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Comparison · Analytics

Dovetail vs Hex

Side-by-side trajectory, velocity, and editorial themes.

D
Dovetail
ANALYTICS
6.3

Dovetail is turning its research repository into an AI analyst that reads, computes, and cites.

◆ Current state

Dovetail has shifted its center of gravity from storing research to answering questions over it. The last month is almost entirely about the chat layer: persistent multi-turn context, code execution with inline charts, admin-curated Docs as context, and a new deep research mode. The MCP server is gaining write tools, making the repository operable by outside agents.

◆ Where it's heading

The arc points to an analytical agent that works across both qualitative and quantitative data and can be driven programmatically. Each release widens what chat can pull in and what it can do, from running code to sustaining reasoning across turns. Dovetail is positioning the chat surface, not the project, as the primary way users interact with their research.

◆ Prediction

Expect deep research mode to gain agentic follow-through that writes results back to Docs, and the MCP write surface to keep expanding toward full repository control from external tools.

H
Hex
ANALYTICS
5.0

Hex is remaking its notebook into an agent that both uses and plugs into MCP

◆ Current state

Hex is converting its analytics notebook into an AI agent platform. It now runs as an MCP client, is invocable from Codex, and ships generative data apps built from prompts, while keeping its model roster current with Kimi K2.7 and Fable 5 and giving admins default-model and branding controls. Integration and governance work — a Figma connector, AWS IAM-role support, signed embedding — rounds out the core.

◆ Where it's heading

The arc points at Hex as connective agent infrastructure: consuming external context and tools via MCP, distributing itself into other agent surfaces like Codex, and letting analysts assemble apps and dashboards from prompts. Expect the agent, rather than the notebook grid, to become the primary interface, with model choice and governance layered on top.

◆ Prediction

Likely next steps deepen the agent's tool-use over MCP connections and push generative apps further toward production embedding and governance controls.

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