Agentic AI Comparison:
Ask On Data vs Harvey
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Introduction
This report compares Ask On Data, a GenAI-based chat tool for data engineering and ETL processes, with Harvey, a specialized AI platform for legal tasks like research, drafting, and analysis. The evaluation covers autonomy, ease of use, flexibility, cost, and popularity based on available data as of late 2025.
Overview
Harvey
Harvey is an enterprise-grade legal AI assistant built on advanced models, tailored for law firms with features for document Q&A, chronology generation, legal research via integrations like LexisNexis, and drafting. It excels in benchmarks and serves large 'Big Law' firms.
Ask On Data
Ask On Data is a chat-based data engineering platform that enables users to perform ETL tasks, data transformations, and analysis using natural language queries. It targets data professionals seeking conversational interfaces for complex data workflows without extensive coding.
Metrics Comparison
autonomy
Ask On Data: 8
High autonomy in handling data engineering tasks via chat, allowing independent ETL and analysis without deep coding, though may require user guidance for complex pipelines.
Harvey: 9
Strong agentic capabilities for driving legal research strategies, drafting motions, and workflows autonomously, outperforming baselines in benchmarks like document Q&A (94.8%) and timelines (80.2%).
Harvey edges out due to proven performance in specialized legal autonomy tasks; Ask On Data is capable but less benchmarked.
ease of use
Ask On Data: 9
Chat-based interface designed for intuitive natural language interaction with data tools, likely self-serve and quick to start for data users.
Harvey: 5
Complex onboarding with long sales processes, demos, and implementation; tailored for Big Law but criticized as clunky for smaller teams.
Ask On Data appears far easier for quick adoption; Harvey's enterprise focus hinders accessibility.
flexibility
Ask On Data: 8
Flexible for various data engineering needs like ETL and analysis across datasets via conversational prompts.
Harvey: 6
Legal-specific with rigid workflows, integrations like LexisNexis, but criticized for lack of customization and being overkill for non-Big Law use.
Ask On Data offers broader data task flexibility; Harvey is less adaptable outside core legal domains.
cost
Ask On Data: 8
Likely affordable subscription model for data tools, with no mentions of high enterprise barriers; positioned as accessible GenAI solution.
Harvey: 4
Very expensive enterprise pricing with custom quotes, long sales cycles, and poor value cited for many teams; built for Big Law budgets.
Ask On Data is presumably more cost-effective; Harvey's high cost limits it to large firms.
popularity
Ask On Data: 5
Niche tool in data engineering space with limited visibility; no widespread mentions or benchmarks.
Harvey: 9
Highly popular in legal AI, topping benchmarks, adopted by major firms like DLA Piper, A&O Shearman, PwC; frequent discussions and AMAs.
Harvey dominates legal AI popularity; Ask On Data trails in broader recognition.
Conclusions
Harvey excels in autonomy and popularity for legal professionals in large firms but lags in ease of use, flexibility, and cost. Ask On Data offers a more accessible, user-friendly option for data engineering, better suiting smaller teams or non-legal data needs. Choice depends on domain: legal workflows favor Harvey, data tasks favor Ask On Data.