Building trust into every stage of enterprise AI.

Ala

Pre-execution authorization for AI agents that can initiate financial or otherwise consequential actions.

The Problem Ala Solves

AI agents are moving beyond recommendations and beginning to call APIs, use payment rails, initiate transfers, issue refunds and interact with operational systems. Traditional monitoring explains what happened after the event. Basic authorization may confirm that an agent can access a tool, but not whether a specific action is appropriate in the current context. For high-impact actions, that is too late and too limited.

What Ala Does

Ala sits outside the AI agent and evaluates each proposed action against declared intent, live constraints and authoritative rules before execution. It returns one of four deterministic outcomes: ALLOW, BLOCK, LIMIT or ESCALATE. The action proceeds only on the basis of that decision.

The system proposing the move is therefore not the system approving it. This separation creates an independent control point between AI intent and real-world execution.

Decision Outcomes

• ALLOW: the proposed action is within the declared objective and all applicable constraints.
• BLOCK: the action violates a hard rule, uses an unapproved counterparty or falls outside the authorized mandate.
• LIMIT: the action is valid in principle but exceeds a permitted threshold; Ala returns a constrained version that may be executed.
• ESCALATE: information is missing, the intent is unclear or a human decision is required before the action can continue.

How Ala Works

1. 1. Observe

   A lightweight package captures relevant API, model and service interactions to map the action surface and associated risks.

2. 2. Declare intent

   The customer defines the objective and constraints, such as maximum amount, permitted currency, approved counterparties or authorized destinations.

3. 3. Decide before execution

   The proposed action is sent to Ala's decision gateway with information on who is acting, why the action is proposed and what will occur.

4. 4. Apply a deterministic verdict

   Ala returns ALLOW, BLOCK, LIMIT or ESCALATE in milliseconds. The same input produces the same result.

5. 5. Use human approval where needed

   Escalations appear in an operator console where an accountable person can approve, reject or adjust the action and record a reason.

6. 6. Prove the decision afterward

   System and human decisions are recorded, replayable and exportable for audit, investigation or customer assurance.

Core Capabilities

• Pre-execution control rather than after-the-fact monitoring.
• Independent authorization outside the AI agent's own process and trust boundary.
• Deterministic, low-latency decision-making suitable for consequential workflows.
• Rules grounded in declared intent, current state and live constraints rather than probabilistic model judgment.
• Human-in-the-loop escalation for ambiguous, incomplete or high-risk actions.
• Replayable and exportable decision records that show why an action was allowed, stopped, constrained or escalated.
• A phased deployment model: Observe, Shadow, Enforce and Autonomy, without requiring re-integration at each stage.

Typical Use Cases

• An AI agent proposes an approved vendor payment within the authorized limit: ALLOW.
• A payment exceeds the maximum amount: LIMIT and return a capped action.
• A transfer is directed to an unknown or unapproved counterparty: BLOCK.
• A proposed trade falls outside the investment mandate: BLOCK or ESCALATE.
• The action is missing declared intent or requires invoice verification: ESCALATE for human review.
• A finance team needs a complete record of automated and human approvals for audit or customer assurance.

Who Ala Is For

Fintechs, payment and treasury platforms, financial operations teams, marketplaces, banks, regulated service providers and companies building AI agents that can initiate payments, transfers, trades, refunds or other consequential actions.