Agentic RAG. Delivered by Your On-Demand AI Team.
Skip the hiring marathon. Superteams.ai plugs in fractional AI experts who design, build, and deploy agentic Retrieval-Augmented Generation systems—fast. From data prep to evaluation, we get you production-ready without the headcount overhead.
On-Demand AI Developers, to Build Advanced RAG Applications
Our experts deploy advanced RAG workflows powered by top-tier AI models, including Llama4, gpt-oss, Claude Opus 4, Gemini 2.5 or GPT-5, to build agentic RAG using your data.
Ensemble of AI Models for Optimized AI Workflows
By leveraging a mix of small and large LLMs, we create custom AI workflows that are both efficient and highly adaptable. This ensemble approach ensures that responses are balanced between speed, accuracy, and cost, allowing us to deliver solutions tailored to each business use case.
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Optimized Chunking and Ingestion for Unstructured Data
Our chunking strategy ensures optimal data segmentation for highly efficient retrieval and processing. Combined with refined ingestion techniques, we create applications that efficiently handle vast unstructured datasets with unparalleled speed.
Get StartedDiverse Vector Stores for Scalable, Custom Solutions
Whether using Chroma, Qdrant, or pgvector, we select the best vector store to fit your data needs. With built-in flexibility for growth, we help companies deploy scalable AI architectures tailored to their workflows, from retrieval to generation.
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Our Process: From Consultation to Delivery
Your dedicated team starts with a Project Manager, who’ll engage the other talent needed for your success.
Initial Consultation
We assess your business needs and identify areas where advanced RAG applications can drive value, creating a custom roadmap for your AI solution.
Technical Scoping
Our team of AI experts defines the project’s technical requirements, selecting the most effective models, tools, and workflows to meet your objectives.
Data Preparation
We guide data gathering, preparation, and ingestion, ensuring your unstructured data is ready for seamless integration into RAG workflows.
Model Deployment
Using a mix of optimized LLMs and RAG strategies, we deploy the AI solution, tailored for fast, precise, and relevant retrieval.
Iterative Testing
Through rigorous testing and fine-tuning, we ensure your solution performs accurately under various use cases, improving its effectiveness.
Solution Delivery
We provide you with a fully operational RAG solution, along with documentation, training, and ongoing support for smooth operation and scaling.
Frequently Asked Questions
Learn more about our platform and our approach to building fractional AI teams.
What is Agentic RAG (Retrieval-Augmented Generation)?
Agentic RAG (Retrieval-Augmented Generation) is the next evolution of AI systems—combining high-quality retrieval with autonomous reasoning and action-taking. Instead of just fetching relevant information and passing it to a language model, Agentic RAG equips the AI with “agent” capabilities: it can plan multi-step tasks, make decisions based on context, call tools or APIs, and adapt its approach as it works.
This means it’s not only answering questions with richer, more accurate information, but also executing processes end-to-end—making it ideal for complex workflows, research, and business operations that need both depth and adaptability.
What sort of problems can Agentic RAG solve?
Agentic RAG excels in situations where information is scattered, context matters, and decisions require more than a single lookup. It can power domain-specific search and research assistants that not only find the right data but interpret and act on it. It’s effective in customer support — resolving tickets autonomously by pulling from multiple knowledge sources — compliance and policy checks where rules must be applied dynamically, technical troubleshooting that spans different systems, and market or competitive analysis that demands synthesis from varied datasets.
Anywhere a process involves gathering, reasoning over, and then doing something with information, Agentic RAG can shorten cycles, cut manual effort, and raise accuracy.
How can Superteams.ai help build Agentic RAG?
Superteams.ai provides fractional AI teams—engineers, data scientists, and solution architects—who specialise in designing and deploying Agentic RAG systems without the long hiring process.
We handle the full lifecycle: defining use cases, preparing and structuring your data, selecting the right models and vector databases, building retrieval and reasoning pipelines, integrating with your tools and APIs, and running rigorous evaluation. Because our teams work across multiple industries and tech stacks, we bring proven patterns and ready-to-use components that speed up delivery while avoiding costly missteps. The result is a production-ready Agentic RAG solution that’s tailored to your business, deployed in weeks—not months.
Can Agentic RAG systems be deployed on my cloud and ensure full privacy?
Yes. We can deploy fully inside your cloud account—AWS, Azure, GCP, or on-prem—so your data never leaves your control. All components, including vector databases and AI models, run in your environment with encryption in transit and at rest. We follow strict access controls, keep private endpoints, and disable model training on your data by default. If you have compliance requirements like GDPR or HIPAA, we align the deployment to meet those standards.
What data types can you handle when building Agentic RAG systems?
We handle a wide range of structured, semi-structured, and unstructured data, with deep expertise in complex business datasets. This includes SQL databases (MySQL, PostgreSQL, MSSQL) for transactional and relational data; time-series data from IoT sensors, financial systems, or monitoring tools; and analytics datasets from BI platforms like Power BI, Tableau, or Looker. We also process documents (PDF, Word, text), spreadsheets, wikis, API responses, multimedia, and domain-specific formats such as legal filings or scientific datasets.
Our pipelines extract, clean, enrich, and index this information—so your Agentic RAG system can seamlessly retrieve, reason over, and act on insights drawn from both operational and analytical sources.