Build, Deploy, and Operate AI at Enterprise Scale — From Experiment to Production

AI Engineering Platform Solution

Bridge the gap between AI experiments and production-grade deployments. Our AI Engineering Platform provides a unified environment for MLOps, LLMOps, and GPU infrastructure management—enabling your teams to build, train, evaluate, deploy, and monitor AI models with speed, governance, and reliability.

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Challenges
The Problems Most Teams Face Today
AI Models Stuck in Notebooks

Successful prototypes fail to reach production due to the gap between data science notebooks and production-grade engineering, leaving valuable AI investments unrealized.

Fragmented AI Tooling & Workflows

Teams use different tools for ML, LLMs, and infrastructure with no unified platform, creating silos, duplication, and inconsistent practices that slow down every AI initiative.

Uncontrolled AI Costs & GPU Waste

Without centralized management, GPU compute costs spiral out of control, LLM API token usage is untracked, and expensive resources sit idle between training runs.

Lack of Governance & Auditability

Without a systematic process, it is nearly impossible to track model versions, prompt configurations, and data lineage — creating significant compliance and regulatory risks for AI deployments.

Introduction to AI Engineering Platform

The unified platform to build, deploy, and manage all your AI at scale

Our AI Engineering Platform provides the complete infrastructure, automation, and governance required to operationalize every type of AI — from traditional ML models and LLM-powered applications to multi-agent systems. It unifies MLOps, LLMOps, and GPU infrastructure management into a single platform, enabling your teams to move from prototype to production with engineering rigor.

Key Features
Capabilities That Make the Difference
CI/CD for ML & AI Models

Automate the continuous integration, testing, and deployment of your ML models and LLM applications with production-grade pipelines.

  1. Code, data, and model versioning
  2. Automated validation gates for models and prompts
  3. Blue/Green & Canary deployment strategies
Experiment Tracking & Model Registry

Establish a central source of truth for all ML and AI activities, providing a collaborative and auditable environment for your data scientists and AI engineers.

  1. Logging of parameters, metrics, and artifacts
  2. Central, versioned repository for all models
  3. Model promotion workflows (dev → staging → prod)
Feature Store & Data Pipelines

Create a centralized repository for curated, reusable features and build automated data pipelines that feed both training and serving workloads.

  1. Feature discovery, sharing, and reuse
  2. Point-in-time correct feature retrieval
  3. Automated ETL/ELT for model training data
Outcomes
Measurable Results You Can Expect
10x
Faster AI Deployment Frequency
Move AI models and LLM applications from development to production in days instead of months with fully automated CI/CD pipelines.
60%
Reduction in AI Infrastructure Costs
Optimize GPU utilization, LLM token consumption, and compute resources through centralized management and intelligent scheduling.
90%
Reduction in Model-Related Incidents
Proactively detect drift, performance degradation, and quality issues across ML models and LLM applications before they impact business.
100%
Audit-Ready AI Compliance
Generate complete lineage, governance, and compliance reports on demand for all AI assets, satisfying the strictest regulatory requirements.
Use Case
How It Works
A Simple Walk-Through from Start to Finish
  • 1
    Assess & Roadmap

    We evaluate your current AI engineering maturity across MLOps, LLMOps, and infrastructure, then design a phased adoption roadmap tailored to your goals and existing toolchain.

  • 2
    Design the Platform Architecture

    We architect your unified AI platform with standardized pipeline templates, GPU management policies, and governance frameworks that cover both traditional ML and LLM workloads.

  • 3
    Implement & Automate

    Using our accelerators, we implement the core platform components — CI/CD pipelines, model registry, LLM management, GPU orchestration, and monitoring — automating the full AI lifecycle.

  • 4
    Govern & Optimize

    We establish governance frameworks, cost optimization policies, and continuous monitoring, providing insights to optimize your AI models, LLM applications, and infrastructure spend.

Success Story
Deep Dives into Real-World Results
Testimoni
What Our Customer Say
What Sets This Solution Apart
Why Choose Us
Driving Your Success with Expertise and Innovation
Platform Engineering DNA
AI Engineering is a natural extension of our deep expertise in CI/CD, DevOps, and cloud-native platforms. We bring an engineering discipline that pure data science consultancies lack.
Unified ML + LLM Platform
Unlike point solutions that only handle MLOps or LLMOps, our platform unifies both under one roof — a single control plane for managing all your AI workloads at scale.
Holistic, End-to-End Approach
We manage the entire AI ecosystem: data pipelines (DataOps), model pipelines (MLOps), LLM applications (LLMOps), and the underlying GPU infrastructure (CloudOps) — all integrated.
Expertise in Regulated Industries
Our proven track record with top financial and public sector institutions ensures our AI platform is designed for the highest levels of security, governance, and regulatory compliance.

PT Divistant Teknologi Indonesia

Frequently asked questions

Quick Answers to Common Questions

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Start your journey with Us!

Streamlines your software development and deployment process. With IntegraCI, you can easily enabling the automation of CI, CD, GitOps, DevSecOps, Observability, Test, and many more capability. Saving your time and reducing errors with our DevSecOps Platform.

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