Building Enterprise-Grade AI Capabilities at FourKites with ADaSci
As organizations move beyond AI experimentation, the focus is rapidly shifting toward building scalable, production-ready systems. For technology-driven companies, especially in domains like supply chain visibility and real-time analytics, AI is becoming a core engineering capability rather than an auxiliary tool.
The challenge today is not just adopting AI, but enabling technical teams to design, implement, and operationalize complex AI systems that integrate seamlessly with enterprise workflows.
Recognizing this need, FourKites partnered with ADaSci to deliver a comprehensive AI Excellence Program aimed at building end-to-end generative AI capabilities across its technical teams.
The Core Challenge
While awareness of generative AI technologies has increased significantly, the transition from experimentation to production remains a critical bottleneck.
At FourKites, teams comprising data analysts, ML engineers, AI engineers, and architects had strong technical foundations. However, the rapidly evolving GenAI ecosystem, spanning LLMs, RAG pipelines, agentic systems, and LLMOps, introduced new layers of complexity.
Three key challenges emerged -
First, the need to bridge the foundational understanding of large language models with real-world system design and implementation.
Second, limited hands-on exposure to advanced architectures such as multi-agent systems, orchestration frameworks, and retrieval pipelines.
Third, the need for production readiness, including fine-tuning strategies, inference optimization, and deployment architectures required for enterprise-scale systems.
The organization required a structured program that could enable teams to move from isolated experimentation to building integrated, scalable AI solutions.
The Solution
A Comprehensive AI Excellence Program for Technical Teams
To address these challenges, ADaSci designed and delivered a 4-day, instructor-led AI Excellence Program tailored specifically for FourKites.
The objective was clear: enable teams to build, not just experiment with, generative AI.
The program followed an implementation-first approach, guiding participants from foundational concepts to advanced enterprise-grade systems. Each session balanced conceptual depth with extensive hands-on labs, ensuring that participants could design, build, optimize, and deploy AI solutions.
Delivered in an in-person format, the program catered to professionals across experience levels while maintaining a strong focus on real-world application.
Program Design and Learning Modules
The program spanned four days, totalling 28 hours of intensive, instructor-led training.
Day 1: LLM Foundations and RAG Systems
Participants began with a deep dive into large language models, including transformer architectures, prompt engineering, embeddings, and vector databases. The session quickly transitioned into application, with participants building a complete Retrieval-Augmented Generation (RAG) pipeline and exploring advanced retrieval strategies.
Day 2: Agentic AI and Multi-Agent Systems
The second day focused on designing autonomous AI systems using frameworks such as LangGraph, AutoGen, and CrewAI.
Participants explored concepts such as Model Context Protocol (MCP), agent orchestration, memory management, tool integration, and multi-agent coordination. The hands-on component involved building a multi-agent system integrated with external tools.
Day 3: Fine-Tuning, Optimization, and LLMOps
This module introduced decision frameworks for selecting between prompt engineering, RAG, and fine-tuning approaches. Participants worked with QLoRA-based fine-tuning techniques and explored inference optimization strategies to improve system performance and efficiency.
The session also covered LLMOps practices, including monitoring, versioning, evaluation, and deployment architectures—critical for scaling AI systems in production environments.
Day 4: Capstone Project – Design, Build, and Demo
The final day was dedicated to an end-to-end capstone project. Participants worked in teams to design, build, and evaluate a complete AI solution, from problem framing and architecture design to implementation and evaluation.
The program concluded with team demos, expert feedback, and discussions on best practices for deploying AI systems in real-world enterprise scenarios.
Learning Outcomes
The program delivered a significant advancement in technical capability for participating teams.
Participants developed a deep understanding of the generative AI ecosystem, from LLM foundations to advanced system architectures. More importantly, they gained hands-on experience in building RAG pipelines, designing agentic workflows, and implementing multi-agent systems.
The training enabled teams to move beyond isolated experimentation toward designing integrated, scalable, and production-ready AI solutions. Participants also gained practical knowledge of optimization techniques and LLMOps practices, equipping them to deploy and manage AI systems effectively.
Measurable Impact and Engagement
The program’s implementation-first approach ensured strong engagement and practical relevance.
With 28 hours of hands-on training and real-world use cases, participants actively built and iterated on AI systems throughout the program. The capstone project further reinforced learning by simulating real-world engineering challenges.
The combination of structured learning and hands-on execution enabled immediate application of concepts within FourKites’ technical ecosystem.
Strategic Significance for FourKites
This initiative represents a critical step in strengthening AI engineering maturity at FourKites.
By equipping technical teams with end-to-end capabilities, the organization enhanced its ability to design scalable architectures, implement autonomous systems, and optimize AI performance in production environments.
The program also established a strong foundation for scaling advanced AI initiatives, enabling faster innovation and improved operational efficiency across its platform.
Final Takeaway
The future of AI lies in building intelligent systems, not just using tools.
The collaboration between FourKites and ADaSci demonstrates how structured, hands-on programs can transform technical teams into AI system builders.
By combining foundational knowledge, advanced architectures, and production-focused practices, the AI Excellence Program enabled FourKites to accelerate its journey toward scalable, enterprise-grade AI—unlocking innovation and long-term competitive advantage.
ADaSci