/01
Financial services
Fraud, disputes & claims
Establish required facts, calculate payment-level outcomes, and route cases for review.
ENTERPRISE USE CASES
What would you automate
if you could trust AI?
Start with the work you don’t yet trust AI to handle. Claims, refunds, eligibility, approvals. Work where the agent must follow your policies, stay within defined limits, and show why it acted.
/02
Retail & ecommerce
Returns, refunds & exchanges
Check purchase history, policy windows, stock, and required approvals as the customer’s request changes.
/03
Travel & insurance
Claims, reimbursements & rebooking
Apply eligibility and coverage rules as circumstances change.
/04
Enterprise software
Verification, credits & entitlements
Verify supplied figures against source records and policy. Surface discrepancies and calculate permitted adjustments.
/05
HR & benefits
Eligibility, leave & approvals
Apply the relevant policy and route decisions to the authorized person.
/06
Operations
Vendor exceptions & reimbursements
Reconcile documents, amounts, and required approvals before acting.
Illustrative application areas. Available actions and autonomy depend on each deployment’s declared scope and integrations.
FIT YOUR EXISTING OPERATION
Own the task.
Or one critical step.
Use the same Apollo-1 runtime in either role, alongside your people, applications, and other agents.
01 / END-TO-END AGENT
From request to resolution.
Handle the conversation, establish facts, and complete permitted actions. Ask for approval or hand off when the declared policy requires it.
RequestApollo-1Resolution
02 / EMBEDDED CAPABILITY
Governed execution where it matters.
Use Apollo-1 for a specific task or decision inside a larger process, such as checking eligibility or calculating an adjustment.
Your workflowApollo-1Your workflow
FIND YOUR FIRST USE CASE
Look for three sources
of complexity.
Mixed outcomes
Different payments, items, or people within one case need different treatment.
Policy exceptions
A request reaches the edge of the written policy and needs an explicit handoff or approval.
Money that reconciles
Amounts, allocations, credits, and reimbursements need to trace back to their inputs.
DEFINE THE AUTONOMY BOUNDARY
Start with a clear scope.
Expand with confidence.
Define what the agent can handle, when it needs approval, and how you’ll measure success.
01
The outcome
What should the agent resolve? What remains with a person?
02
The rules
Which policies, limits, permissions, and approvals apply?
03
The evidence
Where can the agent establish each required fact?
04
The acceptance test
Which ordinary cases and difficult exceptions must pass?
COMMON QUESTIONS
Putting it to work, answered.
What makes a task a good fit for a governed agent?
Three signals: outcomes that differ case by case, requests that reach the edge of written policy and need an approval, and money that has to reconcile back to its inputs. A task with all three is doing work a scripted flow cannot cover and a prompt cannot be trusted with.
Does Apollo-1 replace the whole workflow or part of it?
Either. It can own a task end to end, handling the conversation, establishing facts and completing permitted actions. Or it can sit inside a larger process and handle one step, such as checking eligibility or calculating an adjustment, and hand back.
What happens when a request falls outside policy?
The action is not permitted, and the declared behavior decides what follows: ask for the missing approval, explain the constraint, or hand off to a person. The trace records which condition failed and why, so the outcome can be checked rather than guessed at.
How do we scope a first use case?
Define four things: the outcome the agent should resolve and what stays with a person, the policies and permissions that apply, where each required fact can be established, and the ordinary cases and difficult exceptions that must pass before it goes live.
THE TERM ITSELF
What is neuro-symbolic AI?
THE COMPARISON
How this differs from an LLM agent.
PUT APOLLO-1 TO WORK