Give every AI a whole workspace to think from.
Upload many files, connect Google Drive or GitHub, add project instructions, and let ChatGPT, Claude, Gemini, Grok and AI Collaboration work from the same indexed knowledge base. Projects are how real work moves beyond one-off chat attachments.
Project workspace
Files, instructions and AI models share one context layer.
Sources
OCR extracts tables, clauses and layout hints.
Code is indexed as searchable reference material.
Imported files can be synced and re-indexed.
Images receive searchable descriptions.
Retrieval engine
Sent to any model
Only the relevant project chunks are attached to the answer.
The model does not need the whole folder dumped into one prompt. It receives the right source material at the right moment.
A chat is temporary. A project becomes institutional memory.
Real AI work has source files, messy PDFs, screenshots, code, client context, decisions, versions and standing instructions. Projects keep that material together so every new conversation starts with the same working memory.
Many files
Build a project library
Upload documents, spreadsheets, images, notes and code into one workspace instead of re-attaching files to every chat. Plan and safety limits apply.
OCR inside
Scanned PDFs become usable
MultipleChat extracts text from PDFs with OCR first, then falls back to additional extraction methods when needed.
Indexed chunks
The AI searches before answering
Files are split into overlapping chunks and searched at question time, so answers can use the most relevant passages without stuffing the whole folder into context.
One context
Any model can use it
Switch from Claude to ChatGPT, compare models, or run AI Collaboration without rebuilding the same context again.
How a file becomes AI context.
This is the important part: Projects do not just store files. They process them, index them and retrieve the parts that matter for your question.
Step 1
Upload or import
Add files directly, import from Google Drive, or bring in GitHub repository files. The file is stored with project metadata and queued for indexing.
Step 2
Extract and OCR
PDFs, Office files, CSVs, code and text are converted to searchable text. Images are described so they can be found later.
Step 3
Chunk and index
Large files are split into overlapping chunks. MultipleChat stores the chunks separately, scoped to the right user or team project.
Step 4
Retrieve for answers
When you ask, the project searches relevant chunks and attaches the best evidence to the prompt with your project instructions.
Backend detail
Chunk retrieval means the AI can work with big project libraries.
The project index uses overlapping text chunks, searches the project at question time, and returns the strongest matches. That is the difference between “I uploaded a file once” and “the workspace can keep finding the right source material later.”
Approx. 1,000 character chunks
Long documents are split into manageable searchable blocks.
Overlap between chunks
Context is preserved across chunk boundaries.
Top matches retrieved
The model sees the relevant passages, not random file noise.
Bring the messy material real work depends on.
Projects are built for PDFs, images, codebases, spreadsheets, presentations, structured data and plain-text notes. Dangerous executable and archive formats are blocked for safety.
PDFs and scanned documents
PDF OCR extracts text from scanned and text-heavy documents, including tables and structured content when available.
Docs, sheets and slides
DOCX, XLSX, CSV, TSV, PPTX and related formats can become searchable project knowledge.
Code and config files
Python, JavaScript, TypeScript, React, Vue, Java, Go, Rust, C/C++, SQL, YAML, JSON and many other text/code formats are supported.
Images and screenshots
PNG, JPG, GIF and WebP files can be described for search, so visual references do not disappear from the project memory.
Connect the places your knowledge already lives.
Projects should not force you to manually rebuild your workspace. Import from Drive, pull files from GitHub, and keep your AI context close to the source.
Google Drive
Import and sync Drive files
Bring Google Docs, Sheets and Drive files into a project. Google-native files can be exported into text-friendly formats, and Drive PDF OCR can help turn difficult PDFs into usable text.
GitHub
Import code from repositories
Connect GitHub, choose repository files, import them into the project, and let MultipleChat index the code. Great for explaining architecture, reviewing modules and keeping project chats grounded in the real codebase.
Project instructions keep every model aligned.
Add standing instructions once: tone, audience, constraints, preferred format, terminology, brand rules, coding standards, citation expectations, or client context. Every project chat can inherit those rules.
Example project instructions
You are working inside the Acme Q3 launch project.
Use the uploaded product brief, pricing sheets, brand voice guide and customer research before answering.
Prefer concise executive language. Flag uncertainty. Cite source filenames when a claim depends on project material.
Most people do not know what to put in a project. Show them.
Projects become powerful when the workspace matches the job. A student needs course material. A marketer needs brand and campaign context. A lawyer needs a matter file. These playbooks explain what to upload, what instructions to set, and what prompts to run first.
Rule of thumb
If a human expert would need the files before giving a serious answer, put those files in a project first.
Students
Course and exam projects
Upload notes, slides, readings, rubrics and past papers. Turn them into study guides, quizzes and essay feedback.
Student playbook →
Marketing teams
Brand and campaign projects
Upload brand guides, product docs, campaign briefs, competitor research and performance notes.
Marketing playbook →
Legal teams
Matter-file projects
Upload contracts, exhibits, scanned PDFs, chronology notes and research for source-grounded legal review.
Legal playbook →
Coders
Codebase projects
Connect GitHub files, README docs, schemas, stack traces and screenshots so AI understands the repo.
Coding playbook →
Consultants
Client engagement projects
Upload briefs, research, notes, spreadsheets and old decks, then synthesize client-ready recommendations.
Consulting playbook →
Researchers
Literature and source projects
Upload papers, notes, datasets and drafts to build literature matrices and source-grounded synthesis.
Research playbook →
Product teams
Feature and roadmap projects
Upload user feedback, PRDs, specs, screenshots and analytics notes to draft better product decisions.
Product playbook →
Sales teams
Account projects
Upload call notes, account research, product docs and proposals for specific follow-ups and objection handling.
Sales playbook →
Content teams
Editorial and source projects
Upload editorial standards, source PDFs, interviews, drafts and brand examples to create source-grounded content workflows.
Content playbook →
Built for serious files, not random uploads.
The backend treats uploaded material as untrusted reference data. Files are validated, dangerous extensions are blocked, indexing is scoped to the user or team, and retrieved chunks are wrapped so the model understands that file contents are sources, not commands.
File validation
Project uploads check file type and content signatures before storing and indexing.
Azure storage
Project files are stored with metadata and lifecycle status before being indexed.
Prompt-injection guardrails
Retrieved file content is marked as untrusted reference material, so uploaded files should not silently instruct the AI.
Team-aware scope
Personal and team project chunks are scoped separately so search stays inside the right workspace.
Make one project for each real objective.
Projects work best when the source material belongs together: one client, one product, one research topic, one codebase, one campaign, one legal matter. That keeps retrieval sharp and reduces irrelevant context.
01
Upload sources first
Give the project its documents, code, images and spreadsheets before asking for final outputs.
02
Write instructions once
Define audience, tone, constraints and source-use expectations at project level.
03
Use the right AI mode
Chat with one model, compare models side by side, or let AI Collaboration synthesize a stronger answer.
Projects in MultipleChat
Stop asking AI to remember what it cannot see.
Put the material inside a project, let MultipleChat OCR and index it, then work with any model from the same source of truth.