BuildAgentic AI · RAG · Integration with existing systems

AI that lives inside your environment.

We design and build agentic AI and RAG systems that run entirely within your AWS account , with full audit trails, OJK-aligned guardrails, and integration into the ERP, HRIS, CRM, or core banking systems already in production. Sensitive company data does not leave your environment, ever.

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When this is needed

Three signals that this is the right next step.

01 / Too complex for rules There are repetitive operational processes that consume significant time but are too complex for rule-based automation.

02 / Prototypes stuck in lab AI prototypes have been built but never made it to production in a way the business can use.

03 / Data stays inside Sensitive data prevents the use of public AI services, so any AI system has to run inside the organisation’s own environment.

Deployment topology

In your AWS account. Behind your guardrails.

Illustrative deployment topology. Specific model choices, integration points, and guardrail configuration are tailored per use case. The boundary stays the same: every component runs inside your AWS account.

UsersEmployees, customers, or operations teams interact through natural conversation. The interface is shaped to the use case , chat, voice, embedded form, agent assist.

Agent & RAGAgentic AI capable of multi-step reasoning and external tool use. Retrieval-Augmented Generation grounds responses in verified data sources. Human escalation when confidence is low.

GuardrailsContent filtering, full audit trails, and OJK regulatory compliance. Every interaction is logged and reviewable. Critical decisions go through human-in-the-loop.

Existing systemsAI integrates into the ERP, HRIS, CRM, or core banking platforms already in production , not as a replacement layer, but as an automation layer that reads and writes through their existing interfaces.

Engineering principles

The AI systems filter. Built for production, not experiments.

We merge agent design, retrieval, integration, governance, and handover into one production model so AI can operate inside the client environment without creating a parallel shadow stack.

01 / ContainAgents inside your boundary

Agentic AI and Redpumpkin run in the client’s AWS account, avoiding prototypes that bypass governance or send sensitive prompts outside the environment.

02 / GroundRAG over verified data

Answers are grounded in governed knowledge sources with retrieval evidence, avoiding generated-from-scratch responses and black-box decisions with no audit trail.

03 / IntegrateAutomation over replacement

AI reads and writes through existing ERP, HRIS, CRM, and core banking interfaces, avoiding replacement projects disguised as automation.

04 / GovernEscalate critical decisions

Content filters, logs, OJK-aligned controls, and human-in-the-loop paths are built in so low-confidence or critical decisions are reviewed.

05 / HandoverMaintainable by your team

Infrastructure as code, agent configurations, runbooks, and knowledge transfer are part of delivery, avoiding systems only ICS can operate.

Merged operating model

Every AI system has to satisfy the use case and the control model.

Agentic AI, RAG, integration, and responsible AI are designed together. The system uses governed data, stays inside the client environment, integrates with existing platforms, and remains transferable after handover.

Case studies & outcomes

Two AI engagements. Both in production.

01

HR department · more than 5,000 active employees

HR Agent handles 80% of employee questions without manual intervention.

Context

An HR department serving more than 5,000 active employees received a high volume of repetitive questions on leave, payroll, and policy.

Before

HR staff spent the majority of their capacity responding to repetitive enquiries, leaving limited time for strategic workforce planning.

What we delivered

HR Agent , an agentic AI assistant running inside the customer’s AWS account, with retrieval grounded in HR policy, an audit trail per interaction, and escalation to HR staff when needed. See HR Agent.

Outcome

80%Employee questions handled automatically

HR Agent handles 80% of employee questions on leave, payroll, and company policy without manual intervention. The HR team redirected 60% of capacity to strategic workforce planning.

02

Trading company · thousands of cross-border documents per month

Trade document processing reduced from 3 days to 4 hours, errors down 94%.

Context

A trading company processing thousands of cross-border documents per month needed to reduce processing time and error rate without replacing the systems already in use.

Before

Document processing required 3 working days end to end across multiple manual handoffs, with error rates that required downstream rework.

What we delivered

An agentic AI workflow that reads, validates, and routes trade paperwork inside the customer’s environment. See Trade Document Processing, Finance Automation, and also Autonomous Price Sync.

Outcome

−94%Error rate · processing 3 days → 4 hours

Trade document processing reduced from 3 working days to 4 hours, with error rates down 94%.

What we do

What we do.

The services below define the scope of an AI Systems & Intelligent Automation engagement with ICS. Specific tooling and model selection are tailored per use case.

Agentic AI

Autonomous agents with tool use and escalationMulti-step reasoning, human-in-the-loop

What this includesAgents capable of multi-step reasoning and using external tools, with explicit escalation to humans for critical or low-confidence decisions.

RAG systems

Retrieval-Augmented GenerationEnterprise knowledge bases, full data security

What this includesNatural conversation grounded in your verified data, with the audit trail showing what was retrieved before the answer was generated.

System integration

Integration with existing systemsERP · HRIS · CRM · core banking

What this includesAI sits on top of the systems already in production , reading and writing through their existing interfaces.

Responsible AI

Guardrails and auditContent filtering · audit trails · OJK compliance

What this includesEvery interaction logged and reviewable. Compliance with OJK and other applicable regulatory requirements built in, not added later.

Redpumpkin platform

Agentic AI assistant in the customer’s AWSRuns entirely within client environment

What this includesA pre-built agentic platform deployable inside your AWS account, configurable to your workflows, with no data leaving the boundary.

After we hand off

After deployment, you can keep ICS engaged through Managed Cloud & AI Operations for monitoring, model performance review, and incident response , or your internal team can operate the system directly. All systems are built to be maintainable by your team, with structured knowledge transfer as part of the engagement.

Talk to us

Start with an AI use-case discovery. Then build inside your boundary.

If repetitive operations are consuming significant capacity, or AI prototypes have not made it to production, the next step is a structured discovery that surfaces the right use case and the integration shape.

The output is a system specification that runs inside your AWS account. Your team can operate it after handover. The data never leaves your environment.

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AI use-case discovery

What the discovery covers

Use case scoring on operational impact and integration feasibility
Data and integration mapping against your existing ERP, HRIS, CRM, or core banking
Guardrail and compliance scope (including OJK applicability)
Deployment topology inside your AWS account
A path to first production system with a structured knowledge transfer

Agentic

Machine Learning

Finance Automation

Transform

Build

Operate

Scale

AI that lives inside your environment.

Three signals that this is the right next step.

In your AWS account. Behind your guardrails.

The AI systems filter. Built for production, not experiments.

Every AI system has to satisfy the use case and the control model.

Two AI engagements. Both in production.

HR Agent handles 80% of employee questions without manual intervention.

Trade document processing reduced from 3 days to 4 hours, errors down 94%.

What we do.

Start with an AI use-case discovery. Then build inside your boundary.

What the discovery covers

AI that lives inside your environment.

Three signals that this is the right next step.

In your AWS account. Behind your guardrails.

The AI systems filter. Built for production, not experiments.

Every AI system has to satisfy the use case and the control model.

Two AI engagements. Both in production.

HR Agent handles 80% of employee questions without manual intervention.

Trade document processing reduced from 3 days to 4 hours, errors down 94%.

What we do.

Where teams go from here.

Data Platform & Engineering

AI-Driven Business Applications

Security & Compliance

Start with an AI use-case discovery. Then build inside your boundary.

What the discovery covers