions.

Meliqor sits in the execution path without requiring you to replace your agent framework, model provider or enterprise systems.

InstructionNatural-language intent
ResolverTarget + scope
GuardPolicy + invariants
DecisionAllow · Correct · Review · Deny
ExecutionApproved system action
Deployment

Run Meliqor where your agents run.

Sensitive execution context does not need to leave your security boundary.

Meliqor Cloud

Fastest path to evaluation, testing and production for lower-sensitivity workloads.

Managed by Meliqor

BYOC

Deploy inside your AWS, Azure or GCP account while retaining control of sensitive execution data.

Recommended for enterprise

On-Prem

Private deployment for regulated, sovereign or highly sensitive environments.

Enterprise & regulated
Start with one workflow

Control the actions where mistakes matter.

Land on one consequential workflow. Validate behavior. Expand once the control layer proves its value.

Customer SupportRefunds, account changes, cancellations and customer-data actions.
Finance OperationsInvoice approvals, payment workflows and financial record changes.
Data & PrivacyDeletion requests, exports, retention and restricted-data handling.
IT & CloudInfrastructure changes, access updates and operational automation.
Outbound CommunicationsRecipient safety, data-class controls and high-impact messaging.
ERP & Back OfficeInventory, master-data, order and workflow corrections.

“The question is no longer whether AI can act. The question is what it should be allowed to execute.”

Meliqor · AI Execution Control Platform
Where Meliqor fits best

Built for teams where AI actions have real consequences.

Meliqor is especially relevant when an agent is moving from suggestion to execution — and a wrong target, scope or policy decision can affect customers, money, data or production systems.

AI-native platforms

Agents embedded in a product

SaaS and AI companies whose agents take actions on behalf of end customers and need enterprise-grade controls.

Mid-market teams

Automation at meaningful volume

Organizations with thousands of monthly agent actions and growing human-review overhead.

Sensitive workflows

Finance, support, IT and data

Workflows involving refunds, account changes, infrastructure, customer data, permissions or regulated processes.

Focused first workflow Typical implementation: a few weeks No multi-quarter transformation program
Business value

More automation. Less review. Fewer costly mistakes.

Meliqor is not only a risk-control layer. It can increase the amount of work AI agents can safely perform without requiring a human to review every action.

01 · Reduce review

Lower manual approval cost

Route only ambiguous or high-risk actions to people instead of reviewing every agent action.

02 · Increase automation

Move more workflows into production

Use execution controls to expand automation where teams currently keep humans in the loop because the downside is too high.

03 · Reduce error cost

Prevent expensive execution mistakes

Catch wrong targets, scope violations and prohibited actions before they create refunds, data incidents or operational rework.

Illustrative ROI

What the business case can look like for a mid-sized AI platform.

Example: 50,000 agent actions per month, 20% currently reviewed by humans, and an average two-minute review time.

Example scenario

Customer Operations AI Platform

600kagent actions / year
120khuman reviews / year
$220kcurrent review cost
~$143kpotential review savings

Illustratively, reducing human review from 20% to 7% can reduce annual review cost from about $220k to about $77k. Add avoided execution mistakes and the economic value can materially exceed the cost of a BYOC deployment.

~$193killustrative annual value
~220%illustrative ROI vs. $60k BYOC
Illustrative example only. Actual results depend on workflow volume, review rate, labor cost, risk profile and deployment scope.
Estimate your workflow

Simple ROI calculator

Potential annual review savings $143,000 Illustrative ROI: 138% before avoided error cost
Pricing

Start with one workflow. Scale when the value is proven.

Pricing is annual and flexible. Final pricing depends on deployment, active agents, execution volume, evidence retention, support requirements and SLA.

Developer

Developer

Free
  • Local development
  • Evaluation & Bench
  • SDK / CLI
Get started
Managed

Cloud

From $20k / year
  • Managed deployment
  • Evaluation + production
  • Included execution volume
Talk to us
Most popular

BYOC

From $60k / year
  • AWS, Azure or GCP
  • Sensitive data stays in your environment
  • Enterprise controls & evidence
Discuss BYOC
Private

Enterprise

From $150k / year
  • On-prem / private deployment
  • Custom SLA & support
  • Regulated environments
Contact sales
Underlying customer cloud and model infrastructure in BYOC or on-prem deployments can be billed directly by the customer's infrastructure providers.
FAQ

What teams usually want to know before they deploy.

1. What is Meliqor?
Meliqor is an AI Execution Control Platform. It sits between agent intent and real-world system changes, resolving target and scope, applying policy and returning ALLOW, CORRECT, REVIEW or DENY before consequential actions are executed.
2. How is Meliqor different from traditional AI guardrails?
Traditional guardrails usually focus on prompts, model inputs or outputs. Meliqor focuses on the execution point: what an agent is about to change in a real system, whether the target and scope are correct, and whether policy permits the action.
3. Does Meliqor replace our AI agent, model or framework?
No. Meliqor is designed to work alongside your existing models, agent frameworks and enterprise systems. It adds an independent control layer in the execution path.
4. What types of actions can Meliqor control?
Examples include API calls, database updates, refunds, account changes, data deletion or export, outbound messages, access changes, ERP updates and infrastructure operations.
5. How does Meliqor decide whether an action should execute?
Meliqor evaluates the resolved target, intended scope, relevant system state and configured policies. It can then allow, correct, route for review or deny the proposed execution.
6. Does sensitive data have to leave our environment?
No. BYOC and on-prem deployments are designed so sensitive execution context can remain inside your own cloud or infrastructure boundary.
7. Which models and agent frameworks does Meliqor support?
Meliqor is designed to be model-agnostic and framework-agnostic. The goal is to provide a consistent execution-control layer across heterogeneous agent stacks.
8. What is Meliqor Evidence?
Evidence captures the decision context behind execution control so teams can audit, replay and understand why an action was allowed, corrected, reviewed or denied.
9. How long does implementation take?
Meliqor is designed to start with one focused workflow rather than a long transformation project. Depending on integration complexity, a first implementation can typically be set up within a few weeks.
10. How does pricing work?
Pricing depends on deployment model, active agents, execution volume, evidence retention, support and SLA requirements. We use annual platform pricing with flexible “from” levels so teams can start with one workflow and expand.
Contact

Bring us one consequential AI workflow.

We can usually determine quickly whether Meliqor is a fit and define a focused path to a first controlled workflow within a few weeks.

Talk to Meliqor

Best fit: AI-native platforms, mid-market automation teams, customer operations, finance workflows, IT automation and sensitive data actions.

Emailcontact@meliqor.com
DeploymentCloud · BYOC · On-Prem
ImplementationFocused first workflow in a few weeks, depending on integration complexity
US design partners

Bring us one consequential AI workflow.

We’ll help you map the execution risk, define control points, benchmark the workflow and determine where Meliqor should allow, correct, escalate or deny actions before they reach production systems.

Request a design partnershipTalk to the product team