From pilot to production

AI applied to real-world banking.

We turn artificial intelligence initiatives into productive, measurable, and governed use cases on a proven banking platform.

Tell us about your AI plans

The banking moment

Banking doesn't have an AI problem.
It has a methodology problem.

The intent is already there. The difficult leap is between the promising test and the productive use case: prepared data, clear hypotheses, measurable KPIs, internal ownership, governance, and real adoption.

Pilots without hypotheses

Tests that work technically but are launched without a primary KPI, baseline, or decision on continuity.

Unprepared data

Information that is fragmented, inconsistent, or difficult to govern when the use case needs to operate in real-world processes.

Delayed governance

Supervision, auditing, ownership, and kill switches are often added after the pilot, when they should be designed from the start.

The hero remains human.

AI amplifies team capabilities; context, judgment, and accountability remain human.

Production architecture

Three layers
working as one.

Each layer handles a distinct responsibility: the platform supports the banking contract, the agentic layer specializes the use case, and the bank retains control over every decision.

Banking architecture

Layer 01 · Infocorp

The contract with the regulator lives on the platform.

Processes, data, integrations, profiling, KYC, and compliance form the foundation where AI can operate with context and traceability.

Identity and channels

Governed data

Native audit trail

Specialized agents

Layer 02 · Vertical AI

Specialization turns models into measurable results.

Models, prompts, and tools are fine-tuned for a specific banking domain. This is the layer where general capability becomes a use case with ROI.

Specialized agents

Banking RAG

Scoped automation

Operational governance

Layer 03 · Bank

Responsibility remains  within the bank.

The hypothesis, primary KPI, AI Owner, and approval committee define what the agent can do, what is audited, and when it must stop.

AI Owner

KPI and baseline

Safe AI approval

Infocorp Framework

Choose where to invest. Validate how to scale.

The four pillars organize opportunities where returns can be measured. The four cross-cutting axes define the conditions to make them work.

01 / Where to apply AI

Augmented personal productivity

Assistants that reclaim hours of repetitive cognitive work without replacing human decision-making.

GURU

Internal support

Semantic search

Intelligent operational excellence

End-to-end automated critical processes: onboarding, KYC, collections, and case management.

KYC/KYB

Collections

Back office

AI-powered products

Financial experiences with more context, memory, and personalization across digital channels.

Channel AI

AI Marketing

Next best action

Accelerated software engineering

End-to-end automated critical processes: onboarding, KYC, collections, and case management.

SmartOps

Migration

Observability

02 / How to scale

Four conditions before moving to production.

No pillar works without strategy, data, Safe AI, and adoption working together.

Strategy and Governance

Hypothesis, KPIs, baseline, and control gates every 90 days.

Data Platform

Quality, accessibility, governance, and traceability.

Safe AI

Privacy, configurable human oversight, and kill switch.

People and Change

AI Owner, role-based training, and usage-based adoption.

We don't just talk about AI.
We make it work.

Four AI-powered solutions already being used by LATAM banks to improve experiences, operations, and responsiveness.

Conversational Banking

Conversational banking via WhatsApp, Messenger, and X.

IC Insight

Proactive insights and alternative recommendations within web or native applications.

AI Process Automation

Expert-led multi-agent orchestration to automate end-to-end business processes.

IC Smart Support Agent

Expert agent for operations, support documentation, procedure manuals, and observability.

Safe AI in banking

Designed to operate with control.

In financial services, an automated decision without traceability is not a decision: it is a risk. That is why governance is not added at the end; it is designed from the start.

Limited autonomy

The agent operates only within an explicitly defined scope. Outside of that scope, it escalates to a human.

Supervision where it matters

Configurable human-in-the-loop: pre-emptive, sampling-based, or by exception, depending on process risk.

Full traceability

Every decision is recorded in plain language for reconstruction, auditing, and export.

Data isolation

Authenticated access layer, model layer, and audit layer are always kept separate.

Kill switch

Clear mechanism to stop or limit the agent under defined conditions.

Evidence

From real-world cases to replicable capabilities.

Four implementations in Latin American banking show results in operations, channels, and engineering.

Conversational channel

Business onboarding

The agent operates only within an explicitly defined scope. Outside of that scope, it escalates to a human.

KYC/KYB

Compliance

Human-in-the-loop

USD

540K

conversion vs human

Conversational channel

Conversational collections

The agent operates only within an explicitly defined space. Outside of that space, it escalates to a human.

+80%

conversion vs human

Digital service

Conversational service

Transactional chatbot with over 30 banking services resolved within the conversation.

+250K

monthly active users

Technical delivery

Agentic engineering

Prompts, contexts, and human auditing applied to technical migration on IC Banking architecture.

−30%

migration cost

Roadmap

From measured pilot to
governed scale in 12 months.

Three horizons with a clear gate condition. Each gate defines what must be proven before committing to the next investment.

Evidence

A measurable hypothesis.

Choose the first case with an owner, baseline, and continuity criteria before building.

Diagnosis using the Maturity Model.

Highest impact/effort cell selected.

AI Owner, minimum Safe AI, and responsible team.

Deliverable

Prioritized backlog, primary KPI, and baseline.

Gate condition

Written hypothesis, measured baseline, empowered owner, and Safe AI approval.

Produce

A real-world case with users and monitoring.

Transition from pilot to operational process without losing control, traceability, or human judgment.

Execution with primary KPI monitoring.

Review: continue, pivot, or close.

Second initiative identified with documented learning.

Deliverable

Limited production with a reusable playbook.

Advancement condition

KPI moved, real adoption, and lessons converted into method.

Scale

Repeatable capability, not an isolated case.

Convert learning into governance, platform, and reporting for new processes.

Second initiative with a standardized method.

Data platform ready for scale.

Reporting to the board and regulators where applicable.

Deliverable

Operating model for scaling with governance.

Advancement condition

Two production use cases with ROI, sustainable structure, and structured reporting.

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AI applied to real-world banking.

Banking doesn't have an AI problem.
It has a methodology problem.

Three layers
working as one.

Choose where to invest. Validate how to scale.

Four conditions before moving to production.