Count vs AI Workspaces like Dust and Langdock [2026]
AI workspaces put agents in every employee's hands. Count is the AI workspace for research and analysis, where every answer can be checked and worked on together.
AI workspaces such as Dust and Langdock give a whole company access to AI models and custom agents, connected to company knowledge and run under admin controls. Count is an AI workspace too, built for research and analysis: a collaborative platform where every claim an agent makes has a reasoning chain under it.
Most AI workspaces get AI used across the company. Count makes its answers ones you can trust.
What should a team look for in an AI workspace?
Model access and agent builders have become standard. What separates workspaces now is what happens after the agent answers:
- Can you check the answer? Both Dust and Langdock cite the documents their agents retrieved. Neither marks which claims those documents actually support. Count labels every claim as cited, assumed, model knowledge or a potential hallucination.
- Can people work on it at the same time? In most AI workspaces you share a conversation. In Count, people and the agent work on the same canvas in real time, and comments sit on the exact claim in question.
- Do agents follow the team's methods? Count's agent skills and context are shared across the workspace, so every colleague's agent builds an answer the same way.
- What does the work become? Usually a chat reply. In Count, a live report, dashboard or deck that keeps its reasoning attached.
- Will people ration their questions? Dust spends credits per message and Langdock applies session and weekly limits. Count Pro includes unlimited AI credits on a fair-use basis.
Which AI workspace is best for research and analysis?
If the work ends in a decision, the workspace has to show why an answer is right, not just produce one. That is the problem Count was built around. A company-wide assistant answers a lot of questions quickly; a research workspace has to stand behind the few that matter. Count's reasoning chains are how it does that.
Count's advantages over a general AI workspace
- Hallucination detection on every answer, not citations alone.
- A real-time multiplayer canvas instead of shared chats.
- Live reports, scheduled reports and alerts built from the research.
- A semantic layer and native database connections for when the question involves your data.
- An MCP server that checks answers from Claude, ChatGPT and other assistants (docs).
Detailed comparisons: Count vs Dust and Count vs Langdock.
“It makes collaboration super easy. Multiple team members can work simultaneously on notebooks, dramatically accelerating our reporting cycles and improving analysis quality.” - Garoe Gonzalez, VP of Data and Analytics at TheVentureCity (review on G2)
AI workspaces and Count compared
| Capability | AI workspaces | Count |
|---|---|---|
| Hallucination detection | No - cite retrieved documents, but do not flag unsupported claims | Yes - every claim traced to its source, assumption or inference, and unsupported claims flagged |
| No usage caps | No - credits per message (Dust) or session and weekly limits (Langdock) | Yes - unlimited AI credits on Pro, on a fair-use basis |
| Live reports | No - Dust Frames are agent-built pages; Langdock results stay in chat | Yes - live reports, scheduled reports and alerts |
| Semantic layer | No | Yes - Count Metrics, with governance and change approvals on Enterprise |
| Agent skills | Yes - custom agents and reusable skills | Yes - agent skills and context shared across the workspace |
| Real-time collaboration | No - shared conversations and projects | Yes - multiplayer canvas, with free viewers |
| MCP connections | Yes - MCP client and server | Yes - connects to MCP servers, and has its own MCP server |
| Native database connections | No - Snowflake and BigQuery only through agent tools | Yes - Snowflake, BigQuery, Databricks, Postgres, Redshift and more |
| Python support | Yes - agents can run Python | Yes - Python and SQL on the same canvas |
Looking at chat assistants instead? See Count vs chatbots.
FAQs
A platform that gives a whole team access to AI models and custom agents connected to company knowledge, with admin controls. Dust, Langdock and Count are examples; Count is built specifically for research and analysis.
For research and analysis, yes: Count has shared agent skills, MCP connections and its own agent, plus reasoning chains and real-time collaboration.
Count matches each step to the right model, balancing cost and performance, using models from Anthropic, OpenAI and Google. Enterprise can choose its own models or bring its own API keys.
Count is SOC 2 compliant, follows GDPR and supports HIPAA. Enterprise adds SSO, SCIM, user groups, row-level security and EU/US data residency.