Dataiku vs Decagon
Relationship
Overlaps on agent orchestration, data analysis and evaluation and observability; same layer and the same scale band (growth-stage private).
Assembled from the recorded fields for this pair, not hand-checked. The comparison below is read from each company’s own profile.
3 of 8 capabilities — Shares agent orchestration, data analysis and evaluation and observability.
Ludbee capability tags · from the product recordsShared product type — Both ship AI agent and agent platform.
Ludbee product recordsSame scale band — Both growth-stage private.
Ludbee scale bands · from valuation and funding figuresAligned comparison
Capability overlap
Shared · 3
Not verified for Decagon · 5
Recorded for Dataiku. Decagon’s product records say nothing either way — a missing record is not a missing capability.
Not verified for Dataiku · 4
Recorded for Decagon. Dataiku’s product records say nothing either way — a missing record is not a missing capability.
Products, side by side
Algorithmic pairing — assembled from recorded fields, not hand-checked
Dataiku
AI agent
AI building agent that turns a business objective written in plain language into a governed Dataiku project of data pipelines, models, agents and applications rendered as an editable visual workflow.
Agent platform
Centralised control plane inside the Dataiku platform for creating, orchestrating, deploying and tracking AI agents across teams.
Expert-to-Agent engine that converts subject-matter-expert know-how into governed AI agents grounded in enterprise data with structured reasoning and human oversight.
Decagon
AI agent
Builds and runs customer-support agents that resolve enquiries over chat, email and voice.
Decagon's chat channel — an AI agent that handles live customer conversations in a web or in-app chat surface.
Decagon's email channel — agents that read and answer customer email threads rather than routing them to a queue.
Decagon's voice channel — an AI agent that answers customer phone calls in place of a hold queue.
Agent platform
Decagon's authoring layer for the rules an AI support agent follows, so non-engineering teams can build, iterate on and scale agents.
An AI partner built into Decagon that helps teams build and improve support agents — distilling best practices from hundreds of Decagon deployments into guidance for the builder, and auto-tuning the agent via "Duet Autopilot," which improves it with every conversation it handles.
No counterpart
Dataiku sells these in a stack layer with no product recorded for Decagon yet — nothing on the other side to compare them against.
Platform
Platform for building, deploying and governing data, machine-learning and agent workflows across an organisation.
Infrastructure service
Cross-platform governance product that discovers every AI agent an enterprise is running -- on Microsoft Copilot Studio, Azure Foundry, Salesforce Agentforce, AWS Bedrock, Google Vertex, Databricks, Snowflake Cortex, n8n or Dataiku itself -- measures each agent's business and technical performance, and flags the ones that pose the greatest risk. Announced 2026-09-24; distinct from Dataiku Agent Hub, which creates and operationalizes agents rather than discovering and governing agents built anywhere.
Control layer over the Dataiku LLM Mesh that caps LLM spend, screens prompts and outputs for sensitive or malicious content, and scores model output quality.
Centralised gateway that routes, meters and governs an organisation's connections to multiple LLM providers from inside the Dataiku platform.
Decagon sells these in a stack layer with no product recorded for Dataiku yet — nothing on the other side to compare them against.
Application
A/B-testing suite for AI support agents: structured experiments (tone, logic, flows) against live traffic with control groups, statistical-significance testing and gradual rollout.
Dashboards and natural-language querying ('Ask AI') over support data: CSAT/deflection performance tracking, heatmaps, customer-journey visualization and knowledge-base performance.
Analyzes support conversations to detect gaps in a company's help center and auto-generates draft articles, ranked by impact, with monthly updates.
Integrated testing suite (internally called 'Simulations') that validates AI-agent behavior across channels before production deployment.
Always-on monitoring and QA for AI and human agent interactions against custom quality criteria.