AI-Powered Data Analyst Platform Development
Budget: $15 – $25 USD
Project: Build an AI Data Analyst Platform (Similar to Wobby.ai)
Objective:
We want to build an AI-powered data analyst platform that allows business users to query their data in plain English and get secure, accurate, and actionable insights. Think of it as an AI Data Analyst—similar to Wobby.ai
—that sits on top of our data warehouse and delivers governed insights to business teams.
Key Requirements:
Core AI Engine
Leverage LLMs (e.g., OpenAI, Anthropic, or open-source alternatives).
Build an intelligence layer to handle business logic, context, and query optimization.
Secure Data Connectivity
Connect seamlessly with modern data warehouses (Snowflake, BigQuery, Redshift, Databricks).
Ensure role-based access control and data governance compliance.
Natural Language Querying
Users should be able to ask data questions in plain English.
AI should generate correct, optimized SQL queries and return insights with visualizations.
Collaboration & Integrations
Integrate with Slack, Microsoft Teams, BI tools (Power BI, Tableau, Looker).
Support report generation (charts, PPT-ready slides, shareable docs).
Governance & Security
Queries must respect organizational definitions and metrics.
Provide audit trails of generated queries.
Additional Features (Phase 2)
Deep analysis agent for root cause insights.
Automated reporting and alerting.
Multi-user collaboration with shared insights.
Desired Skills:
Strong expertise in LLM applications and RAG (Retrieval Augmented Generation).
Experience with SQL query generation and optimization.
Backend skills: Python/Node.js, APIs, data pipeline integration.
Frontend: React, Next.js, or similar frameworks.
Familiarity with cloud platforms (AWS, GCP, Azure) and data warehouse integrations.
Security-first mindset for enterprise data platforms.
Deliverables:
Phase 1: MVP with natural language querying, secure data connection, and visualization output.
Phase 2: Collaboration features (Slack/Teams), reporting automation, governance controls.
Objective:
We want to build an AI-powered data analyst platform that allows business users to query their data in plain English and get secure, accurate, and actionable insights. Think of it as an AI Data Analyst—similar to Wobby.ai
—that sits on top of our data warehouse and delivers governed insights to business teams.
Key Requirements:
Core AI Engine
Leverage LLMs (e.g., OpenAI, Anthropic, or open-source alternatives).
Build an intelligence layer to handle business logic, context, and query optimization.
Secure Data Connectivity
Connect seamlessly with modern data warehouses (Snowflake, BigQuery, Redshift, Databricks).
Ensure role-based access control and data governance compliance.
Natural Language Querying
Users should be able to ask data questions in plain English.
AI should generate correct, optimized SQL queries and return insights with visualizations.
Collaboration & Integrations
Integrate with Slack, Microsoft Teams, BI tools (Power BI, Tableau, Looker).
Support report generation (charts, PPT-ready slides, shareable docs).
Governance & Security
Queries must respect organizational definitions and metrics.
Provide audit trails of generated queries.
Additional Features (Phase 2)
Deep analysis agent for root cause insights.
Automated reporting and alerting.
Multi-user collaboration with shared insights.
Desired Skills:
Strong expertise in LLM applications and RAG (Retrieval Augmented Generation).
Experience with SQL query generation and optimization.
Backend skills: Python/Node.js, APIs, data pipeline integration.
Frontend: React, Next.js, or similar frameworks.
Familiarity with cloud platforms (AWS, GCP, Azure) and data warehouse integrations.
Security-first mindset for enterprise data platforms.
Deliverables:
Phase 1: MVP with natural language querying, secure data connection, and visualization output.
Phase 2: Collaboration features (Slack/Teams), reporting automation, governance controls.