Processing...
NIX built a centralized AI experimentation platform for a leading customer service provider, enabling teams to evaluate, deploy, and scale LLM-based and multimodal solutions efficiently.
Social Networks and Communications
Data Science, AI, ML, Generative AI
AWS, LangChain, OpenAI, GPT-4o, PGVector
Our client, a US-based global provider of customer service technology, delivers AI-powered chat solutions for enterprises in banking, telecom, and e-commerce. To continue improving its products, the company needed a way for internal teams to experiment with advanced tools—ranging from large language models (LLMs) to multimodal AI for text, images, and video.
However, experimentation across different teams was scattered, since each group tested tools separately.
This led to redundant effort, a lack of standardization, and difficulty moving promising prototypes into real customer-facing products. Recognizing the need for a unified solution, the client turned to NIX, impressed by our 30+ years in software development and strong expertise in AI.
Our task was to build a scalable platform that would streamline the client’s AI initiatives with greater order, speed, and reliability.
The project’s complexity lay in the diversity of AI use cases the client needed to support. Product teams required infrastructure for everything from FAQ automation and knowledge retrieval to document processing and image/video generation. The main challenge was building a system flexible enough to cover all these needs while remaining scalable, secure, cost-effective, and easy to use.
To address the client’s challenges, NIX developed a centralized AI integration platform that enables teams to evaluate, compare, and deploy new AI features in a secure and scalable way. The solution was designed to simplify experimentation and make the transition from prototype to production seamless.
Previously, the client’s teams had to set up their own environments whenever they wanted to test a new AI model, which was slow and inconsistent. We built a shared interface in which teams can access and compare different LLMs, such as GPT, Claude, and Mistral, through a plug-and-play system. This allows teams to benchmark models side by side and choose the one that performs best for their specific task—whether it’s answering FAQs, generating content, or analyzing text.
Customer support requires accurate answers pulled from large and varied data sources. To solve this, we designed flexible retrieval pipelines that combine vector search (to find semantically similar information) and keyword search (for precision). This hybrid approach allows the AI to provide more reliable results when working with FAQs, product manuals, and enterprise databases. Teams can also query structured data directly, which helps them test use cases like account lookups and policy retrieval.
Many customer interactions rely on information in documents. To support this, we enabled the platform to process a wide range of file formats—including PDFs, Word, Excel, and HTML—with added optical character recognition (OCR) for scanned files. The system can automatically break down documents into manageable sections, extract key metadata, summarize long reports, and recognize important entities such as names or numbers. This means teams can prototype features like document Q&A and contract analysis without building document-processing pipelines from scratch.
Since visual content is also becoming part of customer service, we integrated CLIP—an OpenAI solution for intelligent image retrieval—and connected the platform with video processing tools such as Sora and KlingAI. This opens the door for future products that can explain certain operations with generated video tutorials or help users find information by uploading an image.
One of the client’s main frustrations was that successful experiments often stalled before reaching production. To solve this, we automated deployment using Terraform and integrated it with the AWS infrastructure. Teams can now replicate the experimental setup in production environments with just a few steps, ensuring the transition from prototype to customer-facing product is quick, reliable, and secure.
We added real-time monitoring and debugging using LangFuse, giving teams visibility into AI agents’ decision-making processes. With these insights, they can better understand the system’s behavior, fine-tune workflows, catch errors early, and continuously improve performance.
To make innovation more consistent across the client’s teams, NIX implemented a framework that streamlines the entire AI experimentation cycle and gives them the flexibility to:
Test and benchmark AI models quickly, comparing performance across multiple use cases
Automate document analysis and knowledge retrieval, improving efficiency and precision
Experiment with both text-based and visual AI capabilities, enabling broader exploration of new concepts
Move successful prototypes into production with minimal rework, ensuring faster delivery of proven solutions
The client now benefits from a cohesive AI environment that unifies experimentation, evaluation, and deployment. Teams can work collaboratively, share results, and reuse proven components instead of rebuilding pipelines for each new idea. This shift eliminated duplication, streamlined collaboration, and introduced consistency across all AI initiatives.
Overall, the solution empowered the company to evolve from ad hoc experimentation to continuous innovation. With a shared platform and clear operational flow, the client is now positioned to scale AI development efficiently, deliver new customer-facing features faster, and strengthen its leadership in intelligent customer service technology.
40%
Faster Prototyping Cycles
Teams now test and evaluate AI models in days instead of weeks.
30%
Reduction In Redundant Effort
Centralization eliminates overlapping experiments across departments.
50%
Faster Deployment To Production
Automated infrastructure setup cuts transition times from months to weeks.
25%
Cost Savings
Streamlined workflows and reduced redundant effort lowers overall operational costs across AI teams.
AI-Driven Application for Mental Health Support in the US
Healthcare
AI-powered System: Cybersecurity Report Generation and Risk Mitigation
AI Agent for Enterprise-grade Device Management
Internet Services and Computer Software
Manufacturing
NLP Chatbot for Accelerating Internal Operations
AI-powered Solution for Reviewing and Rating Books
Education
Pharmaceutical Software Modernization and AI Implementation
Discover NIX AI chatbot solutions to boost engagement and efficiency. Custom development from PoC to enterprise. Learn more here!
Discover AI agent development solutions tailored to your needs. Our expertise in AI agent development helps businesses automate tasks and drive innovation.
Partner with NIX for expert machine learning development. Our ML experts leverage the latest tech stack to create ML solutions tailored to your business needs.
Harness the power of generative AI with our end-to-end services that include AI strategy, model training, deployment, and integration.
NIX is a software engineering company in the USA that offers enterprises digital transformation consulting services to embrace the future and growth.
Elevate your development capabilities with our turnkey team while you concentrate on growing your core business.
Get custom-built solutions that transform raw data into actionable business insights, enhance operational efficiency, and automate internal processes
Unleash the potential of generated data by leveraging AI-based solutions—automate workflows, increase productivity, and optimize spending.
Schedule Meeting
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
