šNote: Researcher will be released to internal Access organisation on July 1st. Researcher provisioning to external customers is done via an agentic tier contract.
What is Evo Researcher?
Evo Researcher (previously known as Evo TeamWork) is an AI-powered research and collaboration tool built into the Evo platform.
Researcher is for work that involves reading, analysing, or producing documents ā and where the context matters as much as the question. You give it your files and instructions upfront, and the AI works within that context across every conversation in the project.
Is it worth a look?
It is if any of these apply:
You regularly work through large documents and need to extract, compare, or summarise information from them.
You produce the same type of output repeatedly (reports, specs, reviews) and want a repeatable, consistent starting point rather than starting from scratch each time.
You need more than one person involved in an AI-assisted task. Researcher projects are shared, so collaborators see the same context, uploads, and outputs.
You want the AI to behave consistently within a defined scope, rather than treating every conversation as a blank slate.
Key Features
Project-Based Workflow: Everything lives inside a project. Create a project, set instructions, upload files, and start chatting with AI in context.
AI Chat: Conversational AI interface powered by LLMs. The AI automatically has access to your project's uploads and instructions.
File Uploads: Upload documents to your project. Files are vectorised and chunked, so the AI can reference large documents without filling the context window.
In-Chat Attachments: Attach files (including images) directly in the chat for the AI to analyse in context.
Instructions: Set a single set of instructions per project to guide the AI's behaviour and responses.
Templates: Pre-configured project setups that bring across instructions, uploads, and settings. Admins can create templates so users can spin up the same kind of project quickly.
Collaboration: Add team members to your projects so multiple people can work together with the AI.
Artefacts: AI-generated content (documents, reports, specs) that can be previewed and downloaded.
Connectors: Integrate with platform data sources. Connector support is being aligned with the central Evo connector framework.
MFE Integration: Surface MFEs (Micro Frontend components) within Researcher. A new "teamwork" flag will allow MFEs to specifically target Researcher alongside existing quick action support.
How it fits into Evo
Researcher sits alongside the other Evo Platform Products. While Evo Builder is for building apps and Agents Builder is for creating AI agents, Researcher is for AI-powered research and collaboration. In the future, artefacts created in Researcher will be pushable into Evo Builder to turn research and specs into built products.
User and Admin Roles
Role | Capabilities |
User | Create projects, upload files, chat with AI, collaborate with team members |
Admin | All user capabilities plus creating and managing templates, managing org-level settings |
šNote: Users are added automatically when assigned a role in the platform.