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NIX engineered a scalable AI workflow orchestration solution for a global enterprise automation software provider to seamlessly integrate context-aware AI capabilities into their ecosystem. This enabled large organizations across heavily regulated industries to automate complex, non-deterministic operations.
AWS, Data Science, Generative AI, AI Agent
AWS, Anthropic Claude, Vercel AI SDK
Our client is a US-based global software provider specializing in intelligent process automation solutions for large organizations across multiple industries, including finance, healthcare, telecommunications, and manufacturing. Their platform helps enterprises streamline complex digital operations, improve efficiency, and reduce manual workload at scale.
To evolve the system beyond traditional rule-based workflows, the client wanted to enable customers to choose between deterministic automation for predictable processes and AI-powered capabilities for more context-dependent operations. The goal was to give enterprises greater flexibility without compromising governance and operational control.
NIX has been cooperating with the client for about a decade, contributing to multiple product development initiatives and working closely with its internal engineering teams. Our deep understanding of the product and expertise in cloud-native, AI-enabled systems led the company to entrust us with building a critical component of its next-generation automation platform.
The project focused on developing a core AI-powered workflow orchestration solution within the client’s enterprise automation ecosystem.
Our key objectives:
One of the challenges was embedding inherently non-deterministic AI capabilities into an automation ecosystem that demands absolute predictability, stringent security, and strict regulatory compliance. The team had to find a way to leverage LLMs’ cognitive flexibility while maintaining the ironclad control and data privacy expected by enterprise customers.
Another challenge involved building a scalable foundation that could support multiple AI providers, evolving use cases, and future integrations without adding unnecessary architectural complexity. Furthermore, the team had to keep pace with rapidly emerging AI technologies and engineering practices while maintaining tight delivery timelines.
NIX operated as a highly autonomous extension of the client’s team, proactively proposing technical ideas and co-creating the platform’s core AI capabilities.
To automate routine tasks such as document reading and request classification, our team introduced AI-powered capabilities that enable the platform to process unstructured information and support more intelligent workflows.
Key capabilities included:
As a result, organizations can automate a broader range of context-dependent business processes that previously required manual effort. Depending on the use case, this may include extracting information from invoices, contracts, and other business documents, classifying incoming requests, or summarizing large volumes of content. The platform can also route cases, trigger follow-up actions, and support more intelligent workflow execution based on contextual information.
We developed a unified AI framework that supports multiple leading AI ecosystems, including OpenAI, Azure OpenAI, Google Gemini, Anthropic, and AWS Bedrock. Through a dedicated AI Gateway, the platform can also integrate open-source models such as Qwen, Kimi, Llama, and Gemma. This drives multi-LLM enterprise automation with a highly flexible, budget-optimized approach.
Anthropic Claude models underpin key AI-driven workflow capabilities, while Vercel AI SDK, MCP protocol integrations, and token management tooling provide a consistent approach to introducing AI capabilities across the platform. This foundation provides enterprises with full cost observability and enables them to adopt the technologies that best fit their business requirements. They can switch providers when needed and incorporate future innovations without disrupting existing operations, budgets, or platform stability.
Recognizing that enterprise customers have different requirements for AI adoption, security, and data governance, our team designed the platform to support multiple approaches to model sourcing.
Organizations can connect their own models, leverage models available through AWS Bedrock, or use those hosted directly within the platform provider’s infrastructure. For customers selecting the hosted option, data remains within the provider’s controlled environment and is protected by built-in safeguards, including prompt injection detection, sensitive data handling, and toxicity detection. This helps businesses adopt a comprehensive enterprise AI automation solution while maintaining security and operational control.
Our experts designed and deployed a scalable foundation that delivers the flexibility, performance, and reliability required by large enterprise environments, specifically by:
Because the platform serves large enterprises operating in highly sensitive domains like finance and healthcare, robust governance was a critical requirement throughout the project. Rather than introducing governance as a separate layer, we made it an integral part of the solution itself. Security controls, operational visibility, and oversight mechanisms were embedded directly into workflow execution, ensuring that AI-powered processes remain transparent, manageable, and protected.
This way, organizations can adopt intelligent automation while maintaining compliance requirements, data protection standards, and operational control.
Aimig to optimize and accelerate the development cycle, our team modernized the client’s engineering workflow using AI. We introduced Claude Code, reusable skills tailored to their daily routines, and MCP integrations connecting AI assistants with project infrastructure, knowledge resources, and delivery processes.
We also adopted agentic development approaches for multi-step implementation tasks, code review, and problem-solving. Anthropic’s best practices helped us standardize AI-assisted implementation, testing, documentation, and day-to-day engineering work.
Our collaboration achieved a dual impact, enriching the client’s platform with an AI workflow orchestration solution while fundamentally modernizing their engineering processes. This allowed their customers to automate complex, context-dependent operations that were previously beyond the reach of traditional rule-based tools.
In its upgraded, AI-enhanced form, the system has acquired a secure foundation for intelligent automation, backed by the governance, reliability, and control that enterprises require. It also positions the company to further evolve their AI offerings and accelerate adoption across their customer ecosystem.
Ultimately, these advanced engineering practices and scalable architectural approaches ensure the client is fully equipped to support future technical innovation.
100%
governance and audit coverage across AI-powered workflow execution
6+
AI providers unified through a scalable multi-LLM architecture
70%
faster deployment of new AI use cases
50%
faster engineering through AI-enabled SDLC practices
20+ Experts (Project Manager, Agentic Developer, 2+ QA Analysts, 2+ DevOps Engineers, 2+ Full-stack Developers, 2+ Back-end developers, 2+ UI Developers, 2+ Solution Architects, 2+ UI/UX Designers, Visual/Interaction Designer, Business Analyst, 2+ QA Engineers)
TypeScript, Vercel AI SDK, tiktoken, MCP protocol, ActivePieces framework. Deployed on AWS EKS with Aurora PostgreSQL.
AI development tools (SDLC): Claude Code, MCP-based development workflows, reusable Claude Code skills
AI models (SDLC): Anthropic Claude (Opus 4.5-4.8, Sonnet 4.5-4.6, Haiku 4.5)
AI models/providers (platform): OpenAI, Azure OpenAI, Google Gemini, Anthropic Claude, AWS Bedrock, and open-source models (Qwen, Kimi, LLama, Gemma) via AI Gateway
Cloud / Platform: AWS (primary), Azure
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