01
Customer onboarding with identity verification
End-to-end flow from data capture to customer activation, with identity verification, restricted-list validation, automated risk decisioning and real-time customer communication.
AutomationRules + AIPanama · LATAM
Intelligent automation Process automation with AI.
We automate business processes by combining business rules, API integrations and an AI layer that decides when the rule isn't enough. No fragile bots that break when a screen changes.
- −70%
- Cycle time
- 24/7
- Continuous execution
- 100%
- Audited executions
- API-first
- No screen bots
Flow · Onboarding #4471 Running
Data received
Rule: list OK
Evaluating case
Activation
AI decision
Identity document with borderline image quality. Confidence score 0.71 — below the automatic threshold (0.85). Escalated to human review with full context attached.
Today 1,204
Escalated 38
Avg. time 42 s
The context
The RPA wave already passed; the processes left over are the hard ones
Classic RPA solved
- Copying from one screen to another
- Moving files between folders
- Running scheduled queries
What was left unsolved
- Interpreting the content of a document
- Deciding among edge cases
- Adapting when the screen changed
The first wave of enterprise automation —classic RPA— solved the simple, repetitive processes: copy from one screen to another, move files between folders, run scheduled queries. The processes left to automate are those RPA can't: those requiring interpreting a document, deciding among edge cases, or adapting when the screen changed.
At the same time, many Panamanian companies carry RPA bots that break every time a system is updated, need continuous maintenance and nobody remembers why they were built. The cost of keeping them exceeds the savings they generate.
The next generation of automation combines the best of both worlds: explicit rules for what's deterministic, robust API integrations where they exist, and an AI layer that decides when the rule isn't enough. The result is automated processes that adapt to change and can be explained step by step under audit.
What we offer
Four types of automatable processes
02
Intelligent collections
Portfolio segmentation with probability-of-payment models, optimal channel per customer (SMS, email, call, WhatsApp), contextual message drafting and automatic escalation when the model detects low probability.
03
Accounting closes and reconciliations
Automated data extraction from multiple systems, matching between equivalent transactions, discrepancy detection and adjustment-entry suggestion, with human review on cases the AI flags as uncertain.
04
Incoming correspondence processing
Automated classification of emails, digitized physical mail and tickets. Routing to the correct team, urgency prioritization and extraction of actionable data without manual intervention.
05
Invoice validation and control
Automatic reading of supplier invoices, matching against purchase orders and receipts, discrepancy detection and routing to approval or payment. Shortens the accounts-payable cycle and frees the finance team from low-discretion manual tasks.
Technical reference
AI-driven automation architecture
Our reference stack for AI-driven automation doesn't replace the client's ERP: it orchestrates it. Processes are modeled in a workflow layer where each step can be an explicit rule, a call to an existing system or a decision by an AI agent. Every execution is logged with full context, and the process can be replayed or reverted if something fails. This architecture is especially valuable in regulated environments where traceability of every decision is not optional. On top of that, the process definition itself lives as versioned code, which makes changes auditable and removes the risk of "only one person knows how the bot works".
Event
Triggers the flow
Explicit rule
What is deterministic
AI decision
When the rule isn't enough
Client system
ERP · CRM via API
A business event triggers the flow: a form, an email, a webhook.
Explicit rules resolve everything deterministic that needs no judgment.
An AI agent decides on edge cases, with full context and traceability.
The result is executed against the client's system via API, without touching the screen.
Traceability Every execution is logged with full context, and the process can be replayed or reverted if something fails. The process definition lives as versioned, auditable code.
Why Hypernova
Deployment guarantees
- Active ISO 27001 certification across the entire operation.
- Twelve years operating critical integrations with ERPs and core systems in Panama.
- Automations deployable inside the client's perimeter, without dependency on external SaaS when sensitivity requires it.
- Managed Service model with process success rate and process time monitoring included in the SLA.
Frequently asked questions
What operations teams usually ask
How is this different from RPA?
Classic RPA automates at the screen level and breaks when the interface changes. AI-driven automation operates at the API level when available (much more robust) and uses an AI decision layer for cases rules don't cover. Long-term maintenance is dramatically lower.
Can automated processes be audited?
Yes. Every execution is logged with full context: what data came in, what rules applied, what decisions the AI made (and why) and what actions were executed on client systems. Traceability is a design requirement, not an optional feature.
How are errors or edge cases handled?
The architecture separates "cases the system can handle" from "cases requiring a human". When the AI isn't confident, the process pauses and notifies the operator with enough context to resolve. No blind decisions.
Can it replace an existing RPA bot?
Yes, and we've done it at several clients. The starting point is documenting what the current bot does, evaluating what's better solved with AI versus explicit rules, and migrating in phases so the process doesn't break while replacement happens.
How long does automating a process take?
6 to 12 weeks for a medium-complexity process (for example, onboarding with identity verification). More complex processes with multiple decision branches can take 3 to 5 months.
Ready to automate the processes RPA couldn't?
A 30-minute conversation with our team. Short discovery, no cost, no commitment.