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GenAI Accelerator

From Data to Intelligence seamlessly

GenAI Accelerator

From Data to Intelligence seamlessly

No‑Code / Low‑Code • Multi‑Agent • Enterprise

From Data to Deployment , Smarter and Faster

Build, deploy, and scale AI with speed and precision. The Multi-Agent AI Accelerator combines no-code simplicity with enterprise-grade orchestration, seamless integrations, and built-in governance-helping you transform data into reliable, scalable intelligence. With modular agent architectures and advanced orchestration pipelines, you can design workflows tailored to your enterprise needs. It ensures compliance, security, and seamless adoption across every stage of deployment.

Multi-Agent AI Accelerator

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Top Use Cases

RAG for Structured Data

RAG for structured data follows an LLM-to-SQL pipeline where natural language queries are translated into SQL, executed on relational databases, and the results are returned as contextualized answers. This ensures deterministic query execution while preserving the flexibility of conversational interaction. By bridging enterprise databases with generative AI, it enables accurate and explainable responses. Such workflows are particularly valuable in enterprise reporting, compliance checks, and decision automation. The strength lies in delivering structured insights with natural language ease.

Top Use Cases

Graph RAG

Graph RAG leverages graph databases such as Neo4j or Amazon Neptune to perform entity-relationship reasoning. By traversing nodes and edges, it uncovers complex patterns, hidden connections, and contextual links within datasets. This approach goes beyond isolated retrieval to deliver reasoning across relationships. It proves effective for fraud detection, recommendation systems, and building knowledge graphs. The model benefits from representing data in relational structures that mimic real-world connections.

Top Use Cases

Classical RAG

Classical RAG is designed for unstructured data sources like documents, reports, and articles, using embeddings and vector databases to retrieve semantically aligned information. The LLM then augments its output with this retrieved content, improving accuracy and relevance. This makes it a powerful method for search, knowledge management, and conversational AI applications. It excels when organizations need scalable access to unstructured repositories. In essence, it transforms unstructured corpora into actionable, context-aware insights.

Top Use Cases

Multimodal RAG

Multimodal RAG extends retrieval beyond text to include images, diagrams, and other modalities within a unified pipeline. By blending structured and unstructured signals, it creates richer reasoning frameworks for complex decision-making. This approach is valuable in domains like healthcare, where combining medical text with imaging leads to stronger diagnostic outcomes. It also powers e-commerce product search and cross-media Q&A. Ultimately, it delivers more holistic outputs by merging multiple data types into a single reasoning process.

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Everything You Need to Build, Deploy & Scale

Unified tools and frameworks that simplify development, manage context, integrate seamlessly, and ensure enterprise-grade scalability

Visual Builder & Templates

Drag-and-drop editor with a ready-to-use template library for faster prototyping.

Context & Memory Management

Long-term memory with policy guardrails ensures compliance and safe AI behavior.

Seamless Integrations

Plug-and-play with SQL/NoSQL databases, vector stores, graph databases, search engines, and cloud storage.

Enterprise-Grade Governance

Secure with SSO, RBAC, secrets management, and audit trails to meet compliance needs.

Scalable Deployments

Easily adapt workflows from single-agent pilots to enterprise-scale multi-agent ecosystems.

Choose the Right Setup for Your AI Workflows

Compare agent architectures and choose the model that fits your goals – from rapid prototypes to enterprise-scale deployments.

Monolithic Agent

  • Single, unified intelligent agent designed for rapid deployment of focused tasks.
  • Provides unmatched simplicity, speed, and efficiency in complex execution workflows.
  • Best suited for MVPs, POCs, and highly time-sensitive project environments

Duolithic Agent

  • Separation of concerns: a Coordinator Agent manages multiple Specialist Agents
  • Enables safer tool use, modular workflows, and scalable execution across teams
  • Ideal for enterprises balancing complexity with reliability

UBI Xcelerator is an AI-powered automation tool that streamlines data pipelines across diverse enterprise cloud environments. It integrates multiple data sources while delivering real-time, actionable insights for smarter decision-making. The solution enables faster transformation, optimized operations, and full potential from organizational data assets.

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  • Visual Builder & Templates
    Drag-and-drop editor with a ready-to-use template library for faster prototyping.
  • Context & Memory Management
    Long-term memory with policy guardrails ensures compliance and safe AI behavior.
  • Seamless Integrations
    Plug-and-play with SQL/NoSQL databases, vector stores, graph databases, search engines, and cloud storage.
  • Enterprise-Grade Governance
    Secure with SSO, RBAC, secrets management, and audit trails to meet compliance needs.
  • Scalable Deployments
    Easily adapt workflows from single-agent pilots to enterprise-scale multi-agent ecosystems.
  1. Monolithic Agent
    • Single, unified agent designed for fast deployment of focused tasks.
    • Best suited for MVPs, POCs, and time-sensitive projects.
  2. Duolithic (Coordinator + Specialists)
    • Separation of concerns: a Coordinator Agent manages multiple Specialist Agents.
    • Offers safer tool use, modular workflows, and easier scalability across teams.
    • Ideal for enterprises looking to balance complexity with reliability.

Start Building with Confidence

Take the next step – explore the Builder to design, configure, and deploy your agents with seamless control and efficiency.

How RAG Works in GenAI Studio

RAG connects your enterprise knowledge to AI, ensuring every answer is contextual, accurate, and reliable

Multimodal

Ingest images alongside text, cross‑reference with databases, and output actionable flags, summaries, or forms.

Guardrails

Prompt hygiene, PII filters, content moderation, and policy checks reduce hallucinations and leakage.

Observability

Trace prompts, retrievals, tool usage, and latencies. Export logs for compliance and QA.

Structured RAG (SQL)

The system parses the intent, applies schema-aware SQL generation, and executes directly on your warehouse. It then composes a complete answer with accurate citations.

Graph RAG

The system identifies entities and relations, traverses the graph, and retrieves relevant nodes and edges. It generates structured answers using relationship reasoning.

Unstructured RAG

The system chunks and embeds content, performs vector search with top-K retrieval, and runs grounded generation with checks. An optional verification agent finalizes output.

Power Smarter AI Workflows

 Work with us to design intelligent agents, integrate tools and memory, and build scalable ecosystems that drive faster innovation and reliable outcomes.

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About Us

We design enterprise-grade AI, Automation, and Data Ecosystems that enable organizations in Life Sciences, BFSI, Automotive, Retail, Hi-Tech Industries, and beyond to stay secure, future-ready, and powered for faster, smarter decisions.

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