From Strategy to Production
A validated roadmap means nothing if the first deployment fails. Our Implementation engagement takes your prioritized use case from pilot planning through AI development to a production system embedded in actual business operations — with guardrails, integration, and measurable outcomes from day one.
15 min
Trade Finance Processing — down from 4 days
6
Industry Verticals with Production Deployments
Weeks
From Pilot Kickoff to Production Launch
AWS AI Competency
part of 60 global launch partners
The Challenge
Why Pilots Stall at the Last Mile
Enterprises that complete strategy often struggle to translate a validated roadmap into a production system that delivers real business value.
Demo ≠ Deployment
A compelling proof of concept running on sample data in a sandbox environment is fundamentally different from a production system connected to live enterprise systems with real compliance requirements. Teams that treat pilot-to-production as a packaging exercise find themselves rebuilding from scratch.
Integration Complexity
Enterprise AI does not operate in isolation. The agent must connect to core operational systems, enterprise applications, document workflows, and governance checkpoints. Each integration point introduces latency, security requirements, and failure modes that a standalone demo never surfaces.
Governance Without Architecture
Compliance teams approve frameworks, not code. When the AI model runs against production data — customer records, financial documents, medical files — every decision path needs audit trails, guardrails, and fallback procedures that governance documents alone cannot enforce.
Where We Fit
Enterprise AI Framework
Implementation is Phase 4 of the framework — the transition from design to deployment where your first AI use case goes live in production.
Our Approach
Three-Phase Implementation Methodology
A structured path from validated strategy to a production system running on real enterprise data with full governance compliance.
Pilot Planning
Define success criteria, scope the pilot environment, map integration points, and establish the governance controls that will carry through to production. Every decision made here prevents a rebuild later.
1–2 weeks
01
AI Development & Integration
Build the agentic AI system against production-grade architecture. Connect to enterprise data sources, configure guardrails, implement human-in-the-loop checkpoints, and test against real operational workflows — not sample data.
4–8 weeks
02
Production Launch
Deploy the validated pilot into production with monitoring, alerting, and performance baselines in place. Train end users, document operational procedures, and measure against the success criteria established in Phase 1.
2–3 weeks
03
What We Build
Implementation Capabilities
Every implementation engagement produces a production-grade system — not a prototype. These are the core building blocks we deliver.
Agentic AI Workflow Design
Multi-agent orchestration with role-specific agents for sales, HR, IT, and operations — each with custom guardrails, connected to company data, and built on branching agentic workflows.
Intelligent Document Processing
Image-to-JSON extraction with OCR, connected to your core enterprise systems with human-in-the-loop verification for insurance claims, trade documents, healthcare records, and distribution invoices.
Enterprise System Integration
API connectors to core banking platforms, ERP, HRIS/ATS, CRM, and monitoring tools. Each integration point includes authentication, error handling, retry logic, and audit logging.
Governance & Guardrails
Custom guardrail configuration aligned with Indonesian regulatory requirements — including OJK directives for financial services, data residency controls, and audit trail generation for every AI decision path.
AI Analytics & Reporting
Conversational analytics that connect to enterprise data and enable performance analysis, revenue forecasting, risk flagging, and action recommendations — without requiring technical effort from end users.
User Training & Adoption
Role-based training programs for end users, power users, and administrators. Includes documentation, runbooks, and change management support to ensure adoption sticks beyond the initial launch.
Deployment Options
Choose Your Deployment Model
Every enterprise has different security, cost, and regulatory requirements. We deploy on the architecture that fits your constraints.
Full Serverless
Token-based pricing on Amazon Bedrock. Best for diverse AI use cases where consumption varies across departments and cost predictability per-call matters.
Amazon Bedrock
Private / Cloud-agnostic
Dedicated, isolated deployment in your private cloud, VPC, Kubernetes cluster (e.g. EKS), or on-prem data center. Infrastructure-based pricing with data residency control. Required for highly regulated industries — banking, financial services — and organizations needing steady, predictable costs.
Private / Any Environment
Hybrid
Combine serverless agent orchestration with in-environment data processing. Keeps sensitive data within your perimeter while leveraging cloud AI model access for inference.
Custom Architecture
Industry Expertise
Implementation Across Six Verticals
Our implementation team has deployed production AI systems across these industries, giving us pre-built connectors, compliance templates, and operational playbooks for each.
Banking & Financial Services
FMCG & Distribution
Healthcare
Logistics & Manufacturing
Human Resources
System Monitoring
How It Works
Your Implementation Engagement in Four Steps
A structured process that moves from kickoff to production with clear milestones, approval gates, and measurable outcomes at every stage.
Step 01
Technical Kickoff
Review the strategy deliverables, confirm the pilot use case, define success metrics, and map the integration architecture with your engineering team.
1–2 days
Step 02
Build & Integrate
Develop the agentic AI system, connect enterprise data sources, configure guardrails, and run iterative testing against real operational data — not synthetic samples.
4–8 weeks
Step 03
UAT & Validation
User acceptance testing with your operations team. Validate outputs against established criteria, measure processing accuracy, and iterate on edge cases before go-live.
1–2 weeks
Step 04
Go-Live & Handoff
Production deployment with monitoring dashboards, performance baselines, user training, and operational runbooks. Includes a stabilization period with our team on standby.
1 week
Our Consulting Services
The Full Engagement Lifecycle
Ready to Build
You have the roadmap. Now deploy your first AI use case into production with a team that has done it across six industries.
