Use and deployment
AI assistant vs. an AI platform for organizations
A comparison of AI tool categories — from a foundation model, through a general assistant and automation, to an AI platform for organizations.
Comparisons of AI categories should explain differences in starting point, not claim superiority over a specific brand. Market products change quickly and increasingly combine chat, company knowledge, connectors, agents and skills — so a more durable axis of comparison is what the AI works on, who is responsible for knowledge and how actions are controlled.
Categories of AI tools
| Category | Starting point | Best at | Typical limit |
|---|---|---|---|
| Foundation LLM | A model that generates text and reasons | Generation, analysis, classification | Doesn't automatically know the company's current knowledge or permissions |
| General AI assistant | A user's conversation with AI | Individual productivity and broad tasks | Context and way of working often depend on the user |
| Company AI workspace | A secure team space | Administration, connectors, sharing and productivity | May not reflect a specific company's procedures and rules |
| Chatbot or RAG | Answering from documents | Quick Q&A and search | Usually no agents, knowledge review or actions in systems |
| No-code automation | Data flowing between apps | Repeatable integrations and rules | Company knowledge and the reasoning behind results aren't the core; flows must be configured |
| Slimaco AI | Teams' work on shared company knowledge | Agents and an assistant working on approved knowledge, in the company's apps, under control | Requires connecting sources and apps and naming knowledge owners |
Comparison matrix
Any vendor may be an exception to the description below — this is the typical operating model of each category, not a statement about a specific product.
| Dimension | General assistant | RAG chatbot | Automation | Slimaco |
|---|---|---|---|---|
| Core purpose | Supporting an individual user | Answers from documents | Integrating and automating steps | Doing teams' work on shared knowledge |
| Automations | Depends on product and plan | Outside typical scope | Core; flows configured by hand | Agents created from a task description, on demand and on a schedule |
| Knowledge sources | User's files, chat and connectors | A selected corpus | App data within a workflow | Managed sources, versions, review and knowledge mining |
| Citations | Depends on the mode | Usually yes | Not core | Core to knowledge-based answers |
| Content permissions | At workspace and connector level | Depends on deployment | At app and connection level | Roles plus collection, source and document rules |
| Write actions | Depends on the tool | Rarely | Typical scope | Explicit tools and human approval |
| AI model | Usually the vendor's ecosystem | One or a few models | Platform-dependent | Multiple models, matched to the task |
| Getting started | Self-service | Indexing project | Integration project | Self-service SaaS or with implementation services |
| Biggest value | Quick productivity | Access to documents | Less manual work | Repetitive work done in the company's context, under control |
When Slimaco makes particular sense
- the team has repetitive tasks — quotes, reports, reminders, ticket handling — that should follow the company's rules;
- operational knowledge is scattered across people, documents and mailboxes;
- different teams should get consistent answers from the same approved sources;
- access to knowledge depends on role, team, client or document type;
- AI actions must be approved, limited and traceable;
- the organization wants to use different models without rebuilding the whole system.
When a simpler solution may be enough
- the goal is only individual writing, analysis and brainstorming;
- the team doesn't need shared knowledge or automations based on company rules;
- the problem is one simple flow between two apps;
- there are no requirements for content-level access, action approval or audit;
- the cost of deployment would exceed the value of a small, occasional process.
Qualifying questions
- Which repetitive tasks take the team the most time today?
- Must the result come from an approved document and show its source?
- Should two people in the same role work to the same standard?
- Should different roles see different documents?
- Who approves knowledge and actions with business impact?
- How do we measure time, errors, throughput, onboarding or quality after deployment?
Slimaco AI vs. ChatGPT and Microsoft 365 Copilot in the company
ChatGPT Enterprise and Microsoft 365 Copilot are good tools: they have connectors, citations and permission settings, and their capabilities are growing fast. You don't have to give them up. Those tools are designed primarily as an assistant for an employee. Slimaco is the organization's shared brain: it collects approved knowledge from every department's systems, and agents work on it by company rules. Soon ChatGPT, Claude, Gemini and Copilot will connect to Slimaco via MCP and use the same knowledge, with the permissions of the person asking.
The comparison describes a typical, standard deployment (as of October 2026). Specific features depend on each vendor's plan and configuration.
| Area | Typical ChatGPT / Microsoft 365 Copilot deployment | Slimaco AI |
|---|---|---|
| Department knowledge | Documents and email available in the tool, plus what the user connects or pastes | CRM, ERP, helpdesk, invoicing, email and messaging in one base, within the user's permissions |
| Company knowledge | Access to files and connectors; keeping content current is up to the organization | Approved knowledge with versions and owners; knowledge mining picks up new agreements |
| Answer sources | Citations depend on mode and source | Citations to a specific fragment of an approved document |
| Access control | Permissions inherited from connectors and the workspace | Roles plus collection, source and document rules, shared by people and assistants |
| Automations | Tasks and agents depend on product and plan, often configured separately | Agents created from a task description, on demand and on a schedule, on approved company knowledge |
| Rule changes | Documents and instructions must be updated manually | Once approved, the new version flows into answers and agents' work right away |
| Models | Mainly the tool vendor's models | GPT, Claude, Mistral, DeepSeek and Kimi; the model vendor can be changed without rebuilding |
| Deployment model | Vendor's cloud | SaaS with knowledge stored in the EU, or dedicated infrastructure |
| Working together | — | Coming soon: ChatGPT, Claude, Gemini and Copilot use Slimaco knowledge via MCP |
When ChatGPT alone is enough. For ad-hoc help — writing an email, summarizing a text, one person's brainstorm — regular access is usually enough. The need changes as a company grows: knowledge about how things are done scatters across separate chats, repetitive tasks are still done by hand, and losing one key person means losing procedures that were never written down.
In short: ChatGPT and Microsoft 365 Copilot help employees with individual tasks. Slimaco is a shared system of work for the organization: with memory, sources, access control and agents that handle repetitive work by company rules. You don't have to choose: the team can use the Slimaco assistant or, soon, stay with ChatGPT, Claude, Gemini or Copilot connected to Slimaco via MCP.
See also: what Slimaco AI is, agents and automations and the use-case catalog.