Case Study
Agentic AI building transformation: From idea to API in minutes
A visual, no-code platform that empowers any employee to build and deploy AI workflows, cutting costs, accelerating experimentation, and democratizing agent development.
1h
Time to first prototype
100
LLM tools
10
LLM providers
Customer
Employees count
8000+
Industry
Digital media, marketing services, and software
Technologies
Dify
Kubernetes
LLMs
Python
INTRODUCTION
Our customer, a large digital platform enterprise, set out to make AI agents and workflow development accessible to everyone. As demand for agents and automation grew across teams, they needed a flexible, cost-effective way to build and deploy workflows.
Limited engineering bandwidth, slow experimentation, and high tool costs slowed progress, so we delivered a secure, internal no-code orchestration platform that enables employees to go from idea to a deployed AI agent in minutes.
OBJECTIVE
The initiative focused on removing technical and financial barriers to AI development across the organization. The goal was clear: Enable any employee to independently build, test, and deploy AI workflows, without writing a single line of code or relying on expensive tools.
This would allow business teams to rapidly prototype ideas, integrate AI into products, and dramatically cut the cost and complexity of traditional development processes.
Challenges
Slow internal innovation cycles
Commercial AI agent tools cost
Non-technical teams lacked autonomy
Disconnected tools and processes
OUR APPROACH
We recommended an open-source-first approach and selected Dify as the orchestration layer for LLM workflows. Using its agentic workflows, RAG pipelines, and built-in API publishing, we delivered a working MVP within weeks.
To maximize the value of our solution, we then worked with the client’s network team to make the service available to all field offices and VPN concentrators, opening up the tool to all client employees worldwide. Then, we enabled connectors for major model providers and local runtimes to unify LLM access behind one platform and empower non-technical teams to build agents safely and in minutes due to easy to use no-code UI workflow builder. The result is standardized LLM access, reduced vendor lock-in and operational overhead, and a clear path to scale with observability and plugin-based extensions.
Benefits
Enabled the platform’s visual builder so teams could rapidly assemble LLMs, tools, APIs, and logic into reproducible workflows.
Leveraged the platform’s built-in API generation to expose agents and workflows without writing backend code.
Enabled authenticated user access across business units within the corporate VPN, aligning login and reachability with internal network controls.
Empowered non-technical teams to rapidly create and run AI agents over internal datasets with no developer involvement.
Enabled access to locally hosted LLMs, ensuring data residency and compliance by keeping model interactions within the corporate network.
Results
AI for Everyone
Non-technical employees can build solutions quickly.
Faster Experimentation
Innovation accelerated across all departments.
Future-Ready Platform
Flexible architecture supports new models with ease.
Conclusion
Each partnership starts with a conversation
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