About The Client

The client is a leading global technology distributor and enterprise supply chain solutions provider operating across a complex international footprint. It specializes in bridging digital and technology adoption gaps in fast-growing global markets through large-scale procurement, multi-vendor ecosystem management, and high-volume logistics for leading technology brands.

Challenges

The client faced several systemic barriers that prevented AI from moving beyond isolated experiments to delivering measurable business impact.

  • Fragmented AI Initiatives: Disconnected projects across business units led to redundant models, duplicated efforts, and increasing operational overhead.

  • Scaling AI Beyond Proofs of Concept: Difficulty moving successful AI experiments into production-ready solutions that can scale across the enterprise.

  • Infrastructure and Cost Volatility: Rapid experimentation created unpredictable cloud consumption without a centralized FinOps framework to manage costs and ensure financial predictability.

  • Execution Risk: A shortage of specialized AI/ML architects hindered the development of complex, autonomous Agentic AI systems.

Solutions

Saksoft deployed a Hybrid AI Framework that combines enterprise cloud services with cost-efficient open-source foundations, enabling scalable AI adoption while minimizing vendor dependency.

1. AI Co-Innovation for Enterprise Use Cases

The AI Co-Innovation Lab brings together the client’s business and domain expertise with Saksoft’s AI, data, and digital engineering capabilities to identify high-impact opportunities, co-create AI solutions, and accelerate enterprise-wide adoption.

POD-based multidisciplinary teams focus on high-value enterprise use cases, including:

  • Agentic AI for Supply Chain Orchestration: Developing autonomous agents to streamline multi-step global procurement, complex vendor orchestration, and large-scale channel ecosystem management.
  • Customer 360: Leveraging customer intelligence and predictive insights to enable personalized product recommendations, enhance customer engagement, and identify revenue opportunities.
  • Demand Forecasting: Applying predictive models to improve demand visibility, support inventory planning, and enable more informed supply chain decisions.

This collaborative approach establishes a structured path from identifying business challenges and validating proofs of concept to production deployment and enterprise-wide scaling.

2. Engineering Data Readiness for AI at Scale

To establish a high-velocity foundation for AI, the AI Co-Innovation Lab implemented an Enterprise Data Fabric focused on accelerating data preparation, improving data accessibility, and enabling secure AI adoption.

  • Automated Data Engineering: Streamlined data ingestion and profiling, reducing the time required to prepare high-quality datasets by 60%.
  • Secure Synthetic Data: Generated high-fidelity synthetic datasets to accelerate model development and testing without exposing sensitive production data, supporting global compliance requirements.
  • Contextual Knowledge Bases: Built vector-based repositories to power Retrieval-Augmented Generation (RAG), providing AI models with secure, real-time access to enterprise-wide data.

3. Accelerating AI Engineering Through Reusable Accelerators

The AI Co-Innovation Lab leveraged reusable engineering capabilities to accelerate development, streamline AI solution delivery, and reduce repetitive development efforts.

  • Agentic SDLC Orchestrator: Integrated repository-aware intelligence to provide inline code suggestions and automated documentation, driving 30–50% faster development.

4. Engineering Rigor and IP Sovereignty

The AI Co-Innovation Lab incorporated architectural flexibility and engineering best practices to support long-term scalability, security, and control.

  • 100% IP Ownership: The client retains full ownership of custom-developed code, models, and data artifacts, ensuring long-term independence and control.
  • Vendor-Agnostic Architecture: Minimized vendor lock-in by enabling flexibility across LLM providers and cloud platforms as enterprise requirements evolve.

5. Building Sustainable AI Capabilities Through a BOT Model

Saksoft follows a Build-Operate-Transfer (BOT) model, combining expert-led implementation with structured knowledge transfer and team upskilling to help the client establish a sustainable, self-sufficient AI capability.

Outcomes

  • 50% Faster AI Innovation: Accelerated AI solution development and validation through collaborative, POD-based engineering.

  • 60% Reduction in Data Preparation Time: Streamlined the data-to-AI readiness cycle through automated data engineering.

  • 80%+ Reduction in Manual Compliance Effort: Reduced turnaround times for complex operational, regulatory, and ecosystem compliance questionnaires from days to hours.

  • Improved Financial Predictability: Implemented Cloud FinOps practices to reduce unnecessary compute spend and provide transparent, scalable operational costs.

Related Case Study