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Get startedAI is already transforming how teams write code, generate legal documents, and summarize complex information. But when it comes to data, adoption has lagged behind. Tools like ChatGPT can interpret static files or answer simple questions, yet they struggle to produce reliable, actionable outputs from raw or even modelled data.
The reason is simple: business data isn’t just rows and columns. It comes with complex relationships, evolving schemas, ownership, governance rules, and metric definitions that differ across every organization. Without this surrounding context, AI outputs risk being wrong, inconsistent, or irrelevant, undermining trust and slowing adoption.
This guide breaks down the top AI tools for data in 2025, why context is the key differentiator, and how your team can move from experimentation to dependable outcomes.
OpenAI’s ChatGPT remains the most widely adopted large language model, with strong reasoning abilities and an intuitive chat-based interface that makes it accessible for both technical and non-technical users. Its plugin ecosystem and Model Context Protocol (MCP) support allow it to connect with external applications like Google Drive, GitHub, and Intercom, extending its reach into business workflows.
Strengths:
Limitations:
Best for: Early exploration, prototyping conversational data assistants, and lightweight querying when context requirements are low.
Anthropic’s Claude has gained traction for its emphasis on safety, alignment, and interpretability. Its large context window supports longer prompts, making it well-suited for document-heavy workflows and extended reasoning tasks.
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Limitations:
Best for: Safe experimentation, document summarization, and unstructured analysis where interpretability and longer context are priorities.
Google’s Gemini is designed as a multimodal model, capable of reasoning across text, code, images, and even video. It integrates tightly with Google Workspace, making it appealing for organizations already embedded in Google’s productivity ecosystem.
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Limitations:
Best for: Organizations in the Google ecosystem looking for AI support across documents, emails, and collaborative workflows.
Developed by xAI and integrated with X (formerly Twitter), Grok is positioned as a conversational assistant with personality and real-time awareness of X’s social graph. While less enterprise-focused than other models, it offers quick responses and unique cultural positioning.
Strengths:
Limitations:
Best for: Informal exploration, real-time conversational insights, or early-stage testing. Not for governed analytics or enterprise-scale data reasoning.
Notion AI extends the popular workspace tool with AI-powered summarization, drafting, and search across documents and wikis. It’s especially effective at finding references inside notes, specs, or onboarding materials.
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Best for: Navigating documentation, policies, and project notes, but less effective for structured data environments.
Glean provides enterprise-wide search across documents, Slack, email, and other collaboration tools. It’s optimized for surfacing references from across large organizations.
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Limitations:
Best for: Enterprise knowledge management. Ideal for surfacing unstructured references across silos, but not for structured data analysis.
An open-weight model from Meta, Llama 3 allows organizations to deploy models internally with more control.
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Best for: Teams with infrastructure expertise who want customizable, self-hosted models.
Mistral offers efficient, lightweight open-source models that balance performance with low compute requirements.
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Best for: Cost-conscious organizations experimenting with open-source AI.
Falcon is a performance-optimized open-source model backed by Abu Dhabi’s TII, available for commercial and research use.
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Best for: Research teams and enterprises looking for cost-effective experimentation with customizable open-source AI.
dbt’s AI features support tasks within its transformation layer, such as generating SQL, documenting models, and creating tests.
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Best for: Streamlining dbt development and documentation.
Snowflake integrates AI and natural language features directly into the warehouse. These include natural language querying, anomaly detection, and semantic search for warehouse-resident data.
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Best for: Querying, exploring, and monitoring data directly within Snowflake.
Modern BI platforms are embedding AI assistants for dashboard exploration and natural language querying.
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Best for: Lightweight, self-service analytics directly within BI tools.
Secoda AI is built on a foundation of business context: lineage, documentation, metadata, and governance. This foundation enables it to deliver accurate, context-aware outputs and minimize many of the risks associated with general-purpose AI systems.
How it works:
Secoda AI is powered by a multi-agent system. Each agent specializes in tasks like lineage parsing, query synthesis, or semantic search, and they collaborate to interpret user questions, retrieve context from across the metadata graph, and resolve ambiguous inputs before generating a response.
When a user asks a question, Secoda AI references lineage paths, ownership metadata, access policies, and historical documentation. For example, if asked about a column, it can show how that column was created, how it’s used downstream, who owns it, and whether its definition aligns with business logic. This helps to prevent hallucinated SQL and irrelevant results.
Key strengths:
Best for: Organizations that prioritize governance, accuracy, and trust in AI outputs. Rather than replacing data teams, Secoda AI enhances their workflows with context-driven reasoning, structured problem solving, and collaborative context sharing.
AI is quickly becoming an essential part of the modern data stack, but not all tools are built to deliver reliable outcomes. Commercial LLMs like OpenAI and Anthropic offer powerful reasoning, enterprise search tools make knowledge easier to find, and native AI features in dbt, Snowflake, or BI platforms bring helpful automation for specific tasks. Each has a place, but each also comes with limitations when it comes to governance, lineage, and metadata context.
That’s where context becomes the differentiator. Without visibility into schema evolution, ownership, or access controls, even the most advanced models risk producing outputs that are inconsistent or incomplete. Data teams need solutions that can embed AI directly into the systems they already rely on, respecting governance and adapting as the environment changes.
For organizations serious about scaling AI in data, platforms like Secoda AI provide the foundation that others lack. By grounding every response in metadata, lineage, and governance, Secoda transforms AI from experimental to dependable, helping data teams deliver accurate, secure, and scalable insights their business can trust.
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