Turn your company’s data into practical AI capabilities that support real work. NOVA develops custom LLM and RAG systems that connect AI models with trusted business knowledge so teams can find answers faster, automate routine work, and make better decisions.
Scale Support Without Growing Headcount: Automate common questions and information retrieval so teams spend less time searching for answers and more time solving higher-value problems.
Turn Business Data Into Actionable Insight: Use AI to analyze documents, policies, and operational knowledge so employees can access the right information quickly and make more confident decisions.
Accelerate Workflows Across Teams: Reduce manual research and repetitive tasks across customer support, operations, legal review, and internal knowledge management.
Improve User and Customer Experiences: Deliver faster, more accurate responses across support channels, internal tools, and digital experiences powered by AI grounded in your business data.
A Practical Path to Launch: Move from early use case planning to proof of concept and production planning with a clear scope for the next step.
Give employees and customers reliable answers by retrieving information from documents, knowledge bases, and structured data before generating responses.
Build retrieval systems on Amazon Bedrock Knowledge Bases with S3 and vector search so AI applications can find and use trusted information sources.
Support agents and customers with AI-powered assistance across Amazon Connect environments, including automated responses, knowledge retrieval, and guided support conversations.
Automate specialized tasks such as document review, legal publication analysis, or policy comparison using LLM systems configured around domain knowledge and workflows.
Connect LLM and RAG applications to APIs, business platforms, commerce systems, and internal tools so AI works inside real operational environments.
Build working prototypes that demonstrate how a custom LLM or RAG system retrieves data, generates answers, and performs tasks for real use cases.
Prepare AI solutions for launch with architecture design, implementation planning, delivery scope, and cost estimates that support reliable production deployment.
Move from AI ideas to working systems with structured workshops to plan, test, and prepare solutions for production.
AI Readiness Workshop
In this half-day session, we review your current tools, data sources, and operational goals. The discussion focuses on identifying practical use cases such as knowledge assistants, support automation, or document analysis.
Our team also reviews data availability, content quality, and technical gaps that could affect implementation. By the end of the workshop, your team understands where AI can help most and what steps are needed before development begins.
What’s Included:
Activation Workshop
During this workshop, NOVA works with stakeholders to define a practical LLM or RAG use case and outline the system architecture needed to support it. Our team plans how data retrieval, prompts, and model responses should work together.
Participants also design the environment required to run the solution. The goal is to move from concept to a working proof of concept that shows how the system retrieves information and produces useful responses.
What’s Included:
Production Workshop
This workshop focuses on turning a successful proof of concept into a system your organization can operate and scale. We'll work with your team to define architecture, infrastructure needs, and deployment steps for a custom LLM or RAG solution.
Our discussion covers system reliability, expected workloads, and how the AI service will interact with existing platforms. The outcome is a practical roadmap that shows how to move from testing to a production-ready system.
What’s Included:
Why Nova
From Early Planning to Working Systems: Our team supports the full path from identifying AI use cases to building proof of concepts and preparing solutions for real production environments.
Collaborative Delivery with Your Teams: NOVA works closely with engineering, product, and data teams to design AI systems that align with existing architecture, workflows, and operational constraints.
Our Services
Increase efficiency, scalability, and security with AWS cloud solutions tailored to your business.
Gain a competitive advantage and leverage emerging technology to transform your business.
Our DevOps experts help you stay agile and launch new products, optimizing delivery pipelines.
We monitor, optimize, and secure your tech stack while keeping costs in check and performance high.
Streamline your operations and drive innovation with new integrations and software capabilities.
Frequently Asked Questions
Get quick answers to common questions about Custom LLM and RAG Development and AI systems built around your business data. Whether you're exploring AI for the first time or planning your next implementation, we’re here to help.
Many companies start with general AI tools, but those tools typically have limited access to internal knowledge or specialized workflows. Custom LLM Development becomes useful when teams need systems built around their own knowledge sources, internal processes, and decision logic.
Instead of generic responses, large language models can be tailored to support real operations like internal search, knowledge management, or automated task support inside business applications.
A Retrieval-Augmented Generation system works best when it connects to reliable internal information. These may include documents, product manuals, policies, support articles, CRM records, or structured databases.
A RAG pipeline retrieves the most relevant information before generating a response. This allows AI systems to answer questions using verified business content rather than relying only on general training data.
Employees usually spend time searching through multiple tools to find answers. But an AI agent can retrieve information from company data and present it in a single response.
Techniques like prompt augmentation and prompt engineering guide the model so answers stay focused on the task. This reduces time spent searching for information and helps teams complete daily tasks faster.
Yes. A single AI system can support different teams by connecting to separate data environments and business processes. For example, a system built on foundation models can power internal search for operations teams while also supporting customer-facing experiences.
Many organizations also use these systems for knowledge management. That allows employees across departments to access the same reliable information through one interface.
Organizations typically begin with focused use cases where AI can show value quickly. These may include internal knowledge assistants, document analysis tools, or automation inside support platforms.
At NOVA, we also help teams test GenAI solutions that assist employees in daily tasks. Our AI specialists work with stakeholders to identify use cases where AI can immediately improve productivity or access to information.
NOVA begins by understanding how teams currently work with data and where delays or manual research happen. From there, our team evaluates where an LLM agent or other AI capability could improve daily operations.
We also review available data and system connections. This early planning stage helps organizations avoid building tools that look impressive but do not actually solve real operational problems.
Yes. Many organizations integrate AI directly into existing software environments. For example, RAG-powered assistants can connect with CRM systems, support platforms, internal portals, and APIs.
These assistants use retrieval queries, embeddings, and vector search to pull information from multiple sources. That way, employees can ask questions or complete tasks without changing their toolkit.
After a successful prototype, the focus shifts to making the system reliable and scalable. This usually includes preparing the data pipeline setup, expanding integrations, and testing the AI system under real workloads.
Some organizations may also extend AI capabilities into areas such as customer service workflows or internal decision support systems. NOVA helps teams define the architecture and steps needed to move toward a production-ready deployment.
The timeline depends on the complexity of the use case and the condition of the available data. Simpe prototypes may be built in a short period, while production systems take more planning and testing.
For example, preparing datasets may require techniques like document chunking or content segmentation to organize information properly. NOVA works with each client to define realistic milestones and delivery phases.
AI tools work best when they fit naturally into daily work. Systems designed for a support team or operations staff should operate inside the tools those teams already use. Many organizations also prioritize transparency and reliability so employees trust the system.
NOVA can design confident AI experiences supported by LLM strategy & advisory to ensure the solution is practical and easy for teams to use.
