Most AI writing tools start with a blank prompt box.
That works for brainstorming. It works much less well when the input is real documents and the goal is a specific artifact like a meeting brief, renewal note, competitor snapshot, pricing memo, board update, or RFP response.
That gap is why we built Gixo Briefs.
Instead of starting from a prompt, you start with the material you already have: PDFs, DOCX files, spreadsheets, or internal notes. The system turns those into structured business briefs.
The part I’m most interested in feedback on is the planner that sits in front of generation.
Inside the product we have a catalog of 50+ brief recipes across several categories. But instead of asking the user to pick the right template up front, the planner reads the request and the uploaded sources first, then decides what kind of brief to create.
If the request clearly matches one of the curated recipes, it uses that pattern. If it doesn’t, the planner creates a custom brief plan automatically.
So a user can ask for things like:
• turn account notes into a renewal brief
• summarize competitor documents into a sales snapshot
• build a board update from a research pack
• produce a buyer-friendly pricing comparison
The system tries to answer a more useful question than “what text should I generate?”
It asks:
• what kind of brief is this
• who is it for
• how much detail should it contain
• whether citations are needed
• what sections should exist
• how long it should be
Internally, recipes are not just visual templates. They describe the purpose of the brief, expected evidence style, structure, and target length. If no recipe fits well, the planner generates a custom brief plan with a purpose, audience, tone, structure, and word range.
I like this approach because it feels closer to how a human analyst works. A good analyst doesn’t just read documents and produce text — they decide what artifact needs to exist first. The planner is our attempt to make that step explicit.
The second thing we cared about was grounding the brief in source material. The system is built around source-first writing: citations when needed, number checks, structure checks, and cleanup passes so the output reads like a deliverable rather than a chatbot answer pasted into a document.
The goal isn’t “AI that writes more text.”
The goal is “AI that helps people get to a usable business artifact faster.”
The product itself is a workspace where you can store source documents, reuse them, generate briefs from either curated recipes or planner-created plans, edit the result, and export or share the finished brief.
The planner is the piece I’d most like feedback on.
Many AI tools make the user do the classification step manually — choose the template, or figure out the structure in the prompt itself. Here we’re trying to see how far the system can go by doing that reasoning automatically.
If this sounds useful, I’d especially love feedback from consultants, founders, PMMs, chiefs of staff, analysts, and ops teams.
Questions I’m most curious about:
• Is automatic brief selection actually useful, or would you rather choose manually?
• What brief types are still missing?
• Would custom user-created brief templates be more valuable than a larger built-in catalog?
hardikparikh29•1h ago
That works for brainstorming. It works much less well when the input is real documents and the goal is a specific artifact like a meeting brief, renewal note, competitor snapshot, pricing memo, board update, or RFP response.
That gap is why we built Gixo Briefs.
Instead of starting from a prompt, you start with the material you already have: PDFs, DOCX files, spreadsheets, or internal notes. The system turns those into structured business briefs.
The part I’m most interested in feedback on is the planner that sits in front of generation.
Inside the product we have a catalog of 50+ brief recipes across several categories. But instead of asking the user to pick the right template up front, the planner reads the request and the uploaded sources first, then decides what kind of brief to create.
If the request clearly matches one of the curated recipes, it uses that pattern. If it doesn’t, the planner creates a custom brief plan automatically.
So a user can ask for things like:
• turn account notes into a renewal brief • summarize competitor documents into a sales snapshot • build a board update from a research pack • produce a buyer-friendly pricing comparison
The system tries to answer a more useful question than “what text should I generate?”
It asks:
• what kind of brief is this • who is it for • how much detail should it contain • whether citations are needed • what sections should exist • how long it should be
Internally, recipes are not just visual templates. They describe the purpose of the brief, expected evidence style, structure, and target length. If no recipe fits well, the planner generates a custom brief plan with a purpose, audience, tone, structure, and word range.
I like this approach because it feels closer to how a human analyst works. A good analyst doesn’t just read documents and produce text — they decide what artifact needs to exist first. The planner is our attempt to make that step explicit.
The second thing we cared about was grounding the brief in source material. The system is built around source-first writing: citations when needed, number checks, structure checks, and cleanup passes so the output reads like a deliverable rather than a chatbot answer pasted into a document.
The goal isn’t “AI that writes more text.” The goal is “AI that helps people get to a usable business artifact faster.”
The product itself is a workspace where you can store source documents, reuse them, generate briefs from either curated recipes or planner-created plans, edit the result, and export or share the finished brief.
The planner is the piece I’d most like feedback on.
Many AI tools make the user do the classification step manually — choose the template, or figure out the structure in the prompt itself. Here we’re trying to see how far the system can go by doing that reasoning automatically.
If this sounds useful, I’d especially love feedback from consultants, founders, PMMs, chiefs of staff, analysts, and ops teams.
Questions I’m most curious about:
• Is automatic brief selection actually useful, or would you rather choose manually? • What brief types are still missing? • Would custom user-created brief templates be more valuable than a larger built-in catalog?