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Comparison · Analytics

Displayr vs Hex

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

D
Displayr
ANALYTICS
5.0

Displayr keeps folding AI agents and Chat deeper into survey analysis

◆ Current state

Displayr is layering AI across its survey-analytics workflow: a Data Preparation Agent that flags low-quality respondents and auto-tidies categories, and a Chat assistant that edits documents and now shows exactly what it sends and what it changed. Recent releases are trust-and-polish work on that AI foundation plus steady analytical depth like period anchors and a refreshed workspace.

◆ Where it's heading

The direction is AI-assisted analysis a non-analyst can trust and use — transparent Chat edits, a view-mode chat panel for published documents, and agent-driven data prep. Underneath, the core stats engine keeps gaining precision controls for time-series and tracking studies.

◆ Prediction

Expect continued investment in making Chat auditable and in widening the Data Preparation Agent's automatic judgments; the likely next step is broader agent coverage of the cleaning and analysis pipeline.

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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