Interviews that stay useful
Paste every customer conversation into the Project. Six months later, ask "what did early users say about onboarding?" and get the quotes, not a summary from memory.
AI startup workspace
One workspace for the whole startup: idea, research, files, code, decisions, launch.
A startup in its first year is mostly context: what you learned from forty customer conversations, why you chose Postgres, what the design partner said in March, which pricing you tried and dropped. Most of it lives in scattered chats, docs and heads, and it is lost every time you open a new conversation with an AI. A MultipleChat Project is where that context lives instead, and every model you use works from it.
Free to start. For a startup, the latest models are the ones you want on the hard calls.
Why a startup needs this more than anyone
Decisions pile up faster than anyone can remember them
A founder makes hundreds of small decisions a month and revisits most of them. Without a shared memory, each AI conversation starts from zero and the founder becomes the only place the context lives.
The repo the models can see
Import selected files from GitHub where enabled. Architecture questions, reviews and bug hunts happen against the real code, and a second model checks the first. See coding with AI.
A decision log the AI reads
A decisions.md in the Project: what was chosen, why, what was rejected. New conversations stop proposing the thing you already ruled out in February.
The model and the runway
The financial spreadsheet, the pricing experiments and the results, so "what happens if we raise the price 20%?" is a question the AI can answer from your numbers.
Positioning, copy, channels
The positioning sentence, the landing page, the launch posts, all written from the same customer research. See launching.
Shared, when there is a team
Co-founders and first hires read the same Project. Shared Projects are part of the team plans. See team projects.
The hard calls
Where several models beat one, for a startup specifically
Is the wedge right?
Ask each model independently: is this first product narrow enough to win a customer in a month, and does it lead somewhere? When they disagree, you have found the strategic question to settle before building.
Why will a platform not absorb this?
Have one model argue that a general AI assistant makes your product unnecessary, and another argue the opposite. The stronger argument tells you what to build around.
Architecture you will not regret
Monolith or services, which database, which cloud, build or buy auth. Debate it with the repo in view, choose with the trade-offs visible, log the decision.
Pricing and packaging
Three models, three independent pricing proposals from the same customer research and cost model. The spread is your test range.
The board or investor update
Generate it from the Project: what changed, the numbers, the decisions, the asks. It is accurate because it comes from where the work happened.
Honest scope
What the workspace is and is not
It is where the thinking, the research, the writing, the code review and the decisions happen, with several AI models reading the same context. It is not your repository host, your CI, your CRM or your analytics; those stay where they are, and you bring the relevant pieces into the Project. Your product runs on your own infrastructure. Your data stays in your account and is never used to train any model.
Free is the start. The latest models are the difference.
A startup spends its first year on judgement calls. Use the best judgement available.
Wedge, platform risk, pricing, architecture, hiring: these are reasoning problems with no lookup answer. The latest models handle them with your whole Project in view; older models handle them with the last few messages and a general sense of what startups do. For decisions that set the next year, the paid plans pay for themselves quickly.
Newer models hold your whole Project in view instead of the last few messages, reason through trade-offs instead of picking the first plausible answer, and are wrong less often and with less confidence. For the questions on this page, that is the difference between advice that sounds right and advice you can act on.
Pro from $20 a month, cancel any time. The free plan stays free.
- Your first Project and file uploads
- The major AI families to try the workflow
- A small daily message allowance
- The latest ChatGPT, Claude, Gemini, Grok and Perplexity models, all on the same Project
- Far more messages a day, so a working session does not stop halfway
- Modes where several models draft, challenge and verify each other's answers
- More Projects and larger uploads, so research, repo and model all fit
- Team plans with shared Projects for co-founders and first hires
Start here
First messages for a startup Project
Add the pitch, the interview notes, competitor pages and, where enabled, the repo. Then start here.
WedgeFrom the research in this Project, propose three wedges narrow enough to win a first customer in a month. Which accumulates most over a year?
Platform riskArgue that a general AI assistant makes this product unnecessary. Then argue the opposite. Which is stronger, and what should we build around?
Decision logRead decisions.md. Which decisions look wrong given what we have learned since? Which should we revisit this quarter?
PricingPropose three pricing models from the customer research and cost model in this Project. Show revenue at 100 customers for each.
ArchitectureReview the imported code against our decisions. What will break at ten times the users, and what would you change first?
UpdateDraft this month's investor update from the Project: what changed, the numbers, the decisions, the asks.
Honest answers
What people ask
Is this a replacement for Notion, Linear or GitHub?
No. Those hold tasks and code. This is where the thinking happens with AI, against the same context. Bring the relevant documents and files in; keep the systems of record where they are.
Can my co-founder use the same Project?
Yes, on the team plans, which include shared Projects, shared chats and shared files. See team projects.
Is our code and research safe?
Content stays in your account, is encrypted, and is never used to train any model by MultipleChat or the AI providers. See security.
How does GitHub import work?
Where enabled, you connect GitHub, choose the repository and the files to include, and sync when they change. It is selected files as context, not a full analysis of every commit. See GitHub projects.
Why several models instead of the best one?
Because for judgement calls there is no best one. Independent answers show you where the decision is solid and where it rests on a guess.
What is free?
Your first Project, file uploads and the major model families. The latest models, more Projects, larger uploads and team features are on paid plans. See plans.
Give the startup one memory.
Create the Project, add the pitch and the research, and ask the first hard question. Free to start.
Also: AI startup ideas · create an MVP with AI · build a SaaS with AI · how Projects work