Chord vs Dovetail
Side-by-side trajectory, velocity, and editorial themes.
Chord rebuilds Copilot from the ground up, betting its CDP on conversational AI.
Chord, a commerce data and CDP platform, has put nearly all its recent product energy into Chord AI and its Copilot assistant. The changelog is a steady stream of Copilot refinements — feedback loops, memory, documentation grounding — culminating in Copilot Next, a ground-up rebuild now reaching early customers.
The arc is clear: Chord is turning its CDP into a conversational analytics surface where users ask questions and Copilot answers from their data. The progression from Enriched Context to feedback memory to a full rebuild with persistent, shareable chat shows AI moving from a feature to the core interface.
Expect Copilot Next to widen from its limited early-access group toward general availability, with continued work on answer transparency ('show their work') and conversation sharing.
Dovetail is turning its research repository into an AI analyst that reads, computes, and cites.
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.
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.
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.
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