Analyst Agent

Build, deploy & share specialized AI data analyst agents

Dedicated AI agents that deliver reliable insights while giving data teams complete control over data quality and access. Enable true self-service analytics with specialized, domain-specific agents that work exclusively with your curated data.

Start for free

Get a demo

The old way

One-size-fits-all AI

The new way

Specialized AI data agents

Over-reliance on RAG, prone to missing context or hallucination

One-shot answers that miss data nuances and complexities

Requires a pristine, up-to-date semantic layer across all data

Limited to text-to-SQL, bounding questions to simple data pulls

Focused agents that work within defined dataset boundaries set by the data team

Multi-step problem solving with self-correction and validation

Works with curated datasets in conjunction with your semantic layer

Handles advance use cases with Python and LLMs

Use Cases

Let the AI handle the follow-up requests

Deploy agents in minutes

Cut self-service analytics AI launch time from months to minutes. Deploy specialized agents that instantly enable self-service analytics for specific business domains.

Domain-specific analyst agents

Create dedicated agents for marketing, product, customer success, and more - each working with their own curated datasets for maximum reliability.

Advanced data analysis

Don’t just expect simple data pulls from the AI. Thanks to text-to-Python and LLMs, Analyst Agent can handle complex analyses.

Tight guardrails

Configure the agent to operate within tightly defined data boundaries that you’ve created, providing nearly 100% accuracy within any given report.

Use any data source

Leverage and even merge any number of data sources when building your agents.

Deploy agents in minutes

Cut self-service analytics AI launch time from months to minutes. Deploy specialized agents that instantly enable self-service analytics for specific business domains.

Domain-specific analyst agents

Create dedicated agents for marketing, product, customer success, and more - each working with their own curated datasets for maximum reliability.

Advanced data analysis

Don’t just expect simple data pulls from the AI. Thanks to text-to-Python and LLMs, Analyst Agent can handle complex analyses.

Tight guardrails

Configure the agent to operate within tightly defined data boundaries that you’ve created, providing nearly 100% accuracy within any given report.

Use any data source

Leverage and even merge any number of data sources when building your agents.

Features

Features that deliver true self-service

Dataset-based architecture

Skip the complex semantic layer. Simply point agents at curated datasets for faster deployment and more reliable results.

Real-time data syncs

Keep agents in sync with the latest data changes and user-applied filters in reports.

Multi-step validation

Thanks to custom tools, agents can validate their own work and check for edge cases & data quality issues with transparent access to underlying code before answering.

Python-powered analysis

Complex analysis like regression models and propensity scoring without writing code, using your preferred AI engine (OpenAI, Anthropic, or your private LLM).

Business context awareness

AI that understands your business context and helps users refine their questions to get more meaningful insights.

Universal data connectivity

Connect to any data source and merge multiple sources in memory - from spreadsheets to data warehouses.

Built-in collaboration

Give every user their own AI agent within shared reports, accessing live data without affecting others.

Enterprise-grade infrastructure

Built on scalable, secure architecture with instant agent deployment and SOC2 compliance.

Granular access control

Control exactly what data users can access by deploying AI agents that only work with approved datasets.

Fabi.ai is transforming the way insights are gathered and consumed in the enterprise. Any company not using Fabi.ai is choosing to operate at a disadvantage.

Somrat Niyogi

Founder - Recall Capital

Redefine what's possible with your data.

Start for free

Get a demo

Frequently Asked Questions

Analyst Agent takes a fundamentally different approach by using specialized AI agents instead of a one-size-fits-all solution to handle all analytics requests. Rather than trying to connect AI directly to your data warehouse with a semantic layer, Analyst Agent lets you deploy focused agents that work with curated datasets. This approach ensures nearly 100% accuracy within defined domains, eliminates hallucination risks, and handles messy enterprise data with ease.

Analyst Agent maintains perfect synchronization with your data sources through an advanced infrastructure design. Your agents automatically use the latest data available in reports, whether that's from scheduled refreshes or user-applied filters and updates. Each user gets their own dedicated Python kernel that stays in sync with the source data, ensuring consistent and current analysis without affecting other users.

Unlike general AI that might hallucinate answers when uncertain, Analyst Agent maintains strict boundaries. If a user asks a question that can't be answered with the agent's curated datasets, it will clearly communicate this limitation rather than attempting to provide potentially incorrect information. This transparency helps maintain trust and reliability in the system.

Yes! Complete transparency is a core feature of Analyst Agent. Every analysis that involves code execution comes with full access to the underlying code. This visibility allows technical users to validate the analysis, learn from the AI's approach, and maintain confidence in the results.

Analyst Agent offers flexibility in AI providers to match your organization's needs. You can use leading models from OpenAI or Anthropic, or for enterprise customers, connect to your private LLM deployment. The active AI model is always clearly displayed in the chat interface for reference.

You can deploy new specialized agents in minutes rather than spending weeks building traditional dashboards. Simply connect your data sources, curate your datasets using SQL or Python, and configure your agent's parameters. The platform handles all the infrastructure complexity, allowing you to focus on defining the agent's scope and capabilities.