Capabilities / Build / AI Systems & Intelligent Automation
Build Agentic AI · RAG · Integration with existing systems

AI systems inside your own environment.

We build agentic AI and RAG systems that run inside your AWS account, connect to your existing systems, and keep sensitive data within your boundary.

Designed for secure enterprise automation: governed data, auditable reasoning, controlled tool access, and human approval paths for sensitive decisions.

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When this is needed

Three signals that this is the right next step.

01 / Too complex for rules Workflows need reasoning, data lookup, tool use, and escalation instead of simple if-this-then-that logic.
02 / Prototypes stuck in lab The gap is integration, governance, reliability, data access, and ownership, not another experiment.
03 / Data stays inside The AI system has to run inside your own environment with logging, access control, and review paths.
Deployment topology

Inside your AWS account. Behind your guardrails.

This topology is illustrative. Model choice, data sources, integrations, and guardrails are tailored per use case. The core boundary stays the same: the system runs inside your AWS account.

CUSTOMER'S OWN AWS ACCOUNT · DATA NEVER LEAVES

01 / USERS

Natural workflow entry points

Employees, customers, or operations teams interact through chat, voice, embedded form, or agent assist.

02 / AGENT & RAG

Multi-step AI with evidence

Agentic reasoning, tool use, verified sources, retrieved context, and escalation when confidence is low.

03 / GUARDRAILS

Responsible AI by design

Content filters, audit trails, reviewable logs, and compliance guardrails built into the workflow.

04 / EXISTING SYSTEMS

ERP, HRIS, CRM, core banking

AI reads and writes through the systems already running in production.

Prompts, retrieved content, audit logs, tools, integrations, and orchestration stay within the customer-controlled environment.
UsersEmployees, customers, or operations teams interact through the interface that fits the workflow.
Agent & RAGThe AI system reasons across steps, uses tools, retrieves verified sources, and escalates uncertain cases.
GuardrailsEvery interaction can be logged, reviewed, filtered, and escalated based on policy and risk.
Existing systemsThe AI layer connects to ERP, HRIS, CRM, or core banking through existing interfaces rather than replacing them.
Engineering principles

The AI systems filter. Built for production use.

We design agent behaviour, retrieval, integration, governance, and handover as one system so AI can operate safely inside the client environment.

01 / Contain Run inside your boundary

Agentic AI and Redpumpkin are deployed inside the client AWS account, not as uncontrolled external workflows.

02 / Ground Use verified data sources

RAG grounds responses in governed knowledge with retrieval evidence and reviewable context.

03 / Integrate Automate through existing systems

AI reads and writes through ERP, HRIS, CRM, and core banking interfaces already in production.

04 / Govern Escalate critical decisions

Guardrails, logs, content filters, and human-in-the-loop paths are built into the workflow.

05 / Handover Make it maintainable

Infrastructure as code, agent configuration, runbooks, and knowledge transfer are part of delivery.

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, connects to existing platforms, and remains transferable after handover.

Case Studies

Turning enterprise data into business intelligence at scale.

These case studies highlight how ICS Compute helps organizations modernize their data estates, accelerate insight generation, and build AI-ready platforms powered by trusted data.

↗ DATA & AI PLATFORM
Consumer Enterprise • Media & Publishing

Modernizing Customer Intelligence with Databricks on AWS

ICS Compute designed and implemented a unified Data & AI platform that consolidated fragmented enterprise data into a governed analytics environment. The solution enabled self-service analytics, accelerated insight generation, and established a scalable foundation for machine learning and future AI initiatives.

Databricks AWS Lakehouse Delta Lake Data Governance AI Enablement
↗ Public Case Study
Life Sciences · Pharmaceuticals

PT Genero Pharmaceuticals

ICS Compute manages the cloud infrastructure for Genero Pharmaceuticals, supporting their manufacturing and distribution operations. The engagement includes cloud architecture governance, managed monitoring, incident response, and quarterly cost optimization reviews delivered through regular business reviews.

Amazon EC2 Amazon S3 AWS Systems Manager AWS CloudTrail FinOps
Additional Data & AI transformation engagements available. ICS Compute maintains a portfolio of enterprise Data & AI platform implementations across consumer, financial services, healthcare, manufacturing, and public sector organizations. Additional case studies and technical references are available upon request.
What we do

What an engagement covers.

An AI Systems & Intelligent Automation engagement covers the agent design, knowledge grounding, integrations, guardrails, and handover needed to make AI usable in production.

Agentic AI
Build agents that can reason and use toolsAgents handle multi-step workflows, call approved tools, and escalate low-confidence or critical decisions.
IncludesWorkflow design, tool use, escalation policy, and agent configuration.
RAG systems
Ground answers in verified dataAnswers are generated from governed knowledge sources with retrieved evidence and audit context.
Includesknowledge ingestion, retrieval design, permissions, and evidence logging.
System integration
Connect AI to existing platformsAI works through the ERP, HRIS, CRM, core banking, or operational systems already in production.
IncludesRead/write integrations, API design, workflow triggers, and safety checks.
Responsible AI
Build guardrails into the workflowEvery interaction can be logged, filtered, reviewed, and escalated based on risk and policy.
IncludesAudit trails, content controls, human review, and compliance alignment.
Redpumpkin platform
Deploy agentic AI inside the client AWS accountA configurable agentic AI platform that runs inside your environment and connects to your workflows.
IncludesDeployment, configuration, guardrails, and operational setup.
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. Systems are built to be maintainable by your team.
Related capabilities

Where teams go from here.

Build

Data Platform & Engineering

The data foundation AI systems retrieve from , lakehouse, governance, lineage.

↗
Scale

AI-Driven Business Applications

Production-tested AI applications (HR Agent, Trade Documents, Finance Automation) you can deploy without starting from scratch.

↗
Operate

Security & Compliance

The security and compliance posture AI systems sit inside , including identity, endpoint, and OJK readiness.

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Talk to us

Start with one AI use case. Then build inside your boundary.

If repetitive operations are consuming team capacity, or AI prototypes are not making it into production, start with a structured use-case discovery.

The output is a system specification that runs inside your AWS account, connects to your existing systems, and can be operated after handover.

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AI use-case discovery

What the discovery covers

  • Use case scoring by operational impact and integration feasibility
  • Data and integration mapping across ERP, HRIS, CRM, or core banking
  • Guardrail and compliance scope
  • Deployment topology inside your AWS account
  • Path to first production system with knowledge transfer