LEGOAI

LEGOAI | Simplifying AI for Enterprises is a GenAI-powered analytics platform that automatically converts business requirements into executable machine language, delivering explainable and accurate insights at high speed and scale. It blends business and technology through a data-and-analytics ecosystem designed to boost productivity, accelerate use-case implementation, and widen adoption by reducing technical dependencies via automation.

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What LEGOAI Offers

• GenAI-powered analytics studio that translates business needs into actionable analytics with explainable results
• A modern data & analytics ecosystem designed to transform data into insights rapidly
• Self-serve analytics empowering business users with minimal technical friction
• Automation-driven data management and MLOps integration to streamline workflows
• Rapid adoption through natural language interfaces and enterprise-grade governance

How It Works

1. Requirement to Insight Workflow: Business requirements are captured in natural language or structured inputs and converted into executable analytics pipelines by GenAI.
2. Executable Language Runtime: The platform compiles these pipelines into machine language and deploys them across analytics engines with explainability baked in.
3. Iterative Refinement: Users refine outputs through NL interfaces and guided prompts, reducing time to insight from data.

Core Pillars

• Embedded Intelligence: AI capabilities woven into the data & analytics lifecycle
• Modern Data & Analytics Ecosystem: Scalable, flexible, and enterprise-ready architecture
• Data Management with GenAI: Stewardship and governance alongside automated processing

Business Impact Metrics (Illustrative)

• 70% productivity gains by reducing manhours in data analysis
• 90% acceleration in analytics use-case implementation via natural language interfaces
• 5x increased adoption by removing technical barriers through automation

Founding Team (Highlights)

• Prinkan Pal — CEO: AI Engineering and Innovation leadership in analytics and consulting, ex-BRIDGEi2i, Accenture
• Jaskaran Singh — Chief AI Officer: Led AI Centre of Excellence, ~300 data scientists, inventor
• Pradeep Patil — CTO: Chief Architect of Data & AI Solutions for Fortune 500 companies
• Manan Pachnanda — Chief Product Officer: AI-led transformation programs for Global 500, CDO advisor
• Rabindra Neupane — Founding AI Engineer: Built data & MLOps platform for F500
• Juhel Phanju — AI Engineer: Web applications optimized for Data & AI platforms

Pricing Tiers

• Individuals: Free 5 Datasets | 100 MB Size | 100 Queries; Limited email support; OpenAI LLMs integration; Cloud deployment
• Startups & SMBs: Subscription; 30 Datasets | 10 GB Size | 1K Queries; Domain-specific fine-tuned LLMs; Priority support; On-premise & cloud deployment; Open-source LLMs integration
• Enterprises: Custom plans; Unlimited Datasets; use-case specific fine-tuned LLMs; Priority 24/7 support; On-premise & cloud deployment; Open-source LLMs integration

Contact

• Phone: +91-8884321107
• E-mail: [email protected]
• Address: 91 SpringBoard, 512/10, Outer Ring Rd Mahadevapura, Bengaluru, India, 560048

Platforms & Offerings

• OntoLite
• OntoCraft
• OntoSphere
• API Docs, Blogs, Sign Up

Safety & Compliance

• Enterprise-grade governance and compliance baked into the data & analytics pipeline to support auditable, explainable insights.

How It Works (Summary)

• Upload data sources and business requirements
• GenAI analyzes and translates requirements into executable analytics flows
• Automated data processing, model execution, and insight delivery with explainable results
• Self-serve NL interfaces enable non-technical users to access and refine insights

Why Choose LEGOAI

• Accelerates time-to-insight across enterprise analytics use cases
• Reduces reliance on specialized data teams through self-serve capabilities
• Delivers scalable, governance-conscious GenAI-powered analytics

Feature List

• GenAI-powered analytics studio converting business requirements into executable analytics
• Self-serve analytics for non-technical users
• Automated data management and MLOps integration
• Natural language interface for rapid use-case implementation
• Enterprise-grade governance, security, and explainability
• Cloud and on-premise deployment options
• Open-source LLMs integration and flexible deployment
• Multi-tenant architecture suitable for large organizations
• Scalable data processing across datasets and queries